Mobile App Analytics

mobile app analytics

nynne silding + sidsel baggesen

1

Executive summary Massive quantities of data produced by and about people, things and their interactions is changing the way businesses operate .The smartphone and its accompanying apps is a new source of digital footprints, which due to its unique characteristics is gaining increased attention from managers and decision-makers. Hence the analysis of mobile app data is a field of growing interest, although it remains to be thoroughly studied academically. The purpose of this thesis is to examine the emerging field of mobile app analytics and thus fill a void in the academic literature by identifying value

propositions for mobile app data and explore the maturity level of mobile app

analytics. We explore the field of mobile app analytics from three perspectives – the mobile app as a data source, the analytics tools needed to turn the data into insights, and the organizational aspect of using actionable insights to inform decision-making. Accordingly this thesis’ main analysis is divided into three chapters corresponding to three research questions that collectively will allow us to explore the field of mobile app analytics:

1. What characterizes mobile app data and what are its value propositions?

2. What is the current stage of mobile app analytics?

3. How are mobile app analytics used to support organizational decision-

making?

We undertake a combination of methodological inquiries, centered on a multiple case study. Our case study involves the retail chain Føtex, the financial enterprise Nykredit and the wholesaler and distributer AO. All three companies have developed an app to support their existing business areas, and recently initiated tracking by implementing analytics tools. The research design involves two rounds of qualitative interviews with case representatives and a series of systematic feature inspections of the apps and tools currently employed by our case companies. The empirical analysis is supplemented by a comprehensive literature and industry review and theoretical discussion, enabling us to build a body of knowledge for this emerging field that previously have not been covered in academic literature. Our analysis shows that the mobile platform is characterized by being personal, interactive, ubiquitous, location aware, and multimodal. With reference to

2

particular app specific characteristics, this leads us to conclude that the mobile app as a data source is distinguished from other digital data sources by its ability to add a contextual layer to the data, enabled by sensor technology. We find that the ability to add a time/place dimension to other data types is a unique value proposition. The data comes in various types and structures while the digital nature of the app means that interactions can be tracked instantly as they occur. The app as a data source thereby allow for holistic analysis and timely insights. The app data is furthermore personal and behavioral, enabling personalization and message tailoring. Our empirical analysis identifies gaps between the value propositions of mobile app data, and the data that the case tools currently provide. This is particularly apparent when it comes to sensor input such as location awareness and the fact that the tools are only able to report data on an aggregated level. Additionally, the tools lack transparency, why trust and validity issues arise. On the other hand, the tools provide detailed records of usage, interaction and navigational patterns. In our case companies, we find that there is a general lack of strategic targets for mobile app analytics, otherwise proscribed by the theory. Based on app analytics, they currently take decisions regarding app optimization, and report top-line metrics to management to ensure resources for future projects. Hence, we find that decision-making on the basis of mobile app analytics currently revolves around the app itself or the department on which it is placed. Due to lack of system integration, data complexity and validity, our case companies are currently not able to base strategic or automated decisions on the data that they can derive from their apps. We thus conclude that although mobile app data presents a series of promising value propositions, the current stage of mobile app analytics tools leaves room for further development and investment. A series of initiatives will have to be undertaken before the app data potentials can unfold: The analytics tools must improve their ability to utilize the app specific value potentials, allow for system integration and enhance the granularity levels of the data. If the overall maturity level is leveraged on both the organizational level and in the tool capabilities, mobile app analytics will be a valuable data source suitable for gaining insights into user behavior.

3

Index

1 I N T R O D U C T I O N . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2 Purpose and Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2.1 Clarification of concepts ................................................................................................. 8

1.3 Demarcation and Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1 .4 Structure of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2 M E T H O D O L O G Y . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1

2.1 Research Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.1.1 Research process model ................................................................................................. 12

2.2 Research Paradigms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.1 Epistemology and theoretical perspective ................................................................ 15

2.2.2 Reasoning and theory construction ........................................................................... 16

2.3 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.3.1 Research model ............................................................................................................... 18

2.4 Research Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.4.1 Case studies .................................................................................................................... 20

2.4.2 Semi-structured interviews ........................................................................................ 22

2.4.3 Feature inspections ...................................................................................................... 24

2.4.4 Literature review ........................................................................................................... 25

2.4.5 Online sources ............................................................................................................... 26

2.5 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.5.1 Data coding ...................................................................................................................... 27

2.6 Research Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.7 Methodological Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3 C A S E D E S C R I P T I O N . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 0

3.1 Føtex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.1.1 Indkøbshjælp .................................................................................................................... 32

3.2 Nykredit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.2.1 Mobilbank ........................................................................................................................ 33

3.3 AO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.3.1 AO.dk mobil ..................................................................................................................... 34

3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4

4 M A C R O E N V I R O N M E N T A N A L Y S I S . . . . . . . . . . . . . . . . . . . . . . . . . . 3 7

4.1 The Smartphone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.2 Mobile Apps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.3 The App Analytics Ecosystem .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.3.1 The app users ................................................................................................................. 40

4.3.2 The app owners .............................................................................................................. 41

4.3.3 Tool providers ................................................................................................................ 42

4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

5 T H E V A L U E O F M O B I L E D A T A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 4

5.1 Characterizing Mobile Footprints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.1.1 The mobile platform ...................................................................................................... 46

5.1.2 Advanced sensor technology ....................................................................................... 48

5.1.3 Sensor networks and the internet of things ............................................................. 49

5.2 Mobile Data and Research Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.2.1 Reality mining and urban planning ........................................................................... 51

5.2.2 Mobile phone sensing ................................................................................................... 53

5.2.3 Summary ........................................................................................................................ 54

5.3 Data Value Propositions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.3.1 Personal data ................................................................................................................... 55

5.3.2 Big data ........................................................................................................................... 56

5.3.3 Data through a critical lens ......................................................................................... 60

5.3.4 Summary ......................................................................................................................... 61

5.4 Discussing Mobile App Data Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.4.1 Mobile app data and volume, variety and velocity ................................................ 62

5.4.2 Particulars of mobile app data ................................................................................... 63

6 C U R R E N T S T A G E O F A P P A N A L Y T I C S . . . . . . . . . . . . . . . . . . . . . . 6 7

6.1 Tool Industry Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 6.1.1 Analytics tool market .................................................................................................... 68

6.1.2 Mobile app analytics strategy ..................................................................................... 72

6.1.3 Strategy and tool selection ........................................................................................... 72

6.1.4 App analytics value propositions .............................................................................. 74

6.2 App Development and Tool Implementation . . . . . . . . . . . . . . . . . . . . . . . 75 6.2.1 Føtex ................................................................................................................................. 75

6.2.2 Nykredit ........................................................................................................................... 77

6.2.3 AO .................................................................................................................................... 78

6.2.4 Summary ........................................................................................................................ 79

5

6.3 App Input and Tool Output Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 6.3.1 App input types ............................................................................................................. 80

6.3.2 App analytics tools ....................................................................................................... 83

6.3.3 Summary ........................................................................................................................ 89

6.4 Discussing Data Value and Tool Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 6.4.1 App analytics tool maturity ........................................................................................ 93

6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

7 O R G A N I Z A T I O N A L D E C I S I O N - M A K I N G . . . . . . . . . . . . . . . . . . . . 9 5

7.1 Analytics and Decision-Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 7.1.1 Analytics defined ............................................................................................................ 96

7.1.2 Data driven decision-making ...................................................................................... 98

7.1.3 Analytics maturity level .............................................................................................. 100

7.1.4 Real-time business intelligence ................................................................................. 101

7.2 Mobile App Analytics at Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 7.2.1 The analytical culture ................................................................................................. 104

7.2.2 Resources and skills .................................................................................................... 106

7.2.3 App data reporting ..................................................................................................... 108

7.2.4 Data integration ........................................................................................................... 111

7.2.5 Decision levels and latency ......................................................................................... 112

7.2.6 App analytics maturity level ...................................................................................... 114

7.3 Discussing App Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 7.3.1 Analytics resources ........................................................................................................ 117

7.3.2 Tool and system integration ...................................................................................... 118

7.3.3 Decision levels ............................................................................................................... 119

8 C O N C L U S I O N . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 2

8.1 Characteristics and Value Propositions . . . . . . . . . . . . . . . . . . . . . . . . . . 123 8.2 Current Stage of Mobile App Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 8.3 Mobile App Analytics and Decision-Making . . . . . . . . . . . . . . . . . . . . 126 8.4 The Maturity Level of Mobile App Analytics . . . . . . . . . . . . . . . . . . . . . 127

9 L I M I T A T I O N S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 9 1 0 P E R S P E C T I V E A N D F U T U R E W O R K . . . . . . . . . . . . . . . . . . . . . . . . . 1 3 1 R E F E R E N C E S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 3 4

6

1 INTRODUCTION

As we continue the journey from analog to digital, we are entering a new era - an era where data is everywhere, across sectors, economies and organizations. Massive quantities of digital data are being produced by and about people, things, and their interactions with websites, social media, mobile devices and a growing number of sensors embedded into our surroundings. This steady stream of digital footprints is causing a new data hype. Since there are documented performance gaps between companies that effectively make use of data and those who do not, data value propositions have gained attention from many researchers and business executives. Data becomes valuable when meaningful patterns are extracted from it; hence activities concerned with obtaining insights from data are gaining impetus as the data awareness increases. Analytics is one of the activities, which recently has become synonymous with the measurement of digital data sources such as websites, social media and most recently mobile phones. Mobile services are ubiquitously available, and in Europe the mobile penetration has long surpassed 100 percent. We tend to carry the mobile with us everywhere we go, and as the phones get ‘smarter’ they fulfill a growing number of our daily information and communication needs. Hence increasing volumes and varieties of data will come from our interactions with mobile devices. The realization that

7

the mobile platform is becoming an increasingly important touch point between companies and their customers, means that an increasing number of organizations move services unto the mobile platform. For many companies this entails the development of an app that can be used as a new communication touch point, branding tool or e-commerce channel. A part of the recent data hype is associated with the mobile footprints that are created when users interact with smartphones and apps. This has resulted in an emphasized focus on how companies can measure their mobile activities and utilize this potentially rich data source to reach their business goals. A recent addition to the analytics field is thus measurement of data from mobile apps. Mobile app analytics is therefore a field of increasing awareness in the blogosphere, and though the field is still young, a wide range of tools is being developed with the purpose of helping companies turn their mobile app data into actionable insights. The novelty of the field, however, means that the topic of mobile app analytics has yet to be covered scholastically why we believe there is a need for concrete knowledge on the matter. The aim of our thesis will thus be to fill this academic void by providing an exploratory study of the emerging field of mobile app analytics.

1.2 Purpose and Objective

This thesis presents an exploration of the field of mobile app analytics by enlightening relevant themes and exploring its current stage of development, as well as future potentials. Hence the purpose of this thesis is to identify value

propositions for mobile app data and explore the maturity level of mobile app

analytics. We find that the novelty of the notion calls for an exploratory and descriptive research design, which is appropriate when uncovering potentials of an uncharted and complex field. In order to fulfill our research purpose we find it vital to focus on its three constituent elements – the mobile app as a data source, the analytics tools needed to turn the data into insights, and finally the organizational aspect of using actionable insights to inform decision-making. For this reason we have developed the following three research questions: 1. What characterizes mobile app data and what are its value propositions?

8


3. How are mobile app analytics used to support organizational decision-making? These questions will constitute the three main chapters of our thesis, and will each contain a critical analysis and a thorough discussion before concluding on our findings and answering the different perspectives of our research purpose.

1.2.1 Clarification of concepts

In this section we will account for the concepts that constitute our research questions. We will further provide a clarification of the deliberations and delineations inherent in these notions. We use the word value propositions to describe a promise or potential of value. We intent to evaluate value potentials by reviewing and analyzing what benefit mobile app data can bring to organizations and in some cases to research. Since little academic writing has been devoted to the app as a data source, we analyze and discuss related data areas, and hence borrow value potentials from the more established fields, to create new value propositions for the emerging field of mobile app analytics. With the current stage of mobile app analytics, we refer to the developmental stage of the analytics market, and the analytics practices that are carried out in companies. This entails an analysis of the current tool capabilities and the analytics processes that our cases currently undertake. When asking the question of how app analytics is used to support decision-making, we intent to analyze how our case companies utilize the data that their current analytics tools provide to inform decisions. This entails a discussion of which types of decisions mobile app analytics is suited for, and how the analytics tools are currently supporting their decision processes.

1.3 Demarcation and Scope

The following will account for the deliberate choices we have made regarding the scope of our research. Since our aim is to explore and define an emerging research field with no predefined boundaries or scope, our research purpose encompasses a broad perspective on mobile app analytics, its potentials and

9

challenges. Our thesis employs a case study approach in order to examine and illustrate the current stage of mobile app analytics in a real-life organizational context. Even though an important constituent of this case study approach has been to access the case companies’ mobile app data in order to uncover its potentials, we do not undertake specific data processing. Since the field of mobile app analytics concerns topics such as personal data collection and analysis, a relevant component is the ethical and privacy precautions that should be considered under such circumstances. While we realize that privacy is an important concern, we will not engage further in this discussion. This is a deliberate choice since our focus is on the value propositions of mobile app analytics and not its constituent ethical dilemmas. Within the field of mobile app analytics many different types of apps create a multitude of deviating value propositions. Accordingly we have chosen to narrow the scope to include applications where the main value creating activities lie beyond the apps. This means that we exclude mobile games, mobile social networks or other mobile services where the organizations’ business model revolves around their activities on the mobile platform. Hence, this thesis focuses on the type of companies and apps, where the app is created as a support function to the existing business processes and hence adds value to these. Finally this thesis will not account for technical specifications of mobile app analytics, even though the term does entail many technical aspects. The technical analysis undertaken in this thesis is limited to a data perspective on our case companies’ apps and their affiliated analytics tools. We find it relevant to emphasize that this exploratory part of our thesis is to be seen as a snapshot of the current stage of development, as we realize that new technology and media are fast moving objects, where trends and tendencies quickly develop and dissolve.

1.4 Structure of Thesis

This section will provide an overview of the structure of the present thesis. Following the introduction, chapter two will present our research design,

10

philosophy and methods. Chapter three will provide a presentation of our three case companies and their respective mobile apps. Chapter four consists of a macro environment analysis with the aim of identifying the essential elements of the ecosystem surrounding mobile app analytics. In chapter five, six and seven we will present our theoretical framework and the analysis that enables us to answer our three research questions as described above. In chapter five the characteristics of mobile app data and its value propositions will be examined by a thorough literature review, focusing on mobile specifications and data value. Chapter six is an empirical outline of the current capabilities of the mobile app analytics tools and their ability to capture the value of mobile app data. In chapter seven we construct a theoretical framework around organizational data usage and examine how mobile app analytics is employed in our three case companies. Each chapter in the analysis will close with a discussion of relevant findings and a preliminary conclusion. These three main chapters will set the scene for chapter eight, where we compile the various findings and arguments leading to our conclusions of the characteristics of mobile app data and the maturity level of mobile app analytics. Chapter nine and ten will outline the limitations of this thesis and its further perspectives and possibilities for future examination.

11

2 METHODOLOGY

The following chapter will outline our research strategy and the plan by which we intend to carry out this strategy. In line with the definition provided by Blumberg et al. (2005) our research design includes the overall approach to research, the methods used for data collection and analysis, as well as the theoretical perspective and epistemological considerations underlying our study. How a research project is designed is of great importance for the establishment of new knowledge. The purpose of the present chapter is thus to account for our deliberations regarding the theoretical, empirical and analytical approach of this thesis. The chapter will open with an account for our overall research approach, before the research philosophy is outlined in terms of our epistemological stance and theoretical perspective. Subsequently, we will argue for our choice of methods and describe in detail how each is carried out. The chapter will conclude with a section on the methodological limitations that our research design presents.

2.1 Research Approach

The novelty of the topic we address in this thesis calls for exploration, which Blumberg et al. (2005) points to as the suitable research approach when little information about a particular topic is known and relevant variables must be identified in order to advance the study. The purpose is to learn from our experiences of the current investigation and refrain from being biased by any

12

preconceived notions. On an operational level this means that we, with the exploratory approach, are concerned with hypothesis generation rather than hypothesis testing (ibid.). Consequently this stage of our research process allows for an open-minded, flexible and unstructured approach. In most research projects exploration comprises the first stage of a study, before a more exhaustive approach, such as description, is undertaken. Description is relevant when the purpose is to describe and encapsulate variables or phenomena without the intent to unveil causal links between them (ibid.). This approach is preferred when the researcher attempts to answer ‘what’-questions and when the amount of available information is relatively low. Compared to the exploratory approach descriptive studies are characterized by a higher level of structure and formality, and often take their point of origin in research questions or hypotheses. The research approach undertaken in this thesis is an interchangeable process of both exploration and description. The early stages of our project are highly characterized by exploration when the objective is to discover and establish an overview of our problem area and to identify relevant literature, sources and themes. Furthermore, the subsequent phases of our research process have an exploratory starting point, especially when conducting the literature review aiming at uncovering the current field of mobile app analytics. The latter part of this project has been approached descriptively since we aim to describe the significance and interconnections of the variables we have identified in the exploratory phases.

2.1.1 Research process model

The model below provides an illustration of our research design and the general research approach that underpins this study. As mentioned above, our research process has had an exploratory starting point where preliminary unstructured interviews were conducted and relevant literature and themes were identified. As the amount of information increased, our broad problem area narrowed resulting in the formulation of our research purpose and the three research questions. The subsequent process involving the developing of our research design, collecting empirical data, and coding and analysis also had an exploratory starting point, that particularly reflected our approach to the literature review

13

and tool and app inspections. The qualitative interviews and the coding and analysis process were in greater extend characterized by a descriptive approach. A more detailed account of each method will be provided in a subsequent section.

Figure 2: Research Process Model, inspired by Blumberg et al., (2005)

14

2.2 Research Paradigms

In line with Crotty (1998), we find that any research process entails four basic elements: Epistemology, Theoretical perspective, Methodology and Methods. These four elements present the interrelated levels of decisions that go into the process of planning and designing research (Crotty, 1998).

Figure 3: The research process, Crotty, 1998.

Central to any research process is the methodology and methods used to conduct a study. We understand methodology as the underlying strategy used to arrive at a desired outcome, while methods are the tools and techniques applied to realize it (ibid.). According to Crotty (1998) the justification behind choosing a particular methodology and an accompanying set of methods lies in the purpose, or the research question, of a study. How these methods are applied, and how the results are viewed, are determined by the theoretical perspective - the philosophical stance we bring to our methodology. Inherent in the theoretical perspective is the epistemological preposition. We define epistemology as the theory of knowledge, which involves the meaning ascribed to knowledge and its creation (ibid.). We introduce Crotty’s four interrelated paradigms here with the aim of describing our research process in terms of these elements in the coming sections. Outlining the research process in terms of these four basic paradigms will account for our rationales and choices regarding the structure of our research design. Quite a substantial number of models have been developed with the attempt to describe the research- process and design. We have chosen to draw on Crotty mainly because we find his problem-oriented approach a natural and logic way to go about the research process. According to Crotty (1998), a research process should always take its origin in the problem or question it is trying to solve, even

!"#$%&'!

!"#$%&%'%()!

!"#$%#&'()*+,#%-,#(&'.#!

!"#$%&'()(*+!

15

though the influences of method, methodology, theoretical perspective and epistemology can work in several directions, and from different starting points. To avoid confusion it is worth noting that when Crotty refers to methodology, the concept is similar to what other scholars call research design (Blumberg et al., 2005; Babbie, 2007). For the sake of clarification, we use the term research design to refer to the overall plan or strategy from which we carry out our research.

2.2.1 Epistemology and theoretical perspective

In the following we will account for the epistemology underpinning the present study. Epistemological considerations is the theory of science concerned with the nature of knowledge, its creation, and the definition of what can be conceived as ‘true knowledge’ (Gilje & Grimen, 2002). When we consider the epistemological stance, we are not concerned with the empirical collection of data about the real world but rather with philosophical concerns regarding the procedures and approaches used when generating knowledge about reality. Epistemology is thus not science, but reflection on scientific activities and knowledge creation (ibid.). Historically epistemology has been divided into two main groups, objectivism and constructivism, and a range of variations of the two. In the following section we will discuss the opposing perspectives seen in the two epistemological stances. “Objectivist epistemology holds that meaning, and therefore meaningful reality, exists as such apart from the operation of any consciousness” (Crotty, 1998, p. 8). As this quote illustrates, we see that objectivists believe that objective truth exists, and that it is therefore possible for a researcher to encapsulate true knowledge about the world using scientific methods (Crotty, 1998). This is a typical way of considering knowledge within the natural sciences and a supposition tightly linked to the theoretical perspective of positivism. We find that constructivism on the other hand, deals with the notion of truth in a noticeably different way. From this perspective, knowledge and truth is internally constructed, and what is true is ‘socially negotiated’ (Blumberg et al., 2005). The constructivist standpoint is often seen in the theoretical perspective interpretivism (Crotty, 1998). If we use the widely cherished metaphor: ‘if a tree falls in the forest, and no one sees it, did it really fall?’ An objectivist will say that the truth is objective and therefore independent of the subject, meaning that the tree falls regardless of anyone watching. A pure constructivist, on the other hand,

16

will argue that there is no meaning without the mind and therefore contest the notion. We find that a general difference between positivism and interpretivism can be found in how knowledge is developed (Blumberg et al., 2005). Positivism is concerned with reducing phenomena to simple elements, which is to represent general laws. On the contrary, the interpretivism that we see in the humanities and in some branches of social science, seeks a broad and total view on the phenomena, with the intent to find explanations that goes beyond the current knowledge (ibid.). An interpretivist approach therefore typically results in a qualitative research approach, rather than the quantitative that is most commonly used in positivism. In line with Crotty (1998), we believe that our methodological standpoint should spring from the questions that we are trying to answer. Due to the exploratory nature of our research purpose we have chosen a qualitative research design, which will provide us with new and valuable insights and explanations to a phenomenon that goes beyond existing knowledge.

2.2.2 Reasoning and theory construction

In the literature the relationship between theory and research are mainly described in philosophical terms, but we find that their interconnectedness have operational implications for our study (Blumberg et al., 2005). The literature describes two main models for reasoning and logic: Deduction and induction. Deductive reasoning moves from the general to the specific. That is, from “(1) a pattern that might be logically or theoretically expected to (2) observations that test whether the expected pattern actually occurs” (Babbie, 2007, p. 22). Deduction has traditionally been the preferred method of reasoning in the positivistic perspective as it is based on ‘proof’ and validation. In contrast, inductive reasoning moves from the specific to the general - “from a set of specific observations to the discovery of a pattern that represents some degree of order among all the given events” (ibid., p. 22). In the inductive model conclusions are viewed more as hypotheses, and are thus not presented as the only possible truth.

17

The practical implication of using either induction or deduction lies in the way arguments are constructed. In inductive reasoning arguments are built around previous experience or observations, while deductive reasoning bases arguments on theory and other accepted principles (ibid.).

Figure 4: Wheel of Science, inspired by Adeline de Gruyter (1971). Since this study is concerned with uncovering a new field of research, and hence do not have previously stated hypotheses, it is mainly based on inductive reasoning. However, in practice, most research projects sequentially make use of both reasoning approaches in the interplay between empirical data and theory (Blumberg et al., 2005). Even though we do not explicitly work with hypotheses, deduction occurs when we use theoretical sources to explain certain empirical phenomena.

2.3 Research Design

By taking its point of departure in our research purpose the following paragraph will account for the choices made regarding our methodological combination of a multiple case study and a literature review. A more detailed account of the methods will follow in the subsequent sections. The overall purpose of this thesis is to identify value propositions for mobile app

data and explore the maturity level of mobile app analytics.

"#$%&'!

%()$&*+",%-)!

$./,&,0+1!2$-$&+1,3+",%-) #'/%"#$)$)

!"#$

%&!'"!

!"!#$%&'(!

18

In order to adequately fulfill the overall purpose, we find it necessary to break down the main problem into sub-questions, which allows us to focus on three particular areas of mobile app analytics. Figure 5 illustrates the three questions and the research methods applied in each question. Since few prior studies have been engaged with identifying the value potentials of mobile app data in particular, we will employ a literature review synthesizing sources from related fields such as mobile communication, digital footprints, Big data and the personal data ecosystem. By combining these areas, we illustrate unique characteristics of mobile app data and develop a set of value propositions for the data source. This will describe the insights that potentially can be derived from the analysis of mobile app data, which we will use to compare with the current performance of the app analytics tools in our case companies. Mobile app analytics happen in an organizational context. Therefore we have chosen to employ a multiple case study approach where we can study the topic in its natural setting. The case study approach allows us to study the entire ecosystem of mobile app analytics in an organization, while the multiplicity increases our ability to identify relevant elements and deliver robust results. This approach allows us access to three mobile apps and three analytics tools, on which we will base a large part of our analysis. By systematically inspecting the three apps and their related analytics tools we can identify the available data types from each platform. Our analysis of the three case companies furthermore entails qualitative semi-structured interviews with the app responsible in each company. From this we gain access into the strategic considerations and particular app analytics activities and discover how each company generally works with data and decision-making.

2.3.1 Research model

The following model illustrates how we fulfill our overall research purpose by addressing the three research questions:



3. How are mobile app analytics used to support organizational decision-making?

19

As mentioned the three questions each contribute with a different perspective of mobile app analytics. The model demonstrates specifically what methods are applied in each question and their relative weight. As can be seen, the first question is addressed by reviewing previous literature, while the two subsequent questions are more empirically founded.

Figure 5: Research design overview

20

2.4 Research Methods

The aim of this section is to elaborate on the particular methods that we have chosen to collect empirical data from the ‘real word’. As illustrated above our research design consists of a multiple case study, a series of qualitative research interviews, a feature inspection of the related apps and tools, and finally an extensive literature review. These methods will be presented individually in the following sections.

2.4.1 Case studies

The case study specialist Robert Yin provides a frequently cited definition of the case study as “an empirical inquiry that investigates a contemporary phenomenon within its real context; when the boundaries between phenomenon and context are not clearly evident; and in which multiple sources of evidence are used” (Yin, 1984, p. 23). The case study approach is rooted in the social sciences and is typically descriptive or exploratory in nature, as the purpose of a case study is to identify relevant phenomena and clarify how they unfold in a particular context (Yin, 1984). As the quote above describes, the case study approach is relevant when the object of study is embedded into its context to such an extent, that it becomes meaningless to separate the two. We find that evaluating how mobile app analytics is used in an organizational context is as crucial to the study as the examination of the data source itself. This means that the boundaries are blurred between phenomenon and context, and that separating the two disputes the goal of our entire study. Furthermore the case study approach is appropriate for holistic analysis, for instance when an object of study entails multiple elements and processes (ibid.). The value propositions of mobile app analytics are dependent on both the app as a data source, the analytics tools, and the organizational context, why the phenomenon must be studied holistically. For this purpose we employ several methods to gather empirical data, as Yin suggests in his quote above. These will be outlined subsequently.

Multiple case studies: In order to examine the field of mobile app analytics in its organizational context, we have chosen a multiple case study approach. The rationalization behind this choice is that we are able to discover more relevant

21

variables of mobile app analytics and deliver more consolidated results, using multiple cases, than we would be using just one case. A single case approach can be appropriate when examining an extreme or rare case, with limited access to data, while a multiple case approach is considered to deliver more robust results (Blumberg et al., 2005). Hence while a study of the circumstances surrounding mobile app analytics in one organizational environment can be said to deliver context dependent results, the substance of our findings increase as several cases are examined. Within the time frame and scope of this project we have found it appropriate to examine three cases, as this allows us to broaden our empirical analysis while still being able go into depth with each case. Traditionally in a multiple case study approach, the cases will be selected on the basis of some predefined criteria of commonalities or differences between the companies. Our case company selection process, however, has taken its point of departure in the apps rather than the companies. In line with our scope, the primary criterion is that the app is created as a support function to existing business processes, not as the central element in a business model. This is important since we want to examine how mobile app analytics can add value to an organization, not how a mobile app company can measure performance. Furthermore within our scope we want to examine different types of apps in order to map as many input types as possible. A final criterion is that the apps must be connected to an analytics tool. Hence our three case companies were chosen on the basis of their apps, and how these apps complement each other. The exploratory and inductive nature of our study means that no criteria besides the app were put forward, as no theory, hypothesis or prior studies could inform such criteria. In each case company we have sought to find the person with most knowledge about our topic. Therefore, each case representative has been chosen according to their level of contribution to, and knowledge about, the app development process - from idea to implementation - and their practical involvement in the companies’ use of mobile and other digital media.

22

2.4.2 Semi-structured interviews

As part of our case study framework we have chosen to conduct a series of qualitative research interviews. This section will therefore account for our particular take on the qualitative research interview, which we will use to gain insights into the current stage of analytics work within our case companies. In line with our constructivist standpoint we find qualitative interviews to be a valuable method for gaining insights into the views and opinions of individuals involved with our subject of study. When conducting interviews the main goal for us as researchers is to obtain knowledge about the so-called ‘life world’ of the interviewee by interpreting the meaning of the described situations (Kvale, 2007). We use qualitative research interviews in order to obtain a holistic understanding of the organizational attributes surrounding the three companies, how they generally work with data, the strategies behind their app and the analytics initiatives, and specifically how they use the data generated from their apps. According to Blumberg et al. (2005) there are three essential forms of scientific interviews: structured, semi-structured and unstructured. Determining the type of interview to conduct is often based on the fundamental research philosophy, the data collection strategy and the problem at hand (Blumberg et al., 2005). In each case we have chosen to conduct an interview with the company representatives that were chosen on the basis of their knowledge about the mobile app analytics processes. They are not to be treated as experts, but as subjects with key knowledge and insights into the practical and strategic aspects of our research area (Kvale, 2007). We conduct two rounds of interviews; a preliminary unstructured interview and a second round of in-depth semi-structured interviews. In the exploratory phase of our studies we employ an unstructured interview approach, where the researcher only keeps a mental list of what questions to ask, and apart from that, the format is very loosely defined (Blumberg et al., 2005). We approach these initial interviews with no predetermined expectations of what to find, as the aim is to gain a broad understanding of the field and life world of the interviewees. The findings from these interviews subsequently guide the design of a more structured and formal set of interviews.

23

For the second round of interviews we use a semi-structured interview approach, where a more carefully designed interview guide frames the interview. We do not incorporate specific questions, as we want the conversation to flow freely, but include topics and areas that are necessary to answer our research questions (see Appendix I). Furthermore we do not want to induce any specific terminology on the interviewees, why we make sure to use ‘neutral wording’ and open-ended questions. The goal of the interviews is to mimic a conversation between two parties on a particular topic (Kvale, 1997). We aim to make the interviewee feel free to bring up any topic he or she finds relevant, allowing us to maximize the outcome (Kvale, 2007). Throughout the entire design process we make a point of asking questions that lead to dialogue and discussion and also consider how to make the interviewee ‘open up’ and feel secure. As part of the conversational form of the interview we specifically attempt to prompt the atmosphere in the order and formulation of topics. We start with general, factual, ‘non sensitive’ questions and slowly progress to more specific and knowledge intensive ones (Kvale, 1997). All interviews are recorded and transcribed in order to enhance the further processing of the data, which we will elaborate on in the section on coding and data analysis.

2.4.3 Feature inspection method Since mobile apps and analytics tools present a new object of study, no established research tradition exists to guide this part of our empirical research. We therefore examine the three apps and their associated tools by performing a variation of a usability method called Feature Inspection (Nielsen, 1994). The original aim of this method is to describe the technical features of a program, or a piece of software, as detailed as possible often with the aim of making comparative analysis (ibid.). We use the inspection method to examine the more data oriented aspects of mobile app analytics with the objective to map the three apps and tools systematically and in as much detail as possible. We find that the feature inspection method sets a meaningful frame for our systematic review, since the goal is to conduct a manual examination of the technical aspects of a program (application or tool), which serves the purpose of our study well. The original feature inspection method relies on a predefined checklist of expected features from which the walkthrough is carried out. Due to

24

our exploratory approach, we aim at discovering the present conditions instead of looking for errors, inconsistencies or lack of user-friendliness. Rather than making a predefined checklist of what we expect to find, we list the features we come across, and map them according to functionality and data type. The purpose of mapping all features of the apps and the tools is to be able to compare the app input with the tool output, and thereby be in a position to assess what data our case companies are currently gaining from their apps. We further aim to gain insights into the current stage of the tools involved in our study. In the following we will briefly describe the method applied on the apps and tools accordingly.

App inspection: We conduct a systematic feature inspection of the three apps using the exact same approach in all three cases. We inspect the application according to the ‘natural’ order of features. Starting from the landing page, going from left to right, and from the top and downwards. From each page we navigate as ‘deep’ as we can, repeating this path for each feature. This way we systematically go through all features in order to map all functionalities and data types (see Appendix V, VI, VII). The paths of each app are visualized in a tree-model as shown in Figure 6.

Figur 6: Mapping of app input types

25

Tool inspection: Much like the method applied to the apps, we systematically inspect the features and data types of the three analytics tools. Again we follow the most ‘natural’ paths within the system and navigate as deep as possible. Similar to the app inspection the findings are mapped, although the complexity of the tools requires a more systematic approach. In order to get a thorough understanding of how the tools function, a series of data extractions are made in this inspection process.

2.4.4 Literature review

Literature reviews can serve a multitude of purposes ranging from complex theory building to theory evaluation or accounts of the entire ‘body of knowledge’ of a particular topic. The method is often referred to as a separate methodological discipline, with the objective of researching a topic or problem by reviewing previously written literature. However, in practice, literature reviews are often applied as part of a research design along with other data collection methods (Baumeister & Leary, 1997). According to Baumeister & Leary (1997) a literature review differs from empirical research in the questions it can address, and the conclusions it is able to draw. By focusing on patterns and connections among several empirical findings, a literature review can address theoretical questions that are beyond the scope of any one empirical study (Baumeister & Leary, 1997, p. 313). This is relevant for our study since we apply a literature review to examine the maturity level of the mobile app analytics field, which is a problem area with a relatively broad scope. Due to the novelty of this topic we simultaneously suggest a body of knowledge for the emerging research field, where no prior theoretical framework exists. Conducting literature reviews involves three main activities; searching for relevant sources, reading and evaluating the literature, and finally synthesizing the findings (Blumberg et al., 2005). Due to the novelty of our problem area, the literature included in this thesis is primarily found through online academic databases. Our searches are directed by the problem formulation as well as related variables identified in the initial stages of our exploratory research. Furthermore we use a method informally referred to as ‘snowball literature search’. This includes finding an article on a given topic, and browsing through its literature references to find supplementing sources among these. When repeating this activity with several papers within a given subject, the chance of

26

discovering a large portion of the relevant and authoritative sources is high (ibid.). As research projects often benefit from methodological diversity, so can a literature review profit from an assortment or sources (ibid.). As mentioned the third activity when conducting a literature review is to combine and synthesize the various pieces of literature into a new whole. We will do this by bridging and illustrating the interrelatedness of disparate sources, which have not previously been connected. These sources can be distinct in terms of the topics they cover, in the methods they apply, or the literary genre. The breadth and diversity of sources lies naturally in our problem area, as the novelty of the field calls for a combination of scholarly sources, business white papers and online sources.

2.4.5 Online sources

Specifically for this thesis, when attempting to undertake a new area of study within the digital realm, online literature becomes an important source of information. Various blogs and websites concerned with data, analytics and the mobile platform have become central in order to uncover relevant variables and tendencies within our subject area. These sources will often be the first to detect and formulate certain trends and interesting cases about a subject, long before the scholarly literature can generate articles about it. However such blog entries and websites are rarely based on a scientific foundation, seldom refer to scholarly sources in their proclamations, and rarely construct scientific argumentation. Furthermore many of the available online sources concerned with mobile app analytics have some kind of affiliation with the field and can therefore lack objectivity. Consequently, in order to make the most of these, online sources should be approached critically and used for what they are good at. Namely uncovering current movements and orientations within the field, providing real life examples and linking to other interesting sources. We find that the proclamations made in online literary sources must often be supported by empirical findings or scholarly literature. This has been an important guideline for us during the entire research process, both in our exploratory research and especially when conducting our literature reviews.

27

2.5 Data Analysis

After all empirical data has been successfully collected, we begin to systematize and categorize the findings before our analytical process begins. This following section is devoted to the analytical methods applied to the collected data, and hence the process of turning data into insights for further analysis and discussion.

2.5.1 Data coding

Having transcribed all the recorded interviews and mapped out all features and data types of the involved apps and tools, we initiate our coding process. Coding is a pre-analysis method that can be seen as the transitional phase between data collection and the more extensive analysis. The coding process enables us to group and organize data into categories through which patterns appear and themes and concepts arise (Saldaña, 2009). A code is typically a word or phrase assigned to a section of text in order to capture the underlying meaning. The exact wording depends on the coding-paradigm, which include descriptive-, value-, theme- and in-vivo- coding, among others. The distinction is basically between using objective descriptive wording or more value-laden categories, while the in-vivo method is characterized by using the interviewee’s own words as codes (ibid.). Coding the interview data is basically carried out using pen and paper. We print the interview transcripts, read through the material and write the words in the margin that captures the meaning of the data. We use an objective descriptive coding method, trying to capture the essence of the statements without making any premature interpretations or analysis. The data from the feature inspections consists of a large set of variables in form of descriptive feature labels using the terminology of the apps and tools in question. From the application inspections a set of input categories are created containing related features, while redundancies are erased after systematic evaluations. The input types will thus refer to the type of data that the users create when interacting with a specific feature in the app. A set of common categories is determined, but the input types from the different apps are kept separately for further analysis. A similar approach is carried out turning the tool variables into output categories. This coding process demands a more thorough investigation of the functionalities of the different tool features, before placing them in common categories.

28

2.6 Research Quality

In qualitative research the findings will be reliant on the interpretations of the researcher, meaning that the ‘truth’ will be somewhat normative (Blumberg et al., 2005). For this reason we find it necessary to establish quality measures in order to strengthen the soundness of our findings. Discussing quality in terms of validity and reliability belongs to the positivistic research approach, and is less applicable for this type of qualitative study (ibid.). In their pure form, quantitative and qualitative methods are opposing paradigms, with fundamentally different understandings of ‘true knowledge’, why we find it necessary to use other types of assessment tools when evaluating the quality of our research. A main focus for us will be to eliminate biases, present our finding as unambiguously as possible and establish trustworthiness and transparency around our results (Golafshani, 2003). This will enable other researchers to evaluate our methods and thus judge the robustness of our results, which is one of the main purposes of clear and precise reporting (Blumberg et al., 2005). Finally we find it crucial that we as researchers frankly reveal the limitations of the conducted study. This is not to undermine the results, but on the contrary to build trust around the proclaimed quality of our study. The specific methodological limitations will be discussed in the following section, equally with the intention to enhance transparency and trust. The general limitations of our results will be presented after our conclusions, in a closing chapter of this thesis.

2.7 Methodological Limitations

We realize that with a qualitative research approach results are more easily influenced by our own personal biases and idiosyncrasies. The inherent strength of qualitative research is to be able to obtain a deep understanding of a phenomenon, but the focus on detail, subjectivity and context conversely becomes a limitation as it prevents the more objective view. In case studies the research subject is embedded into its context to such an extent, that it can be difficult to isolate the phenomena and distinguish between cause and effect. Furthermore the case study setting presents an uncontrollable and complex environment where many factors might affect the given study

29

objects. The current stage of app analytics, within our case companies, consists of a series of different factors, which can be difficult to separate. One is the design of the app; another is the selection of tool, and the way the tool is set up. Additionally we have to take into account how the company is organized, their general workflow, processes, priorities, and management style. These conditions present a limitation since it will be difficult to avoid ambiguities - something we will try to overcome by ensuring transparency as mentioned in the previous section. Conducting literature reviews is equally dependent on the judgment calls of the researcher performing the searches and reviews. Choosing to follow one path will include leaving others behind, and every selection contains interpretations of the importance and relevancy with a certain degree of subjectivity. With an exploratory approach we try to exhaust a new field of all relevant resources, which presents limitations due to the scope and resources of this project.

2.8 Summary

As we have illustrated in this chapter, our research purpose requires a combination of methodological inquiries. Since we aim to uncover and identify the constituting elements of a new research field, our research approach is a reciprocal process between exploration and description. Due to the novelty of the field we have no prior hypotheses and are therefore highly driven by inductive reasoning. Our methodology is based on an interpretivist view on knowledge creation, and thus a qualitative approach to data collection. Our qualitative research design consists of a case study, which provides the frame for our empirical inquires. Within each case company we conduct two rounds of qualitative interviews with the case representative, as well as a series of systematic feature inspections of the apps and tools that our case companies currently employ. The empirical analysis is supplemented by a comprehensive literature review, enabling us to build a body of knowledge for an emerging field that previously has not been covered in academic literature. Our empirical data is coded in order to deduce the patterns, themes and characteristics, which is to underpin our analysis. Throughout this thesis we will continuously present results, analyze findings and accordingly discuss their implications for our research field.

30

3 CASE DESCRIPTION

This thesis bases its empirical analysis on a multiple case study approach where each case contains three objects of study: the app, the app analytics tool and the organization. We have chosen three case companies that are characterized by a prominent position within the Danish marked, or by their innovative use of mobile apps. Føtex presents one of the largest retail chains in Denmark, with more than 88 stores across the country. Nykredit is the first financial institute in Denmark to develop a mobile app, while AO has achieved industry awards for their innovative take on a business-to-business application. All three companies have developed an app that contributes to their core business, while central activities lie beyond the mere app functionalities. Common for all three apps is that they are used as a customer touch point, as opposed to an internal service or tool. The apps represent three different services and each entail a variety of functionalities. The three apps therefore complement each other well and thus allow us to explore a broad array of features and data types. In the following section we will present our three case companies, each case representative, as well as briefly account for the main ideas and functionalities behind their apps. Throughout our analysis we will dive further into the specific deliberations, strategic considerations and particular analytics processes carried out in each case.

31

3.1 Føtex

Føtex is part of the business concern Dansk Supermarked - a large corporation consisting of 1300 large retail stores in Denmark and other European countries. Dansk Supermarked employs 43.000 people in Denmark while Føtex alone has 15.000 employees dispersed between the headquarter in Brabrand and their retail stores (Dansk Supermarked1). Dansk Supermarked has recently been subject to a great deal of organizational change and restructuring. Where each subsidiary traditionally was managed disassociated from each other, many alignment initiatives have been implemented since 2011. For instance, the communication departments, procurement departments and e-commerce departments for each subsidiary have been consolidated and will hereafter be centralized to achieve economy of scale. In 2012 the organization got a new CEO; Per Bank, after the previous CEO had been occupying the position since 1999. According to Per Bank the financial crisis has changed the way consumers buy their everyday groceries and non-food articles (Dansk Supermarked2). His goal with the various consolidations is a more agile and flexible organization that prospectively can respond to changes in consumer patterns in a more efficient manner. In order to detect such changes, the organization will focus on getting in closer contact with their customers by making customer analysis through surveys, interviews and workshops. Our contact in Føtex is Kristine Salmonsen, a project manager in charge of digital and mobile marketing. She has been responsible for the Føtex app, its development and maintenance, since the decision to spread onto the mobile platform was made in 2010. She is therefore well acquainted with the entire process of developing and implementing the app, as well as the thoughts behind their analytics activities. Our contact with the company also includes Thomas Nielsen, who is a system lead consultant for the mobile platform across the entire group. Thomas Nielsen is responsible for reporting on performance of each app in the organization.

1 www.dsg.dk (a) 2 www.dsg.dk (b)

32

3.1.1 Indkøbshjælp

Føtex’s app Indkøbshjælp (Shopping Aid) is designed to assist the customer in the shopping situation. Its main feature is a shopping list where users can add and remove items and share the list with other Føtex app users by SMS or e-mail. The app also consists of Føtex’s weekly leaflet, a recipe directory, and a service that helps the users locate the nearest physical store. From both the leaflet and the recipes, items can be directly added to the shopping list. Hence the main objective for the app is to be a helpful tool to Føtex’s costumers in their everyday shopping activities.

Image 1: Screenshots from Indkøbshjælp

3.2 Nykredit

Nykredit is a leading Danish finance institution with 4100 employees between its headquarter in Copenhagen and the many subdivisions all around Denmark. They have commercial and mortgage banking as their cornerstones, but also have activities within insurance, leasing, pension and real estate (Nykredit3). The company is structured around four integrated business units: Customers, Products, Operations, and Support. Each unit is supported by a number of specialized competence centers and central staff functions. The responsibility for the digital activities lies in Operations, which comprises the company’s production- and administrative functions, IT operations, digital channels and customer service.

3 www.nykredit.dk

33

Nykredit’s case representative, Thomas Clausen, is a senior project manager in the digital media department, responsible for all of the digital customer touch points such as the website, home banking, mobile banking, and banner adds. Nykredit was the first financial institution in Denmark to launch a mobile app and has since developed quite a large portfolio of products for the mobile platform. Thomas Clausen is responsible for the development and maintenance of the various apps, their mobile analytics and reporting activities. He has been close to the entire app development process and is daily engaged in the performance and utility of the app.

3.2.1 Mobilbank

In Nykredit our case app is a mobile banking service with many of the functionalities known from home banking services tailored to the mobile platform. The main features are centered on banking services such as checking account balance, paying bills, transferring money etc. There are also several ways in which the user can visualize and categorize their spendings to get an overview of their expenditures. Apart from this, the app contains numerous support functions such as a currency converter and directions to the nearest Nykredit branch or ATM. The app also includes several ways to get in contact with Nykredit employees.

Image 2: Screenshots from MitNykredit

34

3.3 AO

Our third case company AO is a wholesaler and distributer specialized in products for the plumbing industry. AO is a business-to-business enterprise with a customer base consisting mainly of small plumbing firms with 1-10 employees. The company employs 700 people, has 48 wholesale stores all around Denmark, and a fully automated warehouse from which their products are distributed. When customers place an order either from their e-commerce platforms or over the phone, the items are sent to the stores where it can be picked up shortly after. In addition a large portion of the revenue is made directly in the stores where customers can purchase their products on site (AO4). AO is structured with central staff functions such as marketing and HR referring directly to top management, and underlying divisions such as Sales, Procurement and Inventory. The app responsibility is placed in the department of development and marketing, headed by Søren Thingholm - our contact person in AO. Søren Thingholm is titled Development Manager and is in charge of business development, e-commerce and marketing. As head of these departments he has a central position and refers directly to top management. Søren Thingholm is involved with all of the company’s e-commerce platforms, which currently counts a web shop, and apps for the mobile and tablet platforms. He is involved with the internal information systems, such as the inventory and distribution systems and CRM system. He is additionally responsible for analytics activities and reporting of the performance measures for each of the company’s digital products.

3.3.1 AO.dk mobil

AO’s app is centered on supporting customers when they are buying construction and plumbing items. Its main feature is a large product catalogue from which users can check prices and place orders. All products are categorized and listed so that the users can search for products, check inventory and add them to an order list. Users can also find product information or add items to their order list, by using a scanner function when in the physical stores. Hence, the app can function as a mobile product catalogue, or as an actual m-commerce app.

4 www.ao.dk

35

Image 3: Screenshots from Ao.dk mobil

Besides these main functionalities, the app entails several other features. The users can locate the nearest store, read news about AO and the industry, update their customer profiles and check-in to let colleagues know their location.

3.4 Summary

Our three cases present obvious similarities, as well as differences. Both Nykredit and AO have designed their app so that it is directly integrated into their central business activities and is a natural addition to their other digital platforms and services. Føtex’s app functions more as a support service that can help their customers in shopping situations, but does not allow for any transactions as is the case with AO and Nykredit. Our cases represent three very different organizations in three distinctive industries. Føtex and AO can be said to be product oriented, while Nykredit offers a service. AO stands out since it is the only business-to-business company where the other two can be described as business-to-consumer enterprises. Although they all have a relatively high number of employees and thus qualify as ‘large enterprises’5, we see a large variation between 700 employees in AO, 4100 in Nykredit and 15.000 in Føtex. In all three cases, we see a tendency to move core business activities online, though they all have a physical location where they meet and interact with their customers on a daily basis.

5 According to the European Commission companies with less than 250 employees qualify as small and medium-sized enterprises (SMEs) and those above qualify as large enterprises. (www.wikipedia.com: Small and medium enterprises)

36

The purpose of this section has been to give an introduction to our case companies, while a more detailed elaboration of how our case companies work with app analytics will follow in our analysis. We have been trusted with access to information and data that is sensitive to the companies and have therefore agreed to sign a Non-Disclosure Agreement, promising that we will safeguard the information obtained and not reveal any knowledge that could be considered sensitive.

37

4 MACRO ENVIRONMENT ANALYSIS

The aim of this chapter is to provide a scan of the macro environment that surrounds the field of mobile app analytics. This macro environment analysis will focus on the constituting elements of the app analytics ecosystem, which we find is determinative of the value propositions that we can ascribe to mobile app analytics. Smartphones and mobile apps are prerequisites for the app analytics ecosystem. We will therefore initiate this inquiry by outlining these technological entities. Subsequently we focus on three main stakeholders in the app analytics ecosystem: the app owners, which are the companies that develop the apps, the app users, and finally the mobile app analytics providers. When exploring the characteristics of mobile app data, and determining the maturity level of the field, these prerequisites and main stakeholders make out the foundation for our analysis. The aim of this chapter is thus to provide an introduction to the field, and furthermore provide insights and definitions that we will draw on throughout our thesis.

4.1 The Smartphone

The mobile revolution has changed the way we communicate, carry out our daily routines and navigate in urban spaces. Our conception of time and space has changed immensely, since we became able to communicate and access

38

information, more or less, anytime – anywhere (Ling, 2008). Communication functionalities have progressively migrated to the mobile platform, transforming it from a portable telephone, to a personal mobile computer and information repository, typically referred to as smartphones. The boundaries between the Smartphone and the less advanced feature phone are often blurred and unspecified. However some technical attributes are typically ascribed to the smartphones such as Internet and wireless access, built-in sensors, touchscreen and increased processing power (Ling & Svanæs, 2011). Furthermore smartphones are often characterized by their ability to download and install apps that extend their functionalities. As their desktop counterparts, smartphones are run on different operating systems, mainly dominated by Apple’s iOS and Google’s Android platform. We expect that the term ‘smartphone’ will eventually die out, as all mobile phones at one point will have adapted smartphone functionalities, and other more advanced phones will take over the position in the market. But for the time being, we will be using this definition as it illustrates the current stage of technological development.

4.2 Mobile Apps

As explained above, smartphones are characterized by their ability to install apps. This section will bring a definition of apps and furthermore highlight its main characteristics. Apps are the small programs, which we access from our smartphone ‘desktops’. Although there is no industry-wide definition, Pew Internet provides a description, that we find adequately sums up the term. Apps are “[...] end-user software applications that are designed for a cell phone operating system and which extend the phone’s capabilities by enabling users to perform particular tasks” (Pew Internet, 2011, p. 2). They further draw a clear distinction between ‘applications’ and ‘functions’, namely that apps are software-based, while functions are “hardware enabled activities such as taking pictures and recording video” (Pew Internet, 2011, p. 2). To complete its purposes, many apps will activate hardware-functions such as the camera, GPS or accelerometer within the device, which is one of the features that distinguish the app from regular mobile browsing.

39

Besides the integration with the phones’ features and services, apps have a particular characteristic that distinguishes it from the general capabilities of the smartphone. Compared to many other online activities, apps are usually single purposed and focused on one particular service or function (Ling & Svanæs, 2011). ‘Normal’ Internet browsing activities are characterized by short attention spans and a multitude of alternatives and links, which lead the user to other sites. On the contrary, once a user enters an app it usually provides several navigation options within the app itself, but rarely let the user move into another portion of the web. This cultivates the app as a ‘silo’ for individual attention, which has become a rare occurrence in a media landscape characterized by endless alternatives and information ‘overload’ (ibid.). The siloed nature of apps is seen in regard to the practical use of it, as it is difficult to navigate away from the app, as well as in regard to the advantage of having undivided attention from the user. Apps are distributed through sales portals provided by the smartphone suppliers, with the two market dominators being Apples App Store and Google Play. According to Datatilsynet (2011) these suppliers have quite different profiles. Apples iPhone and its associated operating system iOS is a closed system, only running on iPhones. Apple preapproves content and software in all the apps that are offered in App Store, which according to a recent press release counts 775,000 apps available for download (Apple6). The Android platform is a more liberal system with a variety of hand set providers and an open-source developing environment (Datatilsynet, 2011). The Android platform has previously been lagging behind Apple in regards to the volume of apps, but several sources report that Google Play has now surpassed Apple and as of January 2013 has approximately 800,000 apps in store. (McCarra, 2013). A post in the popular media blog The Sociable estimates that, by June 2013, Google Play will reach their one million app milestone, and continue to increase their market share in a steady curve (ibid.).

4.3 The App Analytics Ecosystem

In a report from Datatilsynet, the authors provide an overview of the app ecosystem that illustrates the various stakeholders and parties constituting the 6 www.apple.com

40

application market. Inspired by their model we identify three players we find of particular importance to the app analytics ecosystem; the app users, the app owners and the analytics tool providers.

Figure 7: ‘Players in the app ecosystem’, inspired by Datatilsynet, 2011

4.3.1 The app users

Though this thesis is focused on the analytics field from an organizational perspective, the app user plays a central role in the app analytics ecosystem. Since a smartphone ownership is a premise for the access to apps, this section will examine the diffusion of smartphones in a Danish context. According to a study performed by Index Danmark/Gallup, the number of people in Denmark who owns a smartphone has increased significantly through the last years. The study shows that the number has grown from 1.5 million to 2.1 million in just one year (Danske Medier7). These findings are supported by the mobile consumer study Our Mobile Planet, which Google conducted for the same time period. This survey shows that the smartphone penetration in Denmark has increased from 30 percent of the population in the first quarter of 2011, to 45 % the following year (Our Mobile Planet: Denmark, 2012).

7 www.danskemedier.dk

41

According to the Google survey the smartphone penetration rate is highest among the 18-29 year olds where approximately two thirds own a smartphone. This number drops as the age increases till about 22 percent for the age group of 50 and above. Another indicative finding shows that the smartphone owners are becoming increasingly reliant on their devices, with 86 percent using their phones when ‘on the go’ (ibid.). We find that as the use of apps increases, so does the concern for privacy violations. Several projects and studies are launched with the aim of discovering how the collection and transfer of app data might breach the individual’s right to privacy (Datatilsynet, 2011). In an experiment carried out by a Wall Street Journal investigator, 50 of the most popular apps from Apple’s App Store and Google Play were investigated in order to reveal the data streams from the apps send to third parties (Wall Street Journal8). According to the project initiators; “These phones don't keep secrets. They are sharing this personal data widely and regularly” (ibid.). While the quantities and personal nature of the data raise privacy for the app user, they simultaneously present a valuable asset for companies that seek knowledge about their customers and are thus a prerequisite for mobile app analytics.

4.3.2 The app owners

From an enterprise point of view the mobile platform offers a new communication touch point, increasingly used for marketing or branding purposes. Hence in recent years there has been an explosion in the number of commercial apps covering a wide range of services.

The Networked Business Factbook, which examines how Danish businesses make use of social media, mobile services and cloud technology, illustrates the novelty of the app market. The report is based on a wide-ranging survey conducted on 2742 participants, from a wide selection of Danish companies and public institutions. According to their study, mobile services are a fairly new area of interest for most Danish businesses. Nearly half of the recipients are currently present on the mobile platform, and the area is continuing to show rapid growth. Of the companies that are present on the mobile platform, more than half have engaged in their mobile activities within the last year. 23 % have been active between one and two years, while only 8% have worked with mobile solutions for 8 www.online.wsj.com

42

more than three years. Many companies see great potentials in the mobile arena and have therefore prioritized the matter and placed the mobile responsibility on a high organizational level. Of the companies that are not present on the mobile platform, 38% answer that the reason is that they are currently planning their mobile initiatives (Social Semantics.eu, 2012). The study furthermore shows that the branding value is seen as the number one reason why companies invest in mobile solutions, while a large part are also motivated by the business development benefits that comes with an increased customer loyalty, improved services and added value in terms of mobility. These numbers clearly illustrates the novelty of the mobile field for organizations and the interest it is receiving among Danish companies. The study by Social Semantics hereby emphasize how mobile app analytics can be expected to be object of increased attention in the coming years, as the companies grow interest in documenting and rationalizing their mobile investments.

4.3.3 Tool providers

The third major player in the app analytics ecosystem is the analytics tools, often referred to as the third parties. While the mobile app industry is relatively young, there are already a variety of platforms competing to provide companies with good app analytics such as Flurry, Google Analytics, SiteCatalyst, Localytics, WebTrends and many more. These tools serve to help app owners obtain data on how their costumers interact with the content they are presented to, as well as gain knowledge about their behavior. The app analytics tools are based on a similar tradition to web analytics and social media analytics. Each tool functions by offering the companies a software development kit (SDK) that is to be installed in the apps code source. These tags track user-interactions and subsequently send the captured data to the tool in question. There is typically a standard code package, but custom tracking is also available for most tools, allowing the companies to keep track of events and occurrences of particular interest (Apptamin9 ). In large, the tool interfaces offer a compilation of graphs and data visualizations, where a selection of metrics can be selected and combined to illustrate the performance measures of interest. The visualizations often consist of a certain

9 www.apptamin.com

43

variable and its occurrence over time, with an adjustable time span. The market already counts many players with different specializations such as games or location, and many with supporting services such as cross promotions, add management etc. (ibid.).

4.4 Summary

The aim of this chapter is to introduce the field of mobile app analytics, identify main players in the macro environment surrounding the field and provide explanations and definitions to important concepts. The chapter has thereby delivered characteristics of smartphones and apps, entities that are prerequisites for app analytics. Furthermore we have accounted for three main players in the app analytics ecosystem, namely the app users, the app owners and the tool providers. We will draw on the definitions and descriptions outlined in this chapter for the remaining of our thesis.

44

5 THE VALUE OF MOBILE DATA

As the first part of a trinity aimed at uncovering the field of mobile app analytics, we intend to explore the medium from which the data is collected. The purpose of this chapter is thus to analyze the characteristics of mobile app data and its inherent value propositions, in order to answer our first research question. Since few previous studies have been devoted to examine the value created from mobile app data, we turn our attention to more established research areas. This part of our study has consequently been carried out as an extensive literature review and discussion, where we combine and synthesize sources that are relevant for our particular research purpose. We will thereby draw on previous findings in order to construct a body of knowledge related specifically to mobile app data. From our exploratory research we find that mobile app data contains two core value potentials - the value propositions of the mobile platform to which it is naturally affiliated, and value propositions belonging to digital data sources in general. Accordingly this chapter is divided into two main sections; the first part will outline the specific mobile attributes and the particular value propositions that can be ascribed to the mobile platform. The second section will take a broader view on the current data tendencies that we associate with the value of new digital data sources. Finally, a concluding section will provide a discussion of the literature presented in the two previous parts. This discussion allows us to identify and outline the potential value propositions specifically for mobile app data, which we apply in the subsequent parts of this thesis.

45

We will begin this chapter by outlining the characteristics of digital footprints, and in particular the data traits generated from mobile devices. We will then characterize the mobile platform and the sensor technology that makes the app a distinctive data source. We will further examine what value propositions may be attributed to the mobile platform by reviewing literature where mobile data is utilized as the empirical foundation. The second part of this chapter will focus on the expected value creation of Big data and Personal Data, with particular emphasis on their application in relation to mobile app data.

5.1 Characterizing Mobile Footprints

Digital footprints is a literary trope, referring to the fact that our presence in the digital realm leaves behind traces of our interactions, data that does not vanish and thus becomes detectable by others (Wexelblat & Maes, 1999). One of the first popular references to the term is Negroponte’s idea of a ‘slug trail’, which he introduces in his book Being Digital as early as 1995 (Fish, 2009). The relevance of the notion increased with the emergence of Web 2.0, where collective intelligence and social interaction began to dominate more and more of our activities online, resulting in an increasing amount of digital footprints (O’Reilly, 2005). The next generation, referenced as the Semantic Web, further anchors the importance of searchable and processable data in relation to the growing amount of digital data widely produced by users of digital products and services (Gruber, 2007). Scholars concerned with digital data generally agree on an overall definition of digital footprints. As an example Zhang et al. (2010) refers to the term as the “digital traces left by people while interacting with cyber-physical spaces” (Zhang et al, 2010, p. 1), while Wikipedia defines it as “a collection of activities and behaviors recorded when an entity (such as a person) interacts in a digital environment” (Digital Footprints10). In further detail Fish - the author of the book My Digital Footprint - states that “digital footprints are the digital ‘cookie crumbs’ that we all leave when we use some form of digital service, application, appliance, object or device, or in some cases as we pass through or by” (Fish, 200911). He further points out that digital footprints are largely invisible and that they can reveal where we have been, for how long, and eventually also with whom (Fish, 2009).

10www.wikipedia.org 11 E-book: My Digital Footprints

46

In Fish’s objective there are three main sources of digital footprints: the web, broadcast/listen media and the mobile. While these subdivisions are still dominating the picture, technological advances mean that the platforms are converging into multi-modal and heterogeneous media sources (ibid.). Novel technologies such as smartphones, tablets, social networking sites and phenomena such as the Internet of Things are examples of new constellations from which interactions create a broad array of digital footprints (Zhang et al., 2010). According to Fish (2009), mobile phones in particular will add to a growing share of digital footprints, making this platform increasingly more prominent compared to the more traditional sources of digital footprints such as broadcast media and the web. First of all, we spend more time with the mobile phone than we do with any other device, since we carry it with us for the majority of our waking hours (Fish, 2009). Secondly, the mobile phone is increasingly used as repository of personal information of various types (Ling, 2008). Furthermore the mobile phone is significant in that it provides a wide range of data, many of which are unique to the mobile platform (Fish, 2009). Along with the technological advances of mobile devices, the list of existing data sources have developed from simple call records and SMS logs, to data on location, browsing, search, social networks, content creation or purchasing behavior (ibid.). In the following section we will describe the defining characteristics of mobile footprints, in order to pinpoint the mobile platform and the app’s potential as a data source. Thereafter we will go into greater detail with new data constellations by exploring the concept behind the Internet of Things and the advancing sensor technologies facilitated by the smartphones, and thus the foundation for apps.

5.1.1 The mobile platform

In this section we will outline the distinctive characteristics of the mobile platform. Since our research objective is to explore the field of mobile app data and analytics, our forthcoming focus will be on the smartphone and its particular features, though some of the highlighted features will be common for both smartphones and regular, less advanced, feature phones. Through our literature review we have found a series of mobile characteristic themes, which will be synthesized and described in the following paragraphs:

47

Personal: As opposed to the traditional landline the mobile phone is a highly personal device. In the western world each family member will typically have his or her own personal mobile phone and rarely use a mobile phone belonging to someone else (Ling, 2008). Since the mobile has also become a repository of personal information, such as contact lists, pictures, etc., the device has become an intimate and personal accessory (Bauer et al. 2005; Becker & Arnold, 2010). This is increasingly true for the purchase and use of mobile apps, since each app offers a particular service mainly beneficial for the owner of the device on which it is downloaded (Ling & Svanæs, 2011).

Ubiquitous: The ubiquity of the mobile phone can be understood in two ways. First, ubiquity can refer to the fact that the penetration rate of mobile phones is above a 100 percent in the Western World (Ling, 2008), with smartphones accounting for nearly half (Our Mobile Planet, 2012). Secondly, it can mean that mobile phone users typically have their phone with them everywhere and at all times (Bauer et al., 2005). As we saw in the Google study, Our Mobile Planet (2012), the nature of modern mobility means that we can perform progressively advanced tasks when ‘on the go’. Naturally, this also implies that we constantly have access to the data and information that our phones contain, or that we can be located by the help of our mobile devices (McManus & Scornavacca, 2005).

Interactive: Like other digital media, the mobile phone is a highly interactive device. Due to media convergence the mobile phone currently facilitates a rich communication channel, which reaches far beyond calling and texting. This is true on a person-to-person basis, but also relevant when considering costumer-company relationships (Bauer et al., 2005; Becker & Arnold, 2010). These interactions will typically take place on mobile websites, or through company apps, but also on mobile social networking sites. Interactivity also refers to the way users can interact with content and functionalities in a variety of ways, combining highly synchronized communications, such as online chats with asynchronized, in the form on texts, mails and the like (Bruhn-Jensen in Helles, 2010). Location aware: Thanks to location-based technologies such as GPS, WiFi and Bluetooth it is possible to localize devices, and thus users, at all times within just a few meters precision (Becker & Arnold, 2010). The built-in location sensors can detect where the device is located, while call data records on cell phone towers can

48

reveal the positioning of the device when in use. As a data source location awareness is unique to the mobile platform adding a contextual layer to the data. Multimodal: Smartphones in particular add a layer of multimodality, due to the advances in possible interaction modes and input sensitivities (Oviatt, 2002). Apps serve a multitude of purposes and make use of a range of interaction modes possible. This results in a variety of data outputs, something that we will discuss further in the following section on Sensor Networks. Together these characteristics form a unique compilation of data types, although not all of them are exclusive to the mobile platform, nor to the app. In the following we will elaborate further on the multimodal aspect of the mobile phone, which is particularly prevalent in the smartphones. As described in the macro environment analysis the smartphone is characterized as an advanced mobile phone with built-in sensors and the ability to download and install apps. These features add an extra layer to the concept of mobile data, which we will explore further in the following section.

5.1.2 Advanced sensor technology

In the above we find that the data variety of smartphones has grown and the platform has become an increasingly complex repository of data sources. This is not least ascribed to its many sensor capabilities, which will be explored below. When objects can both sense their surroundings and communicate, they are able to take actions relative to their environment or help others take action (McKinsey Quarterly, 2010). Due to the advances in miniaturization and nanotechnology, ever smaller things will have the ability to interact and connect, while the advancing sensor technology will cause a growth in data varieties (ibid.). A sensor is basically a converter that can measure physical qualities and convert these into signals that can be read by an instrument. Sensors can measure a variety of input including pressure, movement, temperature and position, just to mention a few. The combination of detailed sensor data and the ability to connect and send data through the Internet makes out a potentially rich data source, though we are only standing on the brink of this new ubiquitous computing and communication era (ibid.)

49

According to the McKinsey Quarterly (2010), in the years to come, networking technologies, and the standards that support them, must evolve to the point where data can flow freely among sensors, computers, and actuators. Software to aggregate and analyze data, as well as graphic display techniques, must improve to the point where huge volumes of data can be absorbed by human decision makers or synthesized to guide automated systems more appropriately (ibid.).

5.1.3 Sensor networks and the internet of things

Recent advances in network- and sensor technology have led to a major step in the direction towards the much-anticipated realization of the Internet of Things. In short, the concept refers to the notion of expanding the revolution in communication to objects. According to The International Telecommunication Union (ITU), “The Internet of Things is neither science fiction nor industry hype, but is based on solid technological advances and visions of network ubiquity that are zealously being realized” (ITU, 2005, p. 2). They subsequently provide an official definition of the term as: “[…] a global infrastructure for the Information Society, enabling advanced services by interconnecting (physical and virtual) things based on, existing and evolving, interoperable information and communication technologies” (ITU, 2005) As the name implies, Internet of Things is the activity of allowing objects to connect and communicate. This definition cements the potential of the concept, while emphasizing the possibility of further progress and innovation. Specific technological references are thus not included, due to the unforeseeable elements in the coming development, which prolongs the lifetime of their definition (ibid.). In order to connect physical objects and devices to databases and networks a simple and cost-effective system of identifying items is crucial. This will enable a large set of data about things to be collected for further processing and analysis. One technology that is currently in use is the Radio Frequency Identification (RFID) tags, which can be placed on everyday objects, allowing them to connect to the Internet. By means of wireless networks the RFID tags can connect to the Internet using the same IP address system that computers use to connect to the Internet. This technology is mainly known from the business-to-business marketplaces where products are tracked moving through the supply chains, thus improving inventory management, while reducing payroll and logistics costs (McKinsey Quarterly, 2010). Ever-smaller silicon chips for this purpose are gaining new capabilities while costs are decreasing, resulting in a more rapid diffusion rate. According to the ITU, “a new dimension has been added to the world of information and communication

50

technologies (ICTs): from anytime, anyplace connectivity for anyone, we will now have connectivity for anything” (ITU, 2005 p. 2). In another perspective O’Reilly and Battelle (2009) question the assumption that the Internet of Things will solely spring form the combination of cheap RFID and IP addresses; they suggest that we will get to the Internet of Things by captivating objects with the use of mobile phones, image recognition, search and the sentient web. They make the claim that: “We don’t have to wait until each item in the supermarket has a unique machine-readable ID. Instead, we can make do with bar codes, tags on photos, and other ‘hacks’ that are simply ways of brute-forcing identity out of reality” (O’Reilly & Battelle, 2009 p. 8). As we see, there is a growing awareness of the fact that new technologies increasingly are able to sense and communicate with their surroundings. We find that the sensors embedded in smartphones, and their activation by accompanying apps, play an important role in the realization and spread of the Internet of Things, which further emphasizes the value potentials that can be ascribed to the mobile platform as a gathering of rich data sources. In the following we will look at some of the research areas that have made use of mobile data as their empirical foundation. We have not yet encountered any research project that has gathered data through the use of mobile apps as such, but we expect that the value that are ascribed to the mobile platform shares common traits with mobile app data. Whether the value propositions of the mobile platform can be equated with app data will be further discussed in this chapter’s final section.

5.2 Mobile Data and Research Value

Several research communities have in recent years come to realize the potentials of the mobile platform as a data source. In the following, we will provide three examples of research projects that have made use of the mobile phone as a means to data collection in order to extract value propositions and use scenarios from their studies.

51

5.2.1 Reality mining and urban planning

Reality Mining is one of these research fields that use mobile data as their empirical foundation. The term is referring to the process of extracting patterns particularly from behavioral data – thus ‘mining the reality’ (Eagle et al., 2005). This process compiles behavioral data from mobile phones, using statistical analysis and machine learning methods. Its potential to increase the understanding of ourselves and our society made MIT’s Technology Review identify Reality Mining as one of “10 emerging technologies that will change the world” (Pentland, 2009, p. 75). According to Pentland the very nature of mobile phones makes them an ideal vehicle to study both individuals and organizations since people habitually carry their mobile phones with them and use them as a medium for a majority of their communication. Until recently most behavioral research has been done using self-report data, while reality mining now offers a second-by-second picture of actual group interactions over extended periods of time, providing dynamic structural and contextual information (Pentland, 2009). An MIT research group engaged with reality mining has conducted a series of studies using call record data and Bluetooth proximity, but has in recent years additionally begun to collect data by using the more complex sensor systems available in smartphones. “The fact that mobile phones have GPS means that we can leap beyond demographics directly to measuring behavior” (Pentland, 2009, p. 77). As an example GPS data can enable the discovery of the independent subgroups within a given city by analyzing travel patterns. This can reveal intimate details of people’s lives such as where they eat, work, and hang out (Pentland, 2009). The researchers’ proclaimed goal is that the society can use this new in-depth understanding of individual behavior to increase the efficiency and responsiveness of industries and governments. According to reality mining researchers, the application areas are numerous, but the method has mainly been tested on projects about transportation and traffic in urban spaces as well as interaction patterns in social networks. The data has mainly been aggregated and treated as a whole, measuring the combined movements, actions, and connections within larger groups of individuals. Another research area that makes use of mobile data as an empirical source to human behavior is Urban Planning. Urban planning is concerned with the dynamics, activities, design and organization of urban space and integrates areas such as land use and infrastructure. The methods applied in urban planning are

52

similar to those used in reality mining projects, why these two fields seems to have overlapping interests and appliances. According to urban planning researchers Reades et al.: “The intimately personal nature of the mobile phone and our growing ability to track it across the urban landscape [...] opens an unprecedented window into how millions of people, each pursuing their individual interests and responsibilities, use the city” (Reades et al., 2009, p. 835) As the quote above illustrates mobile network data opens a unique window into the behaviors and interactions of individuals within communities. Where previous urban planning research has used other data such as bank notes to examine human motion, a study by González et al. (2008) found that mobile data offer a relatively precise proxy to illustrate individual and group trajectories. While most examples focus on the movement and distribution of people in urban areas, other studies focus on the communication patterns of people. Such studies can be useful when planning communication infrastructures or placing cell phone towers. An example of a study, interested in examining communication behaviors, Eagle et al. (2009) analyze mobile data collected over four years from 1.4 million cell phone users. This comprehensive project aims at examining behavioral patterns that differentiate urban and rural communities. Furthermore they examine whether individuals change their patterns of communication according to their social environment. The authors conclude that mobile phone data can be useful when studying social change, and a suitable tool to compare the behavior of different groups or geographical locations (Eagle et al., 2009). In urban planning, as well as reality mining studies, location is calculated on the basis of the user’s proximity to the cell phone towers. The analyses are primarily done on aggregated data sets from mobile cell towers, collected by the telecommunication companies by law. The researchers thus gain access to the call data records and base their research on these. They treat the users as a whole and focus is on group behavior and patterns, and less on individuals. In the next section we will take a look at a research field that focuses more on the individual data level and in particular on the sensor capabilities embedded into the smartphones.

53

5.2.2 Mobile phone sensing

Another example of a research field that has explored the mobile phone as a means of extracting knowledge about human behavior is Mobile Phone Sensing. While reality mining and urban planning has mainly conducted large-scale projects using extensive data sets consisting of call records, mobile phone sensing is concerned with the sensing capabilities of the mobile device and the type of knowledge that researchers can derive from here. As mentioned above, the wide range of technological advances has empowered the mobile phone to become an advanced sensing device suitable for data collection. Today’s smartphones not only serve as a key computing and communication device in peoples’ lives, but also come with a rich set of embedded sensors such as an accelerometer, digital compass, gyroscope, GPS, microphone, and camera (Lane et, al, 2010). These new sensors embedded into the smartphone are enabling new applications across a wide variety of domains. Mobile sensing researchers predict that the new types of sensing applications will become valuable to research areas concerned with healthcare, social networks, safety, environmental monitoring, and transportation (ibid.). Most recent projects have been carried out as experiments, giving participants mobile devices and advising them on how to collect data using the sensors of the smartphone. Instead of focusing on large groups or entire communities, sensing is most often applied on an individual level. The experimental data collection that most often takes place in these studies has been given the name participatory sensing, as opposed to opportunistic sensing (ibid.). In participatory data collection participants have to give acceptance before the researcher can gain access, allowing the user to retain control of their raw data. In opportunistic sensing the data is automatically logged, without the need of a direct consent from the user (ibid.). The literature suggests that each approach have its different advantages. Since the participatory approach demands user involvement and consent, the size of the projects is limited, as researchers will have to spend time locating participants and negotiating the terms of the data collection. The benefit of opportunistic sensing is that it lowers the burden placed on the user, allowing overall participation by a population of users to remain high. Nevertheless, the opportunistic approach raises more evident privacy concerns, meaning that a researcher will have to take extra precautions when storing and handling the personal data (Zhang et al., 2010).

54

5.2.3 Summary

In the preceding section we have presented literature concerned with the mobile phone and the new stream of digital data sources dominating the current research agenda. We will now sum up our findings and thereby set the stage for the discussion that follows our section on data value propositions. The mobile phone is characterized by being personal, interactive, ubiquitous location aware and multimodal. Smart phones and apps enable advanced sensing, transforming the mobile platform from a communication unit, into a multi-sensor tracking device. The advanced sensor capabilities, facilitating the Internet of Things, are one reason why the subject of mobile data becomes highly relevant and thus widely cited in literature related to data consumption. These capabilities have been explored in research projects concerned with detecting human behavior and interaction such as reality mining, urban planning and mobile phone sensing. The examples, taken from the research literature, highlight a set of value propositions of mobile data. In each project the mobile platform as a mean to measure human behavior is strongly emphasized. This is a research area that has been prone to many challenges in the past, due to the lack of scalability and measurement of user behavior. Furthermore the examples show that the mobile data source can be used to measure on both an aggregated and individual level. As our review of the literature reveals, location and proximity are the most anticipated value proposition for mobile data - an asset that truly distinguish the mobile compared to other sources of digital footprints. Web and broadcast/listen media have less location aware capabilities, and are thus lacking the time/space dimension, that is such a distinguishable advantage of the mobile phone as a data source. The literature included in the first part of this chapter has mainly been concerned with the mobile platform, not the app as such. In the closing paragraph of this chapter we will discuss whether the value propositions for the mobile app as a data source varies from the value propositions for the mobile platform. Where the preceding sections have focused on deriving value propositions in relation to the mobile platform, the following will focus on value propositions of digital data in general.

55

5.3 Data Value Propositions

“Data has become a new asset class” (WEF, 2011). Such idioms can be seen in the many recent articles and reports speaking in relation to the inherent possibilities the digital age are creating for business, government and research. A glance at the scientific literature and the blogosphere reveals that scientists, economists, and the business community all seem overwhelmed by the possibilities inherent in the substantial amounts of data suddenly available to us. As an example of this hype, Wired Magazine wrote on the cover of the issue The End of Science: “The quest for knowledge used to begin with grand theories. Now it begins with massive amounts of data. Welcome to the Petabyte Age” (Wired Magazine, 2008). Scanning the current business research arena we find two main tendencies when it comes to ascribing value to data. One lies in the ability to obtain detailed information about people, their interactions and location. The other relies on the growing quantities of data, which has recently been made accessible due to advances in data storing and mining technologies. In the following we will therefore present two dominating data trends, namely personal data and Big data.

5.3.1 Personal data

Personal data is data created by and about people, relating to a specific, identified or identifiable person. According to a report from the World Economic Forum (WEF) personal data generates a new wave of opportunities for economic and societal value creation (WEF, 2011). They further proclaim that personal data is the new ‘oil’ of the Internet, and the new ‘currency’ of the digital world - metaphors that clearly stress its potential importance and value. The digital footprints people leave behind when interacting with digital devices allow access into individual user behavior (Zhang et al., 2010). Furthermore, a range of influential companies such as Facebook, Twitter and Google are built entirely on the economy of personal data (WEF, 2011). The value of personal data lies in the opportunity to ‘paint a picture’ about the needs and behaviors of individual users rather than the population as a whole or segments of it. According to the World Economic Forum, organizations capture personal data in a variety of ways, which essentially can be divided into three data categories. Data can be volunteered when users explicitly create and share personal content on digital platforms. Personal data can furthermore be observed, which is the interaction patterns and behaviors captured about the users of digital products.

56

Finally, personal data can be inferred, a third type of personal data that occurs when an organization analyzes a combination of volunteered and observed data (ibid.). Personal data value creation: The fact that personal data can be related to one specific person facilitates some specific value propositions. The analysis of personal data enables personalization, also known as one-to-one marketing. In a popular book by Peppers and Rogers (1993) personalization is described as the practice of treating customers on an individual basis by tailoring products or services uniquely to each customer (Peppers & Rogers in Brynjolfsson & Wu, 2009). Since personal data relates to a specific person, various pieces of data can be connected to that individual user, creating a detailed image of their preferences and behaviors (Brynjolfsson & Wu, 2009). As mentioned in the report from the World Economic Forum (2011) observed personal data can record user behavior. The ability to record actual user behavior is significant since behavioral studies traditionally has been based on surveys and self-report data, often compromised by small-scale samples, biases and inconsistencies (Eagle et al., 2005). Several examples from the research community, a few of them mentioned previously in this chapter, illustrate how observed behavioral data can be useful, for example when using location data in reality mining and urban planning projects. Sensor input will for the most parts be in the form of observed personal data since the data is automatically logged without any need for direct user involvement. A discussion of the particular applications and connections of value creating activities of personal data and their implications for the mobile field will conclude this chapter.

5.3.2 Big data

As mentioned in the introduction, the growing amount of large scale data sets has fostered terms such as the Petabyte Age, a concept Chris Anderson, the Editor-in-Chief of Wired Magazine, exemplifies in the following quote: “This is a world where massive amounts of data and applied mathematics replace every other tool that might be brought to bear. Out with every theory of human behavior, from linguistics to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves” (Anderson in boyd & Crawford, 2011)

57

While such a clear attempt to provoke the established scientific research communities should be taken lightly, Anderson calls attention to a current data tendency that will be covered in the coming sections, namely Big Data. Technological developments and declining costs within data storage and processing, has enabled a new data hype referred to as Big data (ibid). Increasingly, massive quantities of data produced by and about people, things, and their interactions are accessible to us. The flood of data from sensors, computers, cameras, mobile phones and the like, surpassed the capacity of storage technologies in 2007, and are continually growing at a tremendous pace (The Economist, 2010). Rick Smolan is the creator of the book The Human Face of Big Data, which compiles more than 200 pages of photos, infographics and articles narrating how Big data is affecting nearly every aspect of our daily lives. The book further makes the point that “The average person today produces more data in a single day than a person in the 1500’s did in an entire lifetime.” (Smolan & Erwitt, 2012, p. 2). All in all it is fair to say that data has become Big! As the name implies, Big data has generally been defined in terms of its volume. According to Wikipedia, data becomes ‘big’ when it involves data sets that are too extensive for commonly used software tools to capture, manage and process, within a tolerable time frame (Big Data12). Other definitions focus less on volume and more on the business applications of Big data. According to Matt Aslett, research manager at IBM, Big data is almost universally defined as “the realization of greater business intelligence by storing, processing, and analyzing data that was previously ignored due to limitations of traditional data management technologies” (Zikopoulos et al., 2013, p. 18). The ability to collect, store and process large amount of data presents privacy concerns (Manovich, 2011). Yet, our literature review reveals a tendency that combines Big data analysis with a participatory sensing system as described previous in this chapter. Big data processing on the individual level has fostered a whole new data consumption movement, which focus on self-monitoring activities related to health and performance. The Human Face of Big Data mentioned above is an example of a movement that utilizes a combination of self-sensing and Big Data advantages. The project consists of a smartphone survey app, with the aim of collection insights into the everyday lives of people around the globe, and

12 www.wikipedia.com

58

simultaneous feeding the data back to the user, by displaying stats and infographics on the average outcomes. At the time of writing, the data set consists of answers from three million users, from more than 100 different countries. Another related tendency is seen in the Quantified Self movement, which is a collaboration of users and developers who share an interest in self-tracking. The members of the movement track and record how they work, sleep and exercise with the use of mobile and wearable sensor technologies. As an example, the sensor wristband Nike+ FuelBand allows its wearers to track all types of physical activities. The information is subsequently integrated into the Nike+ community and mobile app. This way the user can set his own fitness goals, monitor his progression, and compare himself to others part of the community (nikeinc13). In the following we will focus on three characteristics that differentiate Big Data, from previous data concepts, as well as identify its value propositions. Characteristics of big data: The literature suggests several characteristics that separate Big Data from previous activities concerned with extracting intelligence from data. A popular reference of Big Data characteristics is the three V’s, originally posited as early as 2001, meaning that Big Data is distinguishable by three main characteristics; volume, variety, and velocity (Laney, 2001). For instance, the IT consultancy agency Gartner defines Big Data as “high volume, velocity and/or variety information assets that demand cost-effective, innovative forms of information processing” (Gartner, 2012, p. 3). Volume refers to the massive amounts of data that are perpetually created. Big Data is distinctive because of these volumes and is often explained in immense terms such as petabytes and zettabytes as it can be seen in this sections’ opening quote (Zikopoulos et al., 2013). Although we see several examples in the literature, any attempt to pinpoint the exact data volume seems pointless, since it most likely will be outdated the moment this thesis has gone into print. We find it more appropriate to take notice of the fact that the literature reports that data volumes will grow with as much as 80 percent annually in the coming years, although the exact data growth rate is a contested matter in the literature. Variety refers to the diversity of data types and sources that increasingly make up Big Data. As the sources from which data can be extracted becomes ever more

13 www.nikeinc.com

59

numerous and varied, so will the data itself (McAfee & Brynjolfsson, 2012). It has previously been a challenge to consolidate data of different types, however technological and analytical development has made it possible to store and process multi-structured data. This enables a combination of structured data, for instance gathered through sales transactions, and unstructured data such as text strings, images and video (Zikopoulos et al, 2013). Finally, velocity refers to the speed of which the data can be collected and analyzed (McAfee & Brynjolfsson, 2012). Many new digital data sources allow for more or less real-time collection and analysis of data, potentially removing the time lag that has been a challenge of earlier data processing activities (Brynjolfsson & Wu, 2009). While this section has aimed at identifying Big Data characteristics, the following paragraph will pinpoint the specific areas where we see Big Data adding value. The particular applications and connections of the mobile field to the three V’s will be discussed in this chapters’ final section. Big data value creation: Big Data can ‘paint a picture’. With the granularity and multiple structures of Big Data, it is possible to connect various data formats and sources, allowing for holistic analysis and thus a greater understanding of the subject or situation in question. This means that increasing levels of detail can be obtained in a wider field of study (Zikopoulos et al, 2013). An example of the use of holistic analysis, is seen in how the American retail company Sears is combining customer, product and promotion data in order to create tailored offerings that take advantage of personal preferences, as well as local conditions (McAfee & Brynjolfsson, 2012). Because of the velocity of digital data information can be extracted almost instantly, increasing its relevancy and timeliness. For instance, research on Google search engine queries have shown how search data can estimate current levels of influenza activity with a reporting latency of one day, which is weeks before traditional health surveillance systems can detect such streams (Ginsberg et al, 2009). As another example, the real-time properties of Big Data, combined with the advances in sensor technologies, are used to predict natural disasters such as earthquakes and thus prevent devastating effects (ibid.). In the social sciences, researchers have previously been accustomed to choose between data breadth or data depth (Manovich, 2011). However, the volume and

60

variety associated with digital data sources means that it is possible to scale the data analysis on several granular levels, allowing a study to contain both breadth and depth. With low granular data communication data can be used to examine dynamics of social networks from a macro perspective (Lazer et al., 2009). The example mentioned above, where search engine data is used to document disease activity, is a good example of data analysis with high granularity. Moreover, the research literature reveals examples of Big Data analyses where the granularity levels change throughout the study. As an example, Eagle et al. (2005) uses large volumes of communication logs from mobile phones to analyze the dynamics of rural and urban societies. By analyzing data on the collective level, the researchers document behavioral differences between rural and urban environments. Furthermore, by analysis of individual data they are able to identify individuals who have moved between rural and urban areas and illustrate how these change their communication patterns according to their social environment (Eagle et al., 2005). Big Data can also be used for large scale processing of data with very high granularity. This is for instance the case with personalization activities where a type of mass customization can be applied. This describes the way companies like Amazon use volunteered, observed and inferred personal data to generate meaningful recommendations to individual users based on their prior behaviors (McKinsey Global Institute, 2011). This example furthermore illustrates an additional value proposition by applying Big Data analysis to large sets of personal data.

5.3.3 Data through a critical lens

While the paragraphs above might be seen as a blind acceptance of the data hype and its ever ‘increasing and incredible’ promises, it is not. Rather, it is an outline of the potentials of big and personal data described in the literature. However, the literature reveals several challenges and impediments that can hinder effective use of data. The hype surrounding data and its application potentials should be seen in the light of our ability to extract meaningful patterns from it, or what is commonly termed data analysis. This analytical process seeks to glean intelligence from data and translate it into useful information (McAfee & Brynjolfsson, 2012). The fact that Big Data has become commonplace, is one of the main reasons that it should be approached critically. According to boyd and Crawford (2011) data analysis is no longer a domain exclusive to data scientists or experts, but scattered among many people with uneven skills and understanding of data. This entails that many fail to

61

view this type of research through a critical lens, as they would employing any other scientific method (boyd & Crawford, 2011). Furthermore, the literature describes a gap between the promises of data analytics, and the actual capacity and skill sets needed to extract valuable knowledge from the new scale of data, as well as take full advantage of it (Manovich, 2011). This might explain why our review of the literature revealed that most articles are full of promises but short on good examples of the application of data projects. Furthermore, the examples of value propositions mentioned above primarily stem from research projects, not from the business communities. Finally, the ability to store and process large volumes of data on the personal level comprises privacy concerns and ethical implications. It will most likely be the people with data access and the skills to analyze large data quantities that will set the bar and decide the ground rules for what is ethical (ibid.). Hence, it is rarely the data producers themselves that gets to decide how, and for what purpose, their data is consumed. However, as illustrated above, we begin to see examples of well-functioning data ecosystems, where Big Data analysis and personal data can mutually benefit both data owners and producers.

5.3.4 Summary

This exploratory review reveals that Big Data and personal data, as outlined above, are two themes particularly apparent in the recent literature. Big Data is distinguished from other activities involved with extracting intelligence from data by its volume, variety and velocity. These characteristics result in certain value propositions, namely that Big Data allow for holistic analysis, enables the creation of timely and actionable insights, and facilitate analysis on different levels of granularity allowing for both depth and breadth. Furthermore, many of the new digital data sources are able to deliver personal data - data relating to a specific, identified or identifiable person. Personal data can be volunteered, observed or inferred which has implications for the types of information that can be extracted. By using personal data personalization can happen on a detailed level, allowing precise tailoring and unique customizations. It also entails the opportunity to map individual behavior, an activity that has been prone to many challenges and biases in the past. Finally, by combining personal data with Big Data analysis we find that personalization activities can happen on a large scale allowing for a type of mass customization.

62

This concludes our literature review. The following section will combine the findings from the two main sections in a discussion leading to an identification of the characteristics of mobile app data and its specific value propositions.

5.4 Discussing Mobile App Data Value

The main purpose of this chapter is to identify the characteristics of mobile app data, as well as explore its value propositions. This section will thus serve to combine the two main sections of this chapter by discussing how mobile app data corresponds with the inferred traits of Big Data and highlight the unique capabilities that can be ascribed to mobile app data.

5.4.1 Mobile app data and volume, variety and velocity

Big Data differs from other activities involved with extracting information from data by its volume, variety and velocity. In the following, we will discuss how mobile app data corresponds with these traits and what can be inferred about mobile app data on that account. We will additionally discuss the notion of volume as the dominant indicator of Big Data. As described above, since the mobile phone is characterized by multimodality and interactivity, the mobile platform contains many possible data sources and data types. Besides the more traditional tasks that users are able to carry out on the mobile phone, today’s smartphones come with a set of embedded sensors. When these hardware-functions are activated, they can register when users interact with their phone, as well as capture the location of where the app is used. These qualities correspond well to the variety indicator of Big Data. When users interact with digital devices, there is often an immediate response to their actions. Similarly, the digital footprint can be registered and processed instantaneously after its occurrence. Digital data, and therefore also mobile app data, can thus be collected and processed with a limited time lag. Hence, the mobile data can be recorded in real-time, meaning that mobile app data can be collected and processed with high velocity. Besides variety and velocity, volume is the last of the three indicators of Big Data, and additionally the most prominently used. Several of the research areas making use of mobile data have been carried out in large scale. In both reality mining and

63

urban planning projects the cell phone has been used to produce large volumes of data, thus correlating with the third indicator of Big Data. If we disregard call and SMS records, we argue that much of mobile data will be created by users’ interaction with various apps. In these separate ‘silos’, each app can generate data sets accessible to the owner companies. We will argue that whether these data sets are large enough to qualify as big depends on the context. With the right number of users, frequency of use and amount of data points, we find no reason to argue against that mobile app data can reach high volumes. As it is mentioned previously in this chapter, volume is an ambiguous indicator. In association with Big Data, volume is often defined as exceeding the limits of ‘normal’ data storage and analysis capabilities and technologies. However, as data storage technology evolves, ‘normal’ becomes a moving target. Therefore, as several authors mention, volume becomes a moving target as well. Additionally in a critical review of the Big Data hype, boyd and Crawford (2011) reject the tendency to claim that ‘bigger is better’ when it comes to data. Quantity does not necessarily mean quality. Moreover, a McKinsey report (2011), based on prior research notes that human beings are limited in their ability to consume and understand large volumes of data. In combination, these points might serve to justify that variety and velocity are more precise indicators of Big Data. Besides this point there is convincing statistics to support that mobile data will increase in volume in the coming years as many of our online activities move to the mobile platform. Arguably, mobile app data does in fact entail the possibilities of reaching Big Data value potentials and can therefore be used for holistic analysis, timely insights and intelligence on the individual level as well as in large scale.

5.4.2 Particulars of mobile app data

In this present chapter we find that the mobile platform can be characterized by being personal, interactive, ubiquitous, location aware and multimodal. Based on our literature review, we will argue that these characteristics make it a unique platform, capable of providing a series of different data types. In this concluding section we will highlight the uniqueness of the mobile app as a data source, as opposed to focusing on the mobile device as a platform. Furthermore, we will focus on the specific characteristics of apps that differentiate it from other digital data sources. We find it worth mentioning though, that the value propositions of smartphones and apps are tightly connected since the functionalities of the

64

smartphone are a prerequisite for the qualities that can be ascribed to mobile app data. In the literature we currently find two main tendencies related to both the uniqueness of mobile app data and the value that is associated with it. One tendency focuses on the smartphones’ unique technological properties, while the other emphasizes that the value lies in the vast amount of data, which is available due to the omnipresent nature of the mobile device. In the following we will discuss these two aspects in order to determine how they distinguish the app from other digital data sources. As mentioned, smartphones are equipped with a multitude of sensors, which make them increasingly aware of their surroundings. If designed for it, the apps can access these hardware functions, which ads a unique contextual layer to the data. Furthermore, the various functionalities inherent in the smartphone means that app data can take a variety of forms allowing for holistic analysis. Especially the time/space dimension gives the mobile app data exclusive attributes, which is illustrated in the research examples mentioned previously in this chapter. Both the research fields of reality mining and urban planning use location data over time as their main empirical foundation to study individual and group behaviors. As an example the contextual layer provided by the time/ space dimension is widely used to study communication patterns and how these change according to the surrounding environment. The ubiquity of mobile phones makes them unique as personal tracking devices. Since we carry the phones with us for a majority of our waking hours, the data streams from the mobile can paint a detailed picture of our everyday interactions. However, we will argue that the characteristics and use patterns of apps potentially can impede on this value proposition. In all probability the continuous data stream facilitated by the mobile phone is less profound since few apps are used continuously throughout the day. The siloed nature of the app thereby hinders the full advantages of the constant data flow. On the other hand, apps are distinctive in their ability to function offline, which can arguably generate a continuous data flow independent of external factors. The fact that the mobile app is interactive is, in itself, not enough to set it apart as a data source. The interactivity aspect is a cornerstone of digital communication, and

65

has a critical influence on the amounts of data that is produced by websites, social media and mobile phones. Hence, interactivity is not a unique characteristic of mobile phones or apps, but a prerequisite for the entire stream of digital and mobile footprints. As mentioned previously, the app is often characterized as a silo, and in many cases the user will never reach outside the app (Ling & Svanæs, 2011), as opposed to social media sites where communication often happens in a public sphere (boyd & Ellison, 2008) We argue that apps are more focused on its central functionality or service. It is therefore the interaction with the app that creates the data, and rarely newly created content as such. In this line of argumentation, observed interaction data will therefore be the main data type in mobile apps. We therefore argue that the app is a platform highly suitable to portray interactions and behaviors rather than insights on content, communication and sentiments. This strength is further supported by the research projects that use data collected from the mobile to replace self-reporting methods, which has been known to be error prone and biased. On an individual level, this mobile data seems highly appropriate for targeting, tailoring, and personalization, in other words for marketing purposes. On an aggregated level, the generalizability will increase and permit the identification of tendencies amongst a larger group of users. The ability to provide personal behavioral data in large volumes means that the mobile app can be useful for either types of analysis, or even an interchangeable focus on different data granularities. From our discussion of the literature and its appliance to the specific app capabilities, we can hereby summarize the main value propositions of mobile app data. We find that a primary value proposition of app data lies in its ability to add a contextual layer to the collected data. The apps are characterized as being able to access the built-in smartphone sensors, which adds context and affects the versatility of accessible data types. Though the ubiquitous and interactive characteristics of the app affect the data streams, they are less essential in regard to the specific data content. Mobile app data is further distinctive by the highly personal nature of mobile apps and their identified ability to portray actual user behavior. In this chapter we have identified the characteristics of mobile app data and its value proportions. The findings presented above will be applied in the subsequent

66

chapter where we will examine the current stage of mobile app analytics tools. By comparing the value propositions of mobile app data with the current opportunities of the associated tools, we evaluate to what degree the tools realize the app specific value propositions.

67

6 CURRENT STAGE OF APP ANALYTICS

If we consider analytics tools as the intermediary between the value of the data source on one side, and the data potentials for companies on the other, tools come to play a significant role for mobile app analytics. This chapter will provide an analysis of the current stage of mobile app analytics, and thus provide answers to our second research question. As mentioned in the clarification of concepts, the current stage of mobile app analytics refers to the developmental stage of the analytics market, as well as the analytics practice that is carried out in our case companies. The empirical foundation for this analysis consists of a tool industry review, a series of qualitative interviews and an exhaustive feature inspection of our case-apps and their respective analytics tools. The chapter will take a practical approach by gaining important hands-on experience with the apps and analytics tools currently in use in our case companies. We will begin this chapter with an introduction to the analytics tool industry, and the tools that currently constitute the app analytics market. Thereafter our analysis will provide findings from our qualitative interviews in regards to the companies’ app development process, their selection of analytics tool, as well as the challenges and obstacles they meet when working with these tools. This section will be followed by an empirical enquiry of our three case apps, and subsequently an inspection of the three analytics tools: Flurry, SiteCatalyst and Google Analytics.

68

The aim of our analysis is to explore three dimensions of the current stage of mobile app analytics. We will account for the app analytics tool industry perspective, illustrate the degree of deliberations and strategic thinking that our case companies put into their app analytics activities, and finally, account for our empirical enquiry of the three tools used by our case companies. After our analysis, we will discuss the potential gaps between the emphasized app data input types and the data that the tools currently provide. Furthermore, we will discuss whether the tools enable a realization of the value propositions of mobile app data identified in chapter five and in the tool industry review.

6.1 Tool Industry Analysis

In this section we will examine the current analytics industry, with a specific focus on the mobile app analytics tool market. Since smartphones interfere with many aspects of our daily lives, there is an increase in the interest from marketers and developers alike to measure how app users behave and interact with the apps (Matzner, 2012). While the mobile app industry is relatively young, there are already a variety of platforms competing to provide companies with app analytics insights. Little academic writing has been done on organizational app analytics usages, thus our industry review is based on literature from online magazines covering the latest trends in business and technology and influential blogs covering the analytics tendencies in the field.The following section will shortly present the market leaders in app analytics, and emphasize their characteristics in order to determine the general developmental stage of the mobile app analytics industry as a whole. After the tool overview, we will gather a verity of industry best practices and advice on how a company can get the most out of their app analytics initiative.

6.1.1 Analytics tool market

From our analysis we find that the app analytics market is dominated by a handful of tool providers. Furthermore a range of smaller and more specialized tools is available. The coming section will present some of the market’s leading tools which we find currently dominate the app analytics arena.

69

Generally, we find two main tendencies within the app analytics market; tools built specifically for the app platform and web-based tools that expand their portfolio with app functionalities. Flurry is one of the market leaders currently dominating the app analytics field: “More companies trust Flurry Analytics to understand how consumers interact with their mobile applications than all other app analytics providers combined” (Flurry14). 80.000 companies currently use Flurry on more than 230.000 apps, a number that has risen substantially within the last years (Flurry Blog15). Flurry is among the pioneers in app analytics, launching their first tool version in 2008, shortly after the first iPhone began to gain grounds in the market. Flurry offers a ‘free of charge’, ‘plug-and-play’ tool, with a range of predefined metrics, including several engagement measures in addition to custom-made ‘event’ tracking. In august 2012, Flurry furthermore proposed a series of new features based on big data analysis capabilities. This includes benchmarking options that allow companies to match their activities with companies within similar industries. They simultaneously offered a segmentation feature called Flurry Personas, which divide users into groups such as ‘personal finance geeks’, ‘business professionals’ or ‘food and drinks lovers’ (Takahashi, 2012). The benchmarking, as well as the personas feature, is computed from aggregated and anonymous analytics usage data gathered from the more than 200,000 apps in the Flurry network (ibid.). Localytics is another frequently cited tool specialized in mobile app analytics. Like Flurry, Localytics also launched their first tool in 2008. Although the name implies that the tool specializes in location data, a closer look reveals that the tool provides much of the same metrics as Flurry. However, the tool does allow for location tracking by accessing GPS data. Localytics is based on a Freemium model, hence offering a basic free version, with the option of upgrading to a paid Premium version, as well as an Enterprise addition with advanced capabilities. While the standard version offers simple tracking, much like Flurry, the companies who purchase an Enterprise version, gets a series of ‘filters’ that lets the user cross tabulate data to segment users into groups and thus enable an identification of the most ‘valuable customers’ (Localytics16). The selling points for Localytics are ‘up-to-the-minute’ real time

14 www.flurry.com 15 www.blog.flurry.com 16 www.localytics.com

70

measurement, locational data in the form of real-time heat maps and app marketing automations, allowing users to “slice and dice in a variety of ways” (Crunchbase17). Another important feature of Localytics is the ability to integrate the app data with an existing data warehouse or business intelligence system. Flurry and Localytics represent a series of mobile app analytics tools, developed specifically for the app market. The focus is on simplicity and ease of access and use. On Localytics’ website they write “Integration is easy, takes just 10 minutes and requires only a few lines of code” (Localytics18), while Flurry announce that their “Basic setup is simple and quick, taking less than 5 minutes” (Flurry Support19) On the other end of the scale, we find the larger and more established analytics agencies that has sprung from web analytics and recently implemented app analytics offerings. We have identified Google Analytics, Adobe SiteCatalyst and WebTrends to be among the main players in this category. Google Analytics was founded in 2005 and has since become the most commonly used tool for performing web analytics. Like other web analytics companies Google Analytics has later added mobile functionalities to their service and allowed their users to track the traffic created by mobile websites and apps (Marketingland20). Though the initial mobile reporting allowed marketers to connect their Android and Apple apps to their Google accounts, the mobile reporting was based on the same metrics as for the web, and was not able to trace much of the app characteristics (ibid.). In the fall of 2012, Google Analytics launched an app specific update called Google App Analytics, creating app exclusive measures and targets. According to product manager at Google JiaJing Wang, “Our goal with the new Mobile App Analytics reports is to help marketers and developers measure the end-to-end value of their mobile app, to ultimately help them build richer, more engaging experiences for their users” (ibid.). Besides the standard tracking software development kits (SDKs) Google Analytics offers a wide range of customizations, a comprehensive support function, user forum, and a wide variety of benchmarking reports. SiteCatalyst is another major player in the analytics field, a tool widely used to perform web analytics and online marketing by larger corporations. Like Google

17 www.crunchbase.com 18 www.localytics.com 19 www.support.flurry.com 20 www.marketingland.com

71

Analytics, SiteCatalyst has recently added app analytics to their portfolio, though less has been written about their mobile initiatives. SiteCatalyst is a part of the integrated Adobe Marketing Cloud, which allows for a gathering of several analytics initiatives within the same tool. SiteCatalyst is built on ‘Omniture technology’ and is most often referred to as a ‘robust’ ‘powerful’ and ‘professional’ analytics tool (Webanablog21). While Google Analytics is free of charge, SiteCatalyst charges a monthly fee. The price varies depending on the different add-ons that the companies choose to include, as well as the type of support agreement they make. WebTrends is an example of a company offering a full palette of analytics services, including strategy consultancy, tool support and implementation advice (WebTrends22). WebTrends is not a traditional tool provider, but an agency customizing analytics solutions across their own analytics platforms. They furthermore publish a series of guides and white papers including reports on topics such as ‘Strategy and Mobile Maturity’, and ‘Metrics Driven Marketing’. The proclaimed goal is to advise companies on how to make the most of analytics, as well as develop a mobile analytics strategy before indulging in app development. WebTrends offers multi-platform analytics covering mobile apps, social media and websites. In combination with strategic consultancy this gives them the advantages of being able to offer sophisticated analytics solutions. This means that a company that utilizes their tools across platforms can gather otherwise anonymous behavior and house it in historical data log files of behavior linked to particular consumers. Once the consumers eventually make themselves ‘known’, being through purchases, logins to social media sites or the like, all the behavior that was once anonymous becomes associated with those consumers, and companies can thereby access digital footprints for their individual consumers. This presents more detailed segmentation and personalization options than any of the other tool providers. According to Michael Ricci, Vice President of Mobile Analytics at WebTrends, “The more data that is collected on the customer, the more the mobile site or application can be optimized towards the specific interests of each consumer” (Ricci, 2012). The following section will provide some industry best practice on strategy formation, as well as how to implement these analytics tools. Subsequently we will

21 www.webanablog.com 22 www.webtrends.com

72

go into detail with our empirical analysis of our case companies’ strategies with app analytics and the technical analysis of the apps and tools.

6.1.2 Mobile app analytics strategy

This section will elaborate on the strategic aspects of implementing analytics initiatives in an organizational context by examining advice and best practices from analytics literature and industry opinion leaders. This section will begin with an introduction to some of the industry ‘voices’ that we have chosen to include. Stratigent is a consultancy agency with expertise in building effective organizational data processes both system wise and company wise. Stratigent recently published a report on mobile analytics performance, based on a survey involving 241 marketers, who have some degree of analytics processes implemented in their organization. The aim of the survey was to investigate the current stage of mobile involvement and analytics strategies in the contributing companies (Cropper, 2012). We will further draw on the experiences and recommendations of Michael Ricci, Vice President of mobile analytics at WebTrends, as well as include best practice advice derived from the white paper on Mobile Analytics (2012), authored by Eric Rickson, who is the Director of Mobile Analytics at the company. As a final source we will gather a series of app analytics best practices from the online magazine Mashable. In November 2012 the magazine published an article advising marketers and businesses on how to get the most out of their app analytics projects. The author, Ryan Matzner, points to a series of best practices that can help companies make the most of their mobile analytics initiatives. The following section will focus on two central themes, drawn from the sources above, namely tool selection and the formation of an app analytics strategy. Finally we will outline the specific value propositions of mobile app analytics as described by the industry professionals.

6.1.3 Strategy and tool selection

A common theme in the industry literature is the importance of formulating a strategy before indulging in app analytics. The respondents in Stratigent’s survey identified a lack of mobile strategy as one of their key barriers to mobile analytics

73

success. Rickson equally identifies that “[…] marketers skip the critical step of defining and planning a mobile market strategy, heading straight to application development instead” (Rickson, 2012, p. 3). According to Cropper, a senior consultant in Stratigent, companies that are trying to succeed in the mobile domain should optimally have two different, but related, strategies regarding the mobile activities, one for the mobile platform and another for their analytics initiatives (Cropper, 2012). Cropper points to the fact that the analytics strategy should ideally be based on the platform strategy and share the same objectives, meaning that the platform strategy should be in place well before the company initiates the formation of an analytics strategy. Companies should thus ask questions such as: “What are we trying to do with our mobile site or application? How do we know when we have succeeded? And how can we use analytics to help succeed?” (Cropper, 2012, p. 6). In line with these questions, Matzner makes the point, that as an important part of the analytics strategy, managers or analysts should determine what they hope to understand better and set targets accordingly. Targets are the operational goals that need to be in place before an analytics process can begin and a prerequisite for any analytics success (Matzner, 2012). According to Ricci, the role of analytics is a critical factor to a brand’s ability to establish an effective mobile strategy and prioritize limited resources. He further states that “[…] without analytics verifying what content and elements customers are responding to, marketers are simply engaging in educated guesswork” (Ricci, 2012). As we can infer from the above, the industry emphasizes the importance of setting measurable goals and targets, both for the mobile platform, as well as the analytics activities. Matzner argues that the content of the app should play an important role in the strategy formulation process, since the app content in many respects determines what metrics are most important to measure on. For instance, the owner of an m-commerce app might be greatly interested in ‘point of sale’ estimates and ‘churn’, while stores with a physical presence will benefit from gaining insights into how their costumers move about in the physical space (Matzner, 2012). The range of mobile app analytics tools available, offer different variations of tracking. A general concern is whether to use a specialized tool, with the advantages that comes with that, or a broader tool allowing the analyst to align and integrate

74

many analytics initiatives in the same tool (ibid.).The app content and the central functionalities of the app should be an important factor when deciding which metrics to measure upon, and therefore also which tool is best suited for the particular analytics purpose. When implementing a given analytics platform, the tool provider will distribute the company with a Software Development Kit (SDK). According to Matzner it is critical to be very careful when implementing these SDK’s, as a malfunctioning tracking installation can affect the quality of the data. A root cause to incorrect or missing data is often mistakes or bugs in the tracking set-up. Therefore, an important component in the analytics process is continuous quality assessments of the data and identification of persistent sources of errors in the tracking code (ibid.).

6.1.4 App analytics value propositions

From the analytics industry professionals, as well as the tool providers, we can infer that the value propositions of mobile app analytics are mainly seen in the tools’ ability to optimize the app, and thus enhance the user experience. The main goal for the new Google App Analytics tool is, for instance, to help marketers ‘build richer and more engaging experiences for their user’s, while Flurry emphasizes their aim to help companies ‘understand how their customers interact with their apps’. This tendency to focus on the app usages is equally seen in our industry review, where analytics professionals recommend that the analytics strategy should be tightly linked to the goals and objectives of the app. By optimizing app usage according to the overall objectives for the mobile platform, the industry suggests that the improved user experience will result in greater brand associations, additional sales and customer loyalty. This illustrates that the tool industry perceives the value of app analytics in relation to app optimization, and does not focus on the app as a valuable data source per se. In the discussion that concludes this chapter we will therefore provide a comparison of the analytics value creation that the tool industry highlights, and the data value that we have identified in chapter five. From this tool perspective we will continue our analysis by examining the current stage of mobile app analytics within our case companies.

75

6.2 App Development and Tool Implementation

In the following part of our analysis we will focus on the initial app development process and the tool implementation, as is has unfolded in our case companies. We find that looking at the whole development process, from app design to tool implementation, provides a holistic view on the motives and ambitions that underpins their entire analytics initiatives. The aim is thus to derive the considerations and reflections behind the app development phase and tool implementation process, as well as emphasize the practical experiences they have gained. The following sections will take a case specific approach to analytics based on our qualitative interviews. We will present each case separately, and subsequently draw out central themes and tendencies for discussion.

6.2.1 Føtex

This section provides an analysis based on interviews with Kristine Salmonsen, who is a project manager in the digital marketing department, and Thomas Nielsen, a lead system consultant working in a cross-organizational analytics function (Appendix IV). Føtex launched their supermarket app Indkøbshjælp (Shopping Aid) in the spring of 2012. The development process took approximately one year from the management formed the initial idea, till the app was ready for download in the app stores. The simple motive behind the app initiative was, according to Kristine Salmonsen, that “the time was ready for Føtex to have an app like the other companies in the business concern”. Additionally she mentions the creation of a new customer ‘touch point’ as a target for the app, commenced by the fact that an increasing number of customers are turning down door-to-door supermarket leaflets. We thereby find that the main rationale was that the app should function as a channel between Føtex and their customers and not add any particular service or fulfill a specific function. The functionalities of the app were decided based on the goal of creating a valuable utility in the daily lives of Føtex’s customers. Based on statistics showing that a large percentage of Føtex’s customers use their mobile phone while buying groceries, the shopping list was chosen as the main service. Besides this feature, the app holds their sales leaflet and a recipe database that automatically lets the users add groceries to their shopping list.

76

The conceptual and technical developments of the app were made by an external agency. The interview revealed that the decision to connect the app to the analytics tool Flurry came from the external developers and was made without any particular considerations regarding tool functionality or app content. Six months after app-launch it was connected to Flurry, from which they have been tracking their app usage since. Kristine Salmonsen did not have any preferences regarding the tool, although she later regrets not having decided to use SiteCatalyst for their app, since this is the analytics tool that the department uses for their web statistics. According to Thomas Nielsen the usefulness they find in the tools are still very low. The tracking code lacks customization and they can therefore only access standard top-line measures. When asked which of the services and functions in Flurry they uses, Thomas Nielsen answers that they are only using the most basic measurements as he is rather skeptical about the validity of the data that Flurry provides. He points to a lack of transparency when it comes to metrics definitions and terminology consistency, particularly in comparison to the other tools he works with. As an example Thomas Nielsen mentions Flurry’s Personas feature, cited in the industry review. Since the general level of information in Flurry is low, he finds it difficult to evaluate whether or not the personas that Flurry presents adequately covers Føtex’s own user groups. He is therefore critical towards the information that the tool provide since it is generally difficult to see exactly which data the analysis is based on. On the question of what data granularity they prefer as the basis for their app insights, Thomas Nielsen comments that analysis on the individual level can be very valuable, when it comes to developing segments and archetypes. On the other hand he finds this type of data processing to be highly time-consuming, and requires large amounts of resources that are currently not allocated for analytics activities in Føtex. As the paragraphs above illustrate, neither the app development nor the tool selection and implementation process in Føtex has been characterized by any strategic vision otherwise suggested by the literature. Although the main functionality of the app was decided on the basis of a statistical foundation, no measurable KPIs or other quantifiable goals were set. Furthermore the current tracking level is low and characterized by standardized solutions, rather than customization according to targets. Finally we find some skepticism towards the validity of the analytics tool and its measurements.

77

6.2.2 Nykredit

The mobile banking app MitNykredit (MyNykredit) was ready for launch in December 2010, and thereby became part of an app portfolio counting a handful of different mobile services. According to Thomas Clausen, senior project manager in the department of Digital Media, the reasoning behind the app was to move bank functionalities, known from the online banking systems, to the mobile platform once the proper technical and security standards was developed. Commenced by a pressure from the top management, as well as their customers, Thomas Clausen was urged to create a mobile banking solution within just six weeks. Due to this hectic development phase the initial tracking was keep at a minimum, and an actual analytics set up “was not on their radar until after the app was successfully launched”. Although app analytics was an imperative for Thomas Clausen, tool implementation came in the second row after the app usage had taken off, and there were “time and resources” to take the next step. The app covers basic bank functionalities, such as account overview, account transfers and different types of payment options. Since MitNykredit handles quite sensitive data on people’s financial transactions, certain safety precautions must be taken in regard to the data processing. On that rationale they chose to implement SiteCatalyst as their app tracking tool, since it was already safety approved and in use for their online banking analytics initiatives in the organization. Even though Thomas Clausen describes SiteCatalyst as a rather complex tool, he expresses that the positive aspect of working with SiteCatalyst is that the whole organization is linked up to the tool, and therefore employs system analysts specialized in this particular tool. Perhaps for that reason Thomas Clausen expresses no concerns in regarding the tool set up, or the data validity or reliability of the tool. When asked what type of insights he finds most valuable, Thomas Clausen states that he is solely interested in general tendencies that can be extracted from the tools, such as interaction patterns on usage and popular features. He is exclusively interested in the aggregated data sets and mentions that for him there is no value to gain from being able to analyze data on the individual user. In Nykredit’s case, the rationale behind developing a mobile banking app was to provide banking services on the mobile platform, in order to compete in a market with a growing customer demand. Although Thomas Clausen states that mobile app tracking is an ‘imperative’ element when creating an app, no specific

78

quantifiable targets were set beforehand. SiteCatalyst was chosen for several reasons; first of all the tool meet the security demands the company had set up for their online banking solutions, and in that sense, Nykredit might be said to base their tool selection on the content of the app, as Matzner suggests. Secondly, the tool is used across their digital products and they have the necessary skill and tool expertise in-house.

6.2.3 AO

AO launched their business-to-business app in 2011 with plumbing professionals as their main target group. From our interview with Søren Thingholm, development director and head of marketing at AO, we found that the idea to create an app came as a reaction to some significant changes in the market, commenced by the financial crisis. An increase in the mobility requirements of their customers led to a greater pressure on the call centers in AO, as the costumers now need to check prices and place orders while ‘on the move’. Hence as the customers become more mobile, the demand for a mobile touch point occurred. Besides the goal of moving as many costumers onto the mobile platform as possible there was no official strategy formulation in place for the app. The app development process took a total of five months, and was completed in collaboration between the marketing department as content providers and an external developer, who handled the technical development. According to Søren Thingholm the functionalities were taking from the company website and made into a “light mobile version”. The app therefore functions as a mobile product catalogue that lets the user request orders, locate physical stores and get information on stock inventory, as well as connect with colleagues via a chat feature. According to Søren Thingholm, the ambition was to implement an analytics tool from the beginning. As he wanted to be able to align their app measurements with the existing analytics activities in the organization, he chose to implement Google Analytics. Hence this decision was not based on considerations for content, or relevant features, but rather for system alignment reasons. Before the app launch, a few informal KPIs was set, such as the number of downloads. Concerning the granularity level of the app data, Søren Thingholm states that he is mainly interested in data on the individual level and that data aggregation, in his opinion, decreases the data value and its application. In general he always attempts

79

to get the data as granular as possible and finds that the level of data granularity in Google Analytics does not meet his needs and requirements. Furthermore, as Thomas Jensen, Søren Thingholm equally expresses mistrust in the tool as he has seen examples of the location data being misleading. In AO the rationale behind moving services to the mobile platform had a very specific strategic purpose, namely to release the pressure on their call centers and instead direct the price requests and orders to the mobile app. Being able to track on their mobile activities was an imperative as soon as the app was launched and some simple and informal KPIs was set for the app. However, no specific analytics strategy was developed. Furthermore, the selection of tool was rationalized by alignment and not by the particular app specifications or suitability as the literature suggests.

6.2.4 Summary

Through our analysis we find that our cases generally illustrate a low level of strategy formulation in their analytics activities. In Nykredit and AO, the main drivers behind their app development initiatives have to do with the need to compete in challenging markets. The technological advances have created a growing customer demand for innovative solutions, which the companies need to meet in order to maintain a loyal customer base. In both AO and Føtex, the app initiative is initially based on a change in costumer behavior, which has raised the need for a new customer touch point. The tool selection process in all three companies seems to be guided mainly by a wish to be in compliance with the existing tools in the organization, not by strategic considerations regarding to app content or functionalities as the literature suggests. Finally, the three cases point to similar challenges with the tools. In both Føtex and AO, the respondents expressed clear doubts about the validity of the tools, generally stemming from a lack of transparency in the systems. Nykredit has trained SiteCatalyst ‘super users’ to set up the tool, which might be why Thomas Clausen as the only one does not express any mistrust in the systems. In the first part of this chapter, we have explored the current stage of mobile app analytics from the perspective of the tool industry and our case companies. In the following section, we will describe the results of a thorough feature inspection of

80

each case-app, as well as the three analytics tools used in our case companies. The app review will reveal the types of data input that each app potentially creates while the tool review will outline which types of data outputs can be extracted from each tool.

6.3 App Input and Tool Output Analysis

Since the aim of this analysis is to explore three dimensions of the current stage of mobile app analytics, we will now account for our empirical inquiry of the three apps developed by our case companies and their respective tools. Due to our exploratory research design we use a feature inspection method to map all the input types that the three apps generate and the data points that the tools currently offer. The analysis is followed by a discussion of the potential gaps between the emphasized app data input types and the data that the tools currently provide.

6.3.1 App input types

We have chosen to apply a feature inspection since we see every interaction as a potential data unit that can be tracked by the app analytics tools. The following findings therefore represent potential data points, and not the actual pieces of data that the tools collect. Mapping of all the different input types will enable us to detect how well the tools utilize the data that the apps generate, and thus contribute to the analysis of the current stage of mobile app analytics. From a thorough coding process, a series of patterns have occurred, which led us to group features with similar functionalities and create categories that cover the potential data types that each app presents (Appendix V, VI, VII). These categories and input types will be outlined below.

• Navigation data

• Communication data

• Mobile platform and sensor data

• Location data

• Transaction data

• Personal data

• Login data

81

Navigation data involves navigating from one page to another, typing in searches, browsing, content sorting etc. Communication data is the functionalities within the app that let the user perform communication tasks like emailing, calling, and texts messaging. Connections can be made to other users, to the companies’ customer support or specialists, or to social media sites. Mobile platform and sensor data involves the part of the application that actively makes use of the smartphone specific functionalities within the mobile device. Certain apps demand the use of camera, barcode scanners or the mobile web browser to function properly. Location data includes GPS coordinates on where the user/device is located at the time of use, or proximity to a certain location. Transaction data involves financial transactions, sales orders - and filing systems or other types of larger data exchanges. Personal data includes the data types that a user provides and that can be traced back to an identifiable individual. Login data involves user input such as username and password or user id, along with data on when a user is logging in and out of a session. In Table 1 we have divided all app features for each case company into groups represented by an input category.

82

Table 1: App Input Types As illustrated in Table 1 we find, that all three apps contain a variety of input types corresponding to general navigation, communication options, as well as a series of interaction possibilities with the mobile platform and sensor specifications. Furthermore all three apps entail location capabilities with GPS features, measuring placement and proximity. AO and Nykredit further provide potential transactions data, as well as data regarding personal information and login. The Føtex app, on the other hand, does not contain input options for these last three categories. The three apps thereby utilize a wide variation of the inherent functionalities and sensors of the mobile platform and can thereby create a potentially broad selection of data in the tools. In the following tool inspection, we account for the outputs that the tools create on the basis of the aforementioned input types. The chapter will

83

conclude with a discussion of how the app inputs and tool outputs correspond as well as how they utilize the value propositions identified in chapter five.

6.3.2 App analytics tools

In the industry review we showed how the mobile app analytics scene consists of a variety of different tools, each with their distinct properties and purposes. However, our review of the three case-tools also reveals some common characteristics, why we begin this section with an introduction to these commonalities. Subsequently we will present the findings from our tool inspections of Flurry, SiteCatalyst and Google Analytics respectively (Appendix VIII). Although Flurry is built specifically for app analytics, we find that all three tools are based on a web analytics tradition, which has implications for their architecture and possible outputs. As mentioned in the macro environment analysis, the tool interfaces offer a compilation of graphs and data visualizations, where a selection of metrics can be selected and combined to illustrate the performance measures of interest. Equal for all three tools in this inspection, is that they offer a standard metrics overview usually in form of a dashboard, which provides a selection of visualizations, typically presented at the front page, or another easily accessible place. The three tools all allow for a customization of the dashboards in order to present the graphs most relevant to the analyst. The tools provide the companies with processed data, meaning that the raw data has been object to some variant of automated analysis. The analyst is thus not able to access complete data sets, but merely ‘slices and dices’ of these. From our tools inspection we got an overview of all the available features and related data within each of the tools, as well as a thorough understanding of how the tools function. In the following section we present the results that we find best illustrate what data each case company can derive from their tool today, and accordingly emphasize where there might be gaps between potential app input and current tool output. In the subsequent chapter will we go further into detail with how the companies actually use their data, and how they work with these in their given business areas.

84

Føtex / Flurry: Of the three tools used by our case companies, we find that only Flurry is built particularly with the purpose of analyzing mobile app activities. A large part of Flurry’s services lies outside the traditional data analysis, and concentrate on ‘building an audience’ for the app by advertising for it through the extended Flurry network. Another series of features aims at controlling the companies’ adds and sales campaigns. Our inspection reveals that Føtex, however, does not currently use any of these added services, but solely measure the users’ interaction with the app. The entry section presents a simple collection of statistics for both the iPhone and the Android apps. For more detailed analytics, the analyst selects which one of their apps they wish to see statistics for. In this case, the selection is between viewing stats for either the Android or the iPhone app. It is therefore not possible for the analyst to see a comprehensive picture of all of their app engagements at once. Our analysis further reveals that Føtex has two different tool setups for their Android and iPhone app, with the Android version set up to track more features than the iPhone app. By tracking a number of different ‘events’, Føtex has the opportunity to see which of their services are most frequently used, and follow the users’ paths around the app in order to identify popular browsing patterns and ‘dead ends’. However these metrics are currently only set up for the Android app. Compared to the other tools in this inspection, Flurry offers a quite limited selection of variables and metrics. It is mostly concerned with the amount of users and the usage patterns of the mobile app, and only few variables relate to the specific content of the app. A majority of variables measure the number of new and active users, how often they use the app, the retention rates and other similar features. This can provide a picture of the general interaction patterns, and, by measuring how often the users return, how valuable the user base find the app. Besides usage, Flurry offers insights into technical specifications about the mobile device, such as carrier and firmware version, which is automatically tracked from the app users’ phone. As mentioned earlier, Flurry offers a service to their customers where they generate a persona around a company’s app user group based on big data analysis from data gathered across all of the apps connected to Flurry’s platform. Hereby Flurry makes inferences about demographic information such as age, gender and interests

85

within Føtex’s customer group, by using algorithms on the data gathered across the user-base of all their appertaining apps. The tool furthermore provides an estimate of the demographic profile of Føtex’s users, based on data from apps in a similar category to Føtex. Since the users have entered their personal information such as age and gender in other apps, the probable age and gender division for the audience of Føtex is displayed. Similar to Flurry Personas the estimates are made from data gathered across Flurry’s app portfolio. Flurry also provides a map of the geographic location of the app user base, available on granularity levels of continent, country, or city. According to Flurry’s own information, the geographic location visualization is an indication of where the users are when they are using the app. However, exactly what data source is used to track location is not evident from our inspection. While Flurry seems to give their users detailed and unique information about their user group, it is difficult to disclose how these algorithms are made, on what grounds, using what data, and how the user details are inferred. Hence, while Flurry entails explanations of the metrics and graphs and their meaning, we see a general lack of sufficient information about what lies behind the calculations and what they really measure. Flurry offers a small number of variables and data on how users interact with the app. However, its main features lies in the ability to tap into massive amounts of data on app users and usage in order to achieve big data advantages that can benefit their customers. It is nevertheless difficult to estimate the precision and the grounds on which these personas and demographic inferences are made. Nykredit / SiteCatalyst: Nykredit has chosen to use SiteCatalyst for their app analytics. As mentioned in the industry review this tool is originally made for web analytics, but has later added mobile app tracking to their service. From a top level the tool set-up consists of two separate sections, one for the iPhone app and another for Android, which are the two operating systems that Nykredit’s app is designed for. The analyst can switch between the two continually, as the two holds the same sets of variables and metrics. As were the case with Flurry, it is not

86

currently possible to gather the two data sets, why an analyst will have to combine the data in order to establish a comprehensive image of their entire user group. On the entry page, a dashboard presents an overview of the so-called Key Metrics. The dashboard also includes insights on the pages from which the users exit, and where in the world the app is used. In this case, geography is measured on the country level and the tool does not offer any lower granularity. There is no clear indication as to how the tool measures this specifically, other than “the country from which the app is launched”. The dashboard is custom-made and can thus be seen as an indicator of Nykredit’s main interests. Two other metrics are also included in the dashboard, namely ‘referrer types’ and ‘search keyword’, which corresponds with the navigation input category identified in the previous section. As image 5 illustrates, these last two sections have no available data, which could indicate that the metrics are not app applicable, and that the company uses the same dashboards for their web analytics activities. When searching through all available features, one thing that we find noteworthy is the level of detail when it comes to measuring mobile device specifications such as manufacturer, screen size and color depth, just to mention a few. These are measured in page views, but the user has the option of adding several other metrics, such as ‘visitors’ and ‘time spent’. This gives a rather complex image of the technical features of the mobile phones that the apps is designed for, naturally with a wider variation for Android than for iPhones. Other sections that provide a large amount of data are ‘pages’ and ‘paths’. A detailed visualization of the usage paths is shown, with the options to zoom in on fallouts, page flows and entry and exit pages. This gives strong indications of how users interact with the app, navigate around and to the point where they exit again. The custom-made ‘events’ is another section with data supporting a detailed overview of app usages. An event basically functions as a counter, which tracks the times that an action has been performed. These custom variables cover a large portion of the specific page elements such as account overview, approved payments and transactions. Our inspection shows that there is a custom event created for a majority of their activities, but the way the tool is set up, not all of the metrics provide data. It is worth mentioning that the events measure the amount of

87

incidences, and do not deliver more detail as to what content has actually been provided. This gives a thorough overview of the way users interact with the app, as well as which pages are frequently used, but less insights into the content of actual actions. Hence, the numbers are in focus, as opposed to qualitative content. The tool provides a number of visualizations, generally letting the analyst choose between vertical bars, trended lines and pie charts, depending on which variables are measured. In general, SiteCatalyst offers a detailed view of the technical specifications for the app users’ devices. However, throughout our review it became evident that a large portion of the metrics gives a “no data available” message, when trying to retrieve data from particular areas such as ‘traffic source’, ‘campaigns’ and ‘products’. This might point to the fact that many of the variables included in SiteCatalyst are web related and does not apply to app analytics. Alternatively it could also be a reflection of the current tool set up. Another noticeable thing is that the location data is measured by country and time zone, which the tool supposedly subtracts from the mobiles meta-data, and not from the inbuilt GPS as the app input would indicate. AO / Google Analytics: As mentioned, AO has chosen Google Analytics for their app analytics. The tool is originally a web analytics tool, but has recently added mobile capabilities to their services. Most recently a particular app analytics section has been developed and added as an optional feature. However, AO has not updated their tool to this new type of tracking, why these new features will not be covered in this analysis. Google Analytics is a comprehensive system where companies can hold both their web, mobile and tablet analytics in one tool. As opposed to Flurry and SiteCatalyst, when choosing the mobile analytics section in the tool, both the iPhone and Android versions are included, allowing for a collective view of all mobile users at once. Google Analytics’ origin in web analytics is quite obvious in the menus and the many variables that relates particularly to web traffic and behavior. Sections such as ‘adwords’, ‘traffic’ and ‘conversions’ are areas that are built specifically for web and do thus not apply to the mobile platform as such. Our analysis similarly shows that these three categories do not hold any data.

88

The tool allows access to information about the app user group and their engagement with the app. The users’ location can be tracked down to the granularity of city level, and technical information such as network and mobile device are provided. Furthermore, it can be measured whether users are new or returning, how frequently they use the app and for how long. Additionally Google Analytics’ ‘visitors flow’ feature offers an interactive visualization of the usage patterns in the app where the segment, ‘level of detail’ and ‘point of origin’ can be manipulated in order to discover the users’ path throughout the app from entry to exit. The majority of the variables can be viewed with two disparate layers of metrics; ‘site usage’ and ‘ecommerce’. Site usage views each variable from metrics related to the usage of the app, such as ‘visits’, ‘visit duration’ and ‘bounce rate’, which respectively measures the number of times the app has been used, for how long, and how many times the app was left after a single page visit. The ecommerce layer views the variables from a different set of metrics related to the monetary value of the app, such as ‘revenue’, ‘per visit value’ and ‘transactions’ most relevant for particular m-commerce apps. What we have found to be significant in our analysis is the many options the users have of altering the visual presentations of data. Each main variable has at least two different visualization options, where the data can be viewed as a ‘line chart’ or a so-called ‘motion chart’. Furthermore the analyst can add additional variables and metrics to the graphs allowing for comparative analysis or pattern detection. In turn this allows for analysts to generate custom graphs that subsequently can be added to the dashboard. Another significant feature is that the analysts can observe the users’ activities in real time. In real-time mode it is possible to see how many visitors are actively using the app at the moment, how many pages are viewed per minute, what pages the users are engaged with and where the users are located while using the app. According to Google’s own support page, the data is tracked as events occur and the graphs are updated continuously allowing analysts to monitor effects of new campaigns or app changes. However we find no evidence of the exact latency between when an event occurs and when it is possible to monitor it. The category ‘social’ is another feature that is particular relevant to the communication data input type identified in the app review. This section measures

89

the impact of an app’s social features by tracking which social media buttons visitors click and which pages they share and like. Since AO’s app does not apply any social media functions, this section does not provide any data, though the tool presents the possibility of tracking social interactions and communication channels. Google Analytics has a wide selection of metrics and several visualization options that allow the user many alternative graphs and reports. However, many of the most noticeable features are web-based and cannot be activated for app tracking. Hence, data wise, Google Analytics mainly offers variables connected to app usage and interaction.

6.3.3 Summary

Flurry, SiteCatalyst and Google Analytics each offer a wide variety of features and opportunities for analysis and reporting. Across the three analytics tools we find that the web analytics tradition is still dominant and even an app-based tool such as Flurry has similar architecture and metrics to the other web analytics tools. In the case of Google Analytics and SiteCatalyst many variables are left empty or with signs of inaccurate data, pointing to the fact that the tools are designed for web analytics, and not for tracking mobile apps. We additionally find that the available data across all three tools is limited to a few categories. The tools are generally well suited for measuring app usage, namely how many visits and users, what pages are used and for how long, as well as the specific paths the users take when interacting with the app. Moreover we find that all three tools offer detailed information on the technical specification of the mobile device, carrier, operating system and the like. They also provide some type of geographical location, even though the level of granularity varies, and none of the inspected tools offers locational data that surpass city-level. Flurry offers an additional layer, which sets it apart from the other tools, by offering their customers additional information about user personas and demographic inferences. This is based on big data capabilities, supposedly made possible by their extensive group of costumers. This is a unique feature, although the measurements behind the personas and demographic profiles remain unclear why the validity of the results is difficult to assess. We see a general lack of transparency when it comes to providing details on metrics and measurements. This is particularly true in Flurry’s case, but we see similar

90

tendencies in SiteCatalyst and Google Analytics. We will go further into this discussion in the coming section, where a comprehensive and industry wide discussion will illustrate the current state of mobile app analytics and how well the tools take advantage of the potential value of mobile app data.

6.4 Discussing Data Value and Tool Output

Looking at the mobile data potentials presented in the previous chapter, we know that the mobile platform can be characterized as being personal, interactive, ubiquitous, location aware and multimodal. The following will elaborate on the tool capabilities according to these five characteristics, as well as compare the app input types with the identified tool data outputs. In this discussion we will further include the value proposition derived from the tool industry, and the strategic considerations of our case companies. From the app inspection we have categorized the app input into seven different data types, covering navigation-, communication-, location-, mobile platform and sensor-, transaction-, personal- and login data. Of these data types, all three tools excel when it comes to providing insights and patterns on navigation, hence tracking the users’ interaction with the app. Nevertheless our tool analysis reveals that the remaining six input types are not detectable in the app analytics tools included in this analysis. The metrics thereby focus on the amount of navigation and interaction, and not on the actual content. At this stage the case tools can merely retrieve aggregated data sets. Aggregation in this context means that data units are collected into an unorganized whole, which makes it impossible to distinguish the single elements constituting the mass, why the emphasis is on total counts and average measures. From our literature review we know that the personal attribute of the app as a data source, accounts for an important value proposition. This will allow for holistic analysis and personalization relevant for marketing purposes. From our interviews we find that our case representatives have deviating attitudes towards data aggregation. While Thomas Clausen from Nykredit states that he is only interested in aggregated data on average data usages, Søren Thingholm from AO is mainly interested in obtaining individual data in order to add value to his existing user data. Our app reviews tells us that both Nykredit and AO’s apps require their users to login in order to access its features. From this we can infer that the data from these

91

apps can be connected to one specific person, and thus deliver insights on a personal level. Føtex’ app does not contain any such input types, why the personal aspect becomes less obtainable in their case. From the tools inspection we can deduce that at the current stage, the tools do not fulfill the value proposition of personal data that has otherwise been connected to the mobile area. This presents a gap between app data value and input on one side and actual tool output on the other. Interaction is one of the areas in which the tools have shown to provide a great amount of data. The interaction features are able to give a rather detailed portrait of the overall navigation, and answer questions such as how the app is used, which pages are popular and what paths users take within the app. It can be inferred then, that the tools are very well suited for optimizing the user experience with the apps, as the collective interaction patterns can be tracked quite detailed and thoroughly. This is in accordance with the value propositions for mobile app analytics highlighted by the tools industry voices. In this lie the additional value propositions, that by improving the experience for the app users, the customer engagement and retention, and thus loyalty can be increased. Ubiquity affects the data stream, since an omnipresent device bares the likeliness of providing a more constant flow of data than their desktop counterparts. Furthermore as we identified in the previous chapter, digital data sources can be tracked and stored almost instantly allowing for real-time analysis and timely insights. However our analysis of the three tools shows that they only have limited capabilities when measuring activities as they happen. Google Analytics is the only tool that actually provides live tracking of their users, although the exact latency remains unknown. Though Flurry brands itself as being a real-time analytics tool, we find no such options within the tool, meaning that there might be a deviating understanding as to what ‘real-time’ actually signify. Though SiteCatalyst and Flurry data is updated on a daily basis, their real-time tracking is limited compared to what Google Analytics can provide. Ubiquity also refers to the fact that we tend to carry the mobile device with us at all times - anywhere. When it comes to location, all three tools lack sophisticated tracking services. They all include a demographics section that accounts for user location, but the related data differs from the location input, which for all three apps entails GPS coordinates, proximity to physical locations and volunteered location information such as ‘favorite store’ and ‘postal codes’. The tools do not use

92

the data provided by the users and their app interactions, but seemingly rely on a series of meta-data from the default settings in the mobile phone, such as time zone and language. The level of granularity and the general lack of cross tabulations options make these features rather scarce. The three tools thereby lack the ability to realize what might be the greatest value potentials of the mobile platform, namely the capacity to provide a contextual layer to the data by adding time/space tags to other data types. The multimodal capability that characterizes the smartphone, and thus the app, has been center of a large amount of research attention in the recent years. This is however another area where we see a gap between the tool capabilities and the potential data value. The mobile functionalities and sensor data provided by the apps is not captured in neither of the three tools, which could be due to the ‘silo’ nature of the app, where all the generated data that does not ‘live’ within the app, is inaccessible. From our app inspection we know that the apps can activate several of the in-built sensors. Nevertheless when looking at the input categories from the app review, it becomes evident that the tools only capture a small portion of these data inputs, which presents a gap between tool capabilities and potential app data value to an organization. A comparison between app data value and tool output clearly identifies gaps. Most importantly we find that all data relating to the individual becomes aggregated in the tool presentations, and important value potentials are hereby lost. Furthermore the inherent value potential of location data as providing a significant contextual layer to the data, is not utilized in the tools. We can also identify a gap between the data types outlined in our app review and the tool analysis, where the app analytics tools currently only detect navigation data. This data type, however, can be tracked quite detailed and thoroughly in all three tools, living up to the proclamations of the tool industry. The tools are thus suited to answer questions of ‘how much’, ‘how often’ and ‘with what frequency’, as opposed to details on ‘who’, ‘what’ and ‘where’. The value propositions thereby lie in the ability to improve user experience and thereby enhancing branding associations and customer loyalty. In chapter five we identify particular data value potentials of mobile apps. Among others, these entail a focus on interaction and actual user behavior, which correlates well with the tool capabilities as seen above. The tool industry emphasizes the value of app analytics as its ability to say something about the way users interact with the app. The centralization around the app is additionally seen

93

in their advice on tool selection. The appropriate tool should be chosen according to the app strategy and content, not any organizational conditions such as system alignment or data integration possibilities. The focus is thereby on the usage patterns of the mobile app, and not on how its unique characteristics as a data source can add value.

6.4.1 App analytics tool maturity

As illustrated above we find a substantial gap between the potential value of mobile data and the data types the concrete apps can provide, and the current stage of app analytics tools. From our tool inspection we simultaneously find limitations in relation to the validity of the data that the tools provide, which we will elaborate on in this section. We detect a general lack of transparency when it comes to how the different metrics and variables are measured, a concern shared by our case representatives. The tools currently provide limited definitions of their included variables and metrics, thus it becomes difficult for the analyst to unravel how these measures are calculated. Though Google Analytics, and to some extend SiteCatalyst, provide supplementing reports and background information, the descriptions of the methods behind the measures remains unclear and vague. Flurry is even more tenuous when it comes to providing sufficient information on their measurements. This includes the descriptions of their metrics and variables, but also what data is used to create their measurements. Another issue we find when inspecting the tools is a general tendency to use web terminology. One reason for this can be that Google Analytics and SiteCatalyst were, and still are, primarily web analytics tools, which could also explain why a large portion of the metrics are left empty. The journey from web to mobile does not seem to be complete, also giving an explanation as to why some of the data show inconsistencies when the metrics do not apply to app functionalities. Even within Flurry, which has been dedicated to app analytics from the very beginning, there are still remains from web terminology and most variables are similar to those seen in Google Analytics and SiteCatalyst. Hence we find that the tools included in this analysis are less mature when it comes to utilizing the specific value propositions of mobile app data. It could be argued that the approach of building app functionalities on top of a web-based system is less suitable for extracting value of a data source as diverse as the mobile platform. Furthermore we detect an evident issue with system and variable transparency, which we find to be important when evaluating the quality of the tools. For companies employing the tools this will

94

arguably be equally problematic as the validity, and thus quality, of the data can be questioned.

6.5 Summary

The purpose of this chapter has been to explore three dimensions of the current stage of mobile app analytics namely the tool industry, our case companies’ motives and intentions regarding app analytics and the current capabilities of their three app analytics tools.

Our analysis illustrates that the tool industry professionals emphasize app optimization as the main value proposition for mobile app analytics. The tools thereby fulfill their promise by providing detailed accounts of user interactions, valuable for improving the user experience with the app itself. However we find that the lack of transparency, and the tools’ inability to exploit mobile app specific data types, leaves room for development and improvement, which we will elaborate further on in the concluding chapter. From our interviews we additionally find that at the current stage our case companies lack strategic deliberations in regard to mobile app analytics. In addition the tool selection process has been influenced by the desire for system alignment, and not by considerations regarding app content or functionalities as the literature suggests.

!

95

7 ORGANIZATIONAL DECISION-MAKING

In a competitive market, differences in business performance are often ascribed to those who have information and know how to use it, and those who do not. According to Feldman and Sherman, “timely access to critical information separates the winners from the losers in today’s information economy” (Feldman & Sherman, 2001, p. 1). Consequently, data, information and knowledge have been identified as a company’s most valuable assets and a source of competitive advantage (Feldman & Sherman, 2001). Stemming from the realization that accurate and timely access and application of data can ‘separate the winners from the losers’, a number of research areas have arisen to increase efficient organizational use of data, information and knowledge. While theoretical concepts such as Business Intelligence, Analytics, Information Management and the like are concerned with different perspectives of data and information usages, they also emphasize that using data to improve business performance is not only about having access to the right data. It is equally important how this data is put to use in the organizations, why this will be the particular focus of the current chapter.

In chapter five we characterized the value of mobile app data and its constituting value propositions. The sixth chapter focused primarily on app analytics from a tool industry perspective, whereas this chapter will examine how mobile app data can add value to organizational processes. The aim is thus to answer our third research question of how mobile app analytics is used to support decision-making within the organization.

96

In order to address this question we have divided the present chapter into two main sections; first we will construct a theoretical framework regarding organizational data usage, subsequently we will analyze and discuses our empirical findings, and dive into how our cases make use of the data their tools currently provide. We will further analyze how organizational factors influence their ability to make decisions based on data. This chapter will thus add a third and final perspective on the emerging field of app analytics by exploring how our case companies currently undertake analytics activities. As the analysis will illustrate, the app analytics tools that our case companies currently employ will arguably have an effect on how well-suited the data is for decision support. We will therefore conclude this chapter by discussing the deviating analytics understandings that we encounter might have implications on how mobile app analytics can be used to support organizational decision-making.

7.1 Analytics and Decision-Making

The following paragraphs will construct a theoretical framework regarding organizational data usage and decision-making. We will begin this theoretical outline by defining ‘analytics’, a term that has deviating meanings depending on its context. Where the analytics term that was introduced in the previous chapter stem from the analytics tool industry, this chapter will look at analytics from a broader organizational perspective.

7.1.1 Analytics defined

According to Adam Cooper, co-author of the JICS cetis Analytics Series (2012), the term analytics is often applied without any clarity as to what the term is intended to mean. He further claims that “over-use and band-wagon jumping reduces the specificity of the word” (Cooper, 2012, p. 3), meaning that defining any buzzword can be problematic, as there will always be someone else ‘out there’ with another take of the term (Cooper, 2012).

97

Davenport, Harris and Morrison (2010), the authors behind the book Analytics at Work concerned with creating organizational analytics capabilities, defines analytics in a quite simple manner. Analytics is: data + analysis + decision-making

Hence analytics is not simply the data, nor the data analysis, but the whole process on turning raw data into insights, suitable for supporting fact-based decision-making (Davenport et al., 2010). In this process, data analysis is carried out to gain insights from the collected data, while these insights are used to recommend certain actions, or to guide particular decisions (ibid.). Cooper (2012) chooses to provide us with a description rather than a definition of how he perceives the notion; Analytics is “[…] the process of developing actionable insights through problem definition and the application of statistical models and analysis against existing and/or simulated future data” (Cooper, 2012, p. 3). We find that Cooper’s description has two important components that differentiate it from Davenport et al.’s, namely problem definition and actionable insights. According to Cooper, the analytical process must be based on a defined problem, which should guide the analytical process and assist in finding the best solution to the problem. In his opinion problem definition entails human involvement and critical evaluation, and the analytical process can therefore not be fully automated (ibid.). The other key component of Cooper’s description of analytics is actionable insights, which in his opinion are the ultimate outcome of an analytical process. Actionable insights imply that the value of analytics lies in the decisions and actions that can be taken on the basis of it, and not in the mere reporting. Although Davenport et al. does make a mention of systematic reasoning as part of the analytics process, we find that Cooper describes a more scientific approach to analytics where a problem is defined, a list of alternatives is assessed, and the most appropriate action is decided and initiated. Common for the two definitions is the view on analytics as a process that counts several steps. This includes collecting data, analyzing it, drawing meaningful conclusions suitable for decision support and finally taking action accordingly. While the two definitions outlined above generally describe the same basic

98

elements, we find that Cooper provides us with suggestions as to how this process should be approached. As mentioned we have come to find that the analytics term has deviating meanings, depending on the context in which it is conceived. In the case of mobile app analytics, the analytics term is associated with app analytics tools as shown in the industry review. This field is a direct continuation of web analytics and the more recent social media analytics field, and has a strong emphasis on automated data analysis and visualizations. The other term is the more comprehensive business analytics approach as presented by Davenport et al. and Cooper in the present section. This concept is not focused on any particular data source or tool, but rather on how companies can systematize and utilize their data and thus obtain a more data driven culture. We will elaborate further on this distinction in the concluding section of this chapter.

7.1.2 Data driven decision-making

In the theory of analytics the ultimate objective of organizational data usage is to inform decision-making activities and actions in the company. In the following section we will provide a distinction of decision types varying in the level of human involvement and automation. According to Hambrick and Fredrickson (2001), decisions are actions carried out by firms to realize its goals and objectives. Hence such an understanding of decision-making is able to encompass actions made in the entire company, on all organizational levels, as long as the decisions can be said to work towards realizing the company’s goals and objectives. One key component of making better decisions faster is data. Based on that understanding, data-driven decision-making is an area of increasing scientific and organizational attention. According to Davenport et al. (2010), this evolution of decision-making is inevitable as our society, and hence organizations, become more computerized, data-rich and analytical. In a modified version of a famous Socrates quote, Davenport et al. argue that, “the unexamined decision isn’t worth making” (Davenport et al., 2010, p. 2). Nevertheless research suggest that 40 percent of major decisions are based not on facts, but on the manager’s experience and intuition, which can be a deficient, error-prone and risky process (Davenport et al., 2010).

99

Early information theory such as the information-processing view of organizations, suggest that precise and accurate information can enhance decision-making and thus be linked to higher business performance (Brynjolfsson et al., 2011). Additionally Brynjolfsson et al. show a positive correlation between data-driven decision-making and organizational performance (ibid.). Hence, quantitative evidence exists to claim that effective use of data and information to drive decision-making can create positive effects on company performance. Decision typology: Hambrick and Fredrickson’s definition of decisions mentioned above, embraces all types of decisions made on all company levels. A less comprehensive approach is to divide decisions into categories according to their level of automation and human involvement. Decisions can be more or less automated or dependent on human assistance. The required level of human interference rises alongside with the complexity of the decisions to be made (Davenport et al., 2010). Panian formalizes these principles and describes a decision-tripartition where decisions are characterized as strategic, tactical or operational (Panian, 2007).

Figure 8: Decision Typology. Panian (2007)

Strategic decisions are broad and complex, and often involve unpredictable variables. They most frequently affect the entire organization, and typically involve

100

macro decisions regarding ‘mergers and acquisitions’, entrance into new markets or development of new products. Since these types of decisions involve great risks, they will require a high level of human intervention for evaluating various alternatives and formalize action (ibid.). Tactical decisions are less complex than strategic decisions and are often specific to a particular department or process. For the most parts these can be standardized, but exceptions do occur that require some degree of human judgment. These types of decisions are taken more frequently than strategic decisions, and the human intervention is limited to the exceptions. Operational decisions are those that require no human involvement but can be fully automated. The decision-making procedures are programmed to evaluate the data on the basis of the rules that are set up for the particular area. Operational decisions represent the highest number of decisions made on a day-to-day basis, and the company should thus optimally target for a high degree of automation (Panian, 2007).

7.1.3 Analytics maturity level

In order to use analytics to support decisions, a series of components must be in place. Davenport et al. introduce five of such components in their DELTA framework, which is an acronym that stands for Data, Enterprise, Leadership, Targets and Analyst. These five interrelated elements constitute the main focal points that a company needs to consider, when working towards becoming a data driven organization. Data, being the foundation for any good analytics set up, has to do with the quality of the collected insights, while enterprise refers to the organization’s ability to align their analytics initiatives and develop a data driven culture. Creating a data- and analytics oriented company requires leadership, where the top management must encourage and ensure a transformation of operational processes and decision-making procedures. Targets are the operational goals that need to be in place before an efficient analytics process can begin. Finally the analyst is the key employee, who performs the necessary data processing, typically a job that demands some level of skill - both in regard to statistical knowledge, software and computational expertise, as well as an analytical mindset (Davenport et al., 2010). These five cornerstones are elements that need to be in place before a company can truly succeed at the analytics discipline, although not all steps need to be perfectly implemented before the company can experience value of their analytics initiatives (ibid.).

101

Based on the DELTA framework, Davenport et al. develop a five-stage model to describe companies’ level of analytics orientation, going from ‘analytically impaired’ to ‘analytical competitors’. We will draw on this model when analyzing the current stage of analytics activities within our three case companies, and hereby outline important elements for extracting value from mobile app analytics.

7.1.4 Real-time business intelligence

As a concept the essence of analytics is not new. Business Intelligence (BI) is another widely used business term concerned with transforming data into insights that can be used to make better and more informed decisions. As is the case with analytics, BI does not have one precise unanimous definition. Howard Dresner, one of the pioneers of the field, proposed that BI is an umbrella-term to describe concepts and methods to improve business decision-making by using fact-based support systems (Panian, 2012). Turban et al. (2011) offer another definition of the word, namely that BI is used to help organizations make informed and better decisions by allowing access to the right information, at the right time and place. Real-Time Business Intelligence is a variant of BI that has gained a lot of attention in recent literature. In the current economy it has become an important organizational attribute to be agile, flexible and responsive. In other words, companies must make better decisions faster (Panian, 2007). Real-time BI concerns how companies can measure activities almost instantly as they occur, allowing for rapid and timely actions. In the BI literature, much attention has been devoted to the notion of latency and its implications for the value of data and information in decision-making. Latency can be described as the time lag from an event occurs till the moment where actions can be taken upon it (Panian, 2007). In BI, there can be three types of latency:

• data latency • analysis latency

• decision and action latency

102

In order to reduce latency, the time lag in each process must be reduced. While all BI systems have some degree of latency, the purpose of real-time BI is to minimize the time lag between the event and the initiated action. Real-time BI systems have the potential to decrease all three types of latency by allowing continuous data capture, real-time analytical capabilities and automated decision processes (ibid.). Technically ‘real-time’ means something that happens instantaneously. In a response to the real-time BI literature, concepts such as ‘right-time’ and ‘just-in-time’ BI have been introduced to relax the excessive attention on minimizing latency and instead focus on when the information can add the most value (ibid.). The argument against the notion of real-time is that different business situations and events require different response or action times (ibid.). While the objective of traditional BI practices is to inform and support the organizational decision making processes, the point of reducing latency is to streamline the informed decision making process in order to create more agile and flexible organizations, that are able to respond to their environment when needed. As this section illustrates, analytics and BI have evident similarities. In our research we have found quite a lot of discussions on the differences and similarities between the two concepts. Both Analytics and BI are subject to fuzzy definitions and while some view the terms as two sides of the same coin, others view the concepts as ramifications of each other. From the definitions of BI and analytics it becomes clear why such terminological confusions can occur. BI is based on the transformation of data into information and insights (Turban et al., 2011). As seen in the previous section this is very similar to the analytics process outlined by Cooper and Davenport et al., adding to the confusion is the fact that both concepts are said to be an umbrella term covering the other. We use the term analytics to describe activities involving data collection, analysis and decision making in organizations, since our exploratory literature search shows that this is the term most commonly used in recent writing. Furthermore analytics is the term currently used to describe the processing of data from new digital data sources, such as web analytics and social media analytics, concepts that in many ways displays resemblance to mobile app analytics.

103

To summarize our theoretical framework we have defined analytics as a process of turning data into insights that are suitable for supporting fact-based decision-making, much in line with the definitions of Business Intelligence. We further introduce a typology for three types of organizational decisions distinctive by their level of human involvement and automation; namely strategic, tactical and operational decisions. Real-Time BI presents an emphasis on reducing latency in the process of turning data into insights, whereas we illustrate a tendency towards timeliness and appropriateness of data, rather than ‘as quickly as possible’. This theoretical framework is constructed in order to examine the analytics activities in our case companies. The forthcoming analysis presents our empirical findings, and elaborates on the theoretical aspects when appropriate.

7.2 Mobile App Analytics at Work

This section will add the third and final perspective on the emerging field of app analytics by exploring how our case companies currently undertake analytics activities. We will thus bring the preceding theoretical framework into play and dive into how the overall corporate culture and their daily work processes affect their ability to inform decision based on the data they derive form their analytics tools. We will start our analysis by examining our case companies’ overall organizational culture in relation to their orientation towards data and analysis. This will highlight how fact-based their current work processes are, and hence how mature they are when it comes to data-driven decisions. Subsequently we will analyze how their mobile app analytics activities are carried out and discuss which strategic considerations underpin their current actions. Finally we will determine what types of decisions they can make on the basis of their mobile app analytics set up. The following analysis will be an interplay between empirical results drawn from our qualitative interviews, and discussions of how our theoretical framework applies to our case companies. We will conclude our analysis by determining the maturity level of mobile app analytics from an organization perspective, in accordance with our overall research purpose of identifying value propositions for mobile app data and exploring the maturity level of mobile app analytics.

104

Our analysis will be followed by a discussion of how this broader take on analytics contrast the analytics term currently in use in the analytics tool industry.

7.2.1 The analytical culture

An important constituent element of the DELTA-framework is to break the organizational silos and create an enterprise-wide approach to analytics. A so-called ‘analytical culture’ is one where people throughout an organization demonstrate analytical and fact-based decision-making skills. “Having an analytical culture provides notice that ‘how we do things around here’ includes making decisions on the basis of data, facts, and rigorous analysis” (Davenport et al., 2010, p. 146). In order to ensure an analytics orientation in the organization, the theory points to the importance of having top management invest time and resources into transforming operational processes and decision-making procedures according to data-driven principles (Davenport et al., 2012). Similarly Cropper (2012) points out that organizational buy-in is one of the key elements to any good app analytics strategy, meaning that analysts must gain management support to ensure the necessary resources for analytics activities. In both Nykredit and AO, we find indications of an ‘analytics culture’. According to Thomas Clausen Nykredit as an organization has always been concerned with measuring performance. Data is a large part of their everyday work processes and data visualizations are a widely used way of ‘communicating’ performance measures, especially when reporting to the managerial levels. From our interviews we can deduce that in Nykredit, management encourages a data- and analytics-orientation, as they make frequent use of data analysis themselves, both in their daily work processes and in decision-making. Top management simultaneously tries to diffuse this fact-based approach to the rest of their respective organizations. According to Thomas Clausen normal decision-making practices in Nykredit follow an extensive procedural process where all new initiatives and projects are carefully prioritized against the companies’ strategic objectives. In Nykredit we find evidence of a general organizational interest in the app. The interview revealed that the app has many internal stakeholders since its features combine many products gathered from around the company. Its impact, therefore, reaches outside the department for Digital Media in which the app responsibility is currently placed.

105

AO can also be said to have an analytical culture, in accordance with Davenport’s definition of the term. According to Søren Thingholm, the company’s competitive advantage depends on a high level of knowledge in three areas: products, infrastructure and customers. Søren Thingholm notes that working with data is one of the central functions in their marketing department since, in his words; “marketing is data”. This is also why AO conducts workshops and surveys to gain insights into their target group. We find that the organization has quite complex information systems in place, with the purpose of storing, analyzing and developing their knowledge reservoirs. Prospectively they have several plans for system improvements and integration that can refine their user data by combining data sources in a user data system, with the intent of maximizing the value of their data. In AO the app responsible has a central position referring directly to top management. As head of marketing and development Søren Thingholm is involved with all of the digital communication channels in the company, which gives him close contact with all of the apps internal stakeholders. He points out that the app has received a lot of attention and interest across the organization, including on management level. Having management buy-in and leadership are important factors when it comes to ensuring the funds for analytics projects, as well as in relation to the overall analytics orientation that is needed to fully utilize the analytics to support decisions across the organization. While we find Nykredit and AO to be strongly data driving organizations, Føtex just recently began working data and analytics into their daily tasks, commenced by a change in top management. Their new CEO has set forth several initiatives and allocated resources to steer the organization towards creating a more evidence-based culture. Part of the objective with these initiatives is to rely less on ‘gut feeling’, and more on the insights they can extract from analyzing on their existing data sources. Hence the organization has been subject to some degree of intuition-based decision-making in the past, which has similarly been the case when it was decided which functionalities to include in the app. According to Kristine Salmonsen, “This is one of our goals for the future. We should not just assume that we know what the customers want, but rather focus on what we really know. [...] But this has sort of been the tradition around here”. In Føtex, the app responsibility is placed in the digital marketing department, which in this case only employs two people. Additionally, as mentioned in the previous

106

chapter, the app was developed by an external agency. Due to the organizational culture and the placement of responsibilities the internal stakeholders of the app basically count its product owner, which in this case is Kristine Salmonsen. Without stakeholders, or management buy-in, the analytics initiatives are likely to exist in a silo, which hinders the enterprise-wide approach to analytics. By analyzing the analytics culture, we find that the case companies vary in their analytical orientation. While data analysis and fact-based work processes are an integral part of both Nykredit and AO, Føtex is only taking the first steps toward an analytical culture. In Nykredit, the analytics orientation is supported by a very strong enforcement from the management that permeates their everyday work processes, their communication patterns as well as their decision-making activities. The interview with AO leaves the impression that the strong data-orientation is very much a priority for Søren Thingholm and that he might be the stronghold regarding data and analytics in the company. Furthermore Søren Thingholm states that the app, and analytics thereof, is receiving much support and interest from internal stakeholders in the various subdivisions, as well as from management. In Føtex, the interview illustrates a different picture where data and analytics are only beginning to be an integral part of their work processes.

7.2.2 Resources and skills

One way that organizational buy-in towards analytics is illustrated, is in the allocation of resources for analytics activities (Cropper, 2012). Furthermore Davenport et al. suggest that achieving success with analytics and a data-driven culture requires analytically oriented people often with the skills of a trained analyst. According to the survey performed by Stratigent, a lack of both human and financial resources was among the top-three challenges to mobile analytics (ibid.). As it is illustrated above data and analytics has not been a priority in Føtex in the past, why very few resources are allocated for the measurement upon their digital activities and data processes in general. According to Kristine Salmonsen this is the explanation to why the analytics tool was connected to the app nearly six months after launch. They initially only had funding to complete the app development, why the analytics investment had to be postponed. Furthermore, Kristine Salmonsen notes that given the few resources connected to digital marketing in the organization as a whole, they do not feel that they can prioritize utilizing the analytics tool, nor the data, in a satisfying manner.

107

In Føtex, the analytics activities are centralized in a corporate function, reaching across the entire organization. As a lead system consultant Thomas Nielsen is responsible for all app related data processing, and via his position has the technical expertise that is otherwise missing in the marketing unit. Though the skills exist, the preceding chapter highlighted that the tool is still not properly set up, and the analytics is done on a very basic level. According to Thomas Nielsen, the reason behind the poor set up is that his department is not granted sufficient funds to put in the extra work that a proper set up would demand. In AO Søren Thingholm states that they could use their analytics tools more efficiently than they do today. He claims, however, that they lack the knowledge and skill related to the data, their particular tool and metrics, as well as how these types of analytics activities should be undertaken. As opposed to Føtex this is not a question of financial resources, but rather an issue of a knowledge- and skill barrier. This indicates that analytics activities are given lower priority in favor of other tasks in the marketing department, even though the company in many ways can be said to have an analytics-oriented culture. As mentioned in the previous chapter the general lack of trust in the tools could be another reason why they have not invested the time and resources to enhance their analytics skills. On the other hand Nykredit has allocated resources to train staff and hence have internal ‘super users’ of their web- and app analytics tool SiteCatalyst. The job function that Thomas Clausen possesses does not require any particular analytics skill, but if help is needed, there are trained employees who can assist with support. All three respondents fall under the category Analytical Amateurs, that according to Davenport et al. are “[...] knowledgeable consumers of analytics who can apply analytical insights to their work” (Davenport et al., 2010, p. 96). This type, that normally make up 70 to 80 percent of an organization’s analytical talent, are those employees whose primary job is not analytical work, but who need some understanding of analytics to do their job successfully (ibid.). Only Nykredit has employees that fall into the category Analytical Professionals - employees with statistic skills that allow them to “[...] create advanced analytical applications by developing statistical models and algorithms to be used by others in the organization” (Davenport et al., 2010, p. 93).

108

In summary, we can depict three levels of analytical orientation from the sections above. Nykredit displays a data- and analytics oriented culture where management buy-in, resources and skills are present. AO is also a data-oriented organization that is constantly looking to increase the quality of their data. However, they lack the analytical expertise to augment their current analytics value. Føtex is an example of a company where data and analytics has been down prioritized, not least by the management, resulting in both low levels of resources and skills, although our interview revealed that the company is currently undergoing a transition towards a more data- and analytics oriented culture.

7.2.3 App data reporting

In the following section we will outline how our case companies currently engage in app analytics activities. This entails an examination of the data they collect, how this data is reported, and furthermore for what purpose they gather information. We will thereby be able to pinpoint on what level app analytics is used to support decision-making within our three case companies. As seen in the previous chapter, industry voices suggest that companies engaged in analytics should generate formal KPIs against which the performance of the app should be measured. A similar activity is to set Targets, a constituent of Davenport et al.’s DELTA framework. Targets mean setting objectives for your analytics activities, although they do not necessarily entail quantifiable measures. According to Davenport et al. (2010) setting targets involves finding opportunities or business areas where analytics can make a positive contribution. Hence Targets is somewhat similar to the notion of a problem definition, which according to Copper (2012) is one of the main activities in analytics - an activity that will help achieve actionable insights. We can thereby deduce that the theory unanimously agree that an important part of analytics work is setting goals. In Føtex Thomas Nielsen, who is employed in a centralized department, handles mobile analytics for the entire business concern. He currently gathers statistics on the number of users, and the amount of app sessions, by tracking metrics on visitors, downloads and sessions. The data from Flurry is exported from the tool and benchmarked against the other apps in the organization as well as the activity on social media sites and their website. These insights are gathered in a report, which is sent to the direct stakeholders across the business concern, which mainly constitutes the product owners of the different apps and websites. Hence the report describes the current stage of the different digital products and not their

109

performance against any formal KPIs, other measurable objectives or targets. The reports are kept on the departmental level, and neither seen nor used by management. Kristine Salmonsen states that in the future the marketing department would like to focus more on the use patterns and popularity of the specific app elements. She considers this useful when evaluating which features the users find valuable and which might need a redesign. In that sense Føtex has a target on which they can base their mobile app analytics prospectively. As a basis for decision-making this type of target can be used to support decisions leading to streamlining the app architecture and optimization of the users’ navigational experience. In Nykredit Thomas Clausen creates a set of dashboards with key metrics, which he sends to the different app stakeholders. In Nykredit’s case these constitute the different content providers for the specialized features in the app as well as the vice president of the digital media department. For the most parts, the data is kept within the tool, and dashboards are shared with the interested parties who also have access to SiteCatalyst. Thomas Clausen additionally creates a series of so-called ‘KPI reports’ targeted towards the top management. These reports contain the progression of visitors, page views and visits, over time, and a variety of benchmarks between Android, Apple and the online banking service. This type of reporting is similar to what we find in Føtex, where the measurement includes top line metrics on the average numbers of users and the general amount of usage. The emphasis is on benchmarking, and performance is measured against competing communication touch points and not against targets or KPI’s. In relation to the more practical analytics aspects, Thomas Clausen states that one of his overall targets is to know how the costumers interact with the app. He further explains, that this is also why the tool has been carefully implemented, with a series of custom reports and events, making sure that every navigational activity the user engage in is tracked and stored. More specifically he is interested in the aggregated amount of users. Namely, how the app is used, which elements the users interact with and where in the process they might ‘loose’ their customers. These measurements are currently not carried out in any organized or strategic manner, but rather as local ad hoc analytics, which is currently not included in any reporting. The focus of this type of analytics activity is on navigation, which is one of the areas

110

where we see the tools provide rich and detailed data. We find that the data focus in Nykredit is thus related to the app usages, and not on gaining insights into the behavior of the customers in a broader sense. According to Thomas Clausen, one of the most important targets for him is to generate data that he can use as an “internal political instrument”. He uses the benchmark reports, and the data visualizations that the tools provide, to amplify the need for additional resources to the mobile area. The primary goals with the mobile app analytics reporting is thus to ensure the necessary resources by analyzing aggregated user interactions on an average basis. The decisions that he hopes to be able to influence are thus related to resources and subsequently the possibility of initiating new projects to optimize the app’s functionalities. Similar to the process carried out in Nykredit, we find that AO equally create management reports on measures of general app usages. According to Søren Thingholm, he creates simple dashboards and sends them off as PDFs. He furthermore shares the reports with the top sales manager, who, he says, also has an interest in following the usages of the app. Consistent with the reporting practices in Nykredit and Føtex, the analytics reports in AO cover a series of top-level metrics such as numbers of downloads, logins, active users, visit frequency and retention. Søren Thingholm is mainly interested in which pages and elements their users interact with most frequently. Compared to the other two cases, the reports give more detailed accounts for the app usages, and are less concerned with benchmarks. We find that Søren Thingholm had similar targets as seen in Nykredit, namely using the mobile app analytics as an internal political tool in order to ensure further resources to the mobile department, and simultaneously justify their prior expenses. In addition Søren Thingholm states that he would find much more value in Google Analytics, if it could track the users on an individual level. This way he would be able to integrate the mobile data with their existing data sources and thus refine his repository of user data. But, in his opinion, the current stage of the tool does not allow individual tracking, why he sees the integration options as rather scarce. Cropper suggests another approach to analytics, which involves looking beyond KPIs and let the data ‘speak for itself’. Hence, as an alternative to the scientific approach of a problem definition or predefined target, Cropper suggests exploratory analysis

111

where the analyst looks for interesting or surprising patterns in the data. This is similar to the activity of data discovery suggested by the analytics frontrunner Avinash Kaushik, who is an analyst for Google and the author of two books on analytics. However we find that none of our cases apply this analytics approach to their mobile app analytics. On the contrary we argue that entering analytics activities with the preconceived target of resource allocation will hinder the discovery of new and unforeseen insights (Kaushik, 2010). According to Davenport et al. one of the pitfalls of analytics is to use the data analysis to justify what you want to do, instead of letting the facts guide you to the right answer (Davenport et al., 2010). From our analysis we can infer that all three cases display similar approaches to analytics reporting. Generally the top-line metrics are gathered every one to three months, benchmarked against other digital platforms and shared with interested parties. The number of stakeholders, however, varies. In Føtex, there are very few stakeholders mainly counting the product owner of the app. AO and Nykredit have more stakeholders that, in both companies, count management, various subdivisions and content providers. In the following we shall investigate the notion of data integration, which according to the analytics literature is a prerequisite for achieving the enterprise-wide analytics set up needed to drive fact-based decisions.

7.2.4 Data integration

In the survey by Stratigent the respondents reported that a barrier to effective mobile analytics is that the data is siloed within the tools, making it difficult to integrate it with other data sources (Cropper, 2012). In business intelligence, as well as in analytics initiatives, the question of alignment and data integration will often arise as a variety of data sources, tools, and systems have to play together. According to Davenport et al. (2010) data integration involves aggregation of data from multiple sources and making sure that the company is aligned when it comes to data processes. One of the main benefits is operating with ‘one version of the truth’ and thus to ensure that everyone is ‘speaking in the same language’ when it comes to making critical decisions (Davenport et al., 2010). Cropper similarly states that, in order to be able to answer high-level questions, it becomes central to pull different sources together. She finds that an organization can benefit substantially by bridging the data gap between their online, offline and

112

mobile users, which can be achieved by pushing data to a centralized data warehouse or business intelligence system. In practice, this means that a user is identified through user ID or name, this indicator is matched across various platforms, while the system ‘ties’ the data together creating a more detailed and robust image of that customer (Cropper, 2012). As illustrated above, the theory strongly suggests that data sources and analytics activities should not be siloed. However we find that the current mobile app analytics tools are highly siloed, since they do not provide its users with the opportunity to integrate data with other tools or a data warehouse system. This siloed nature of the tools might hinder the integration activities in our three case companies. In the previous chapter we additionally illustrated that our case companies select their mobile app analytics tool on the basis of their existing analytics platforms. Hence one of the main reasons behind selecting a tool is system alignment. Despite this rationale only AO mentions the lack of integration capabilities as decreasing the value potential of the analytics tools. One of Søren Thingholm’s strategic targets concerning his data- and analytics activities is to refine their user data as much as possible. Since the tool does not allow the mobile app data to be integrated directly into another system, the transaction data from their app users’ orders is currently tracked by a different system that integrates it with other data sources. We will elaborate further on the data integration issues in the discussion, which concludes this chapter.

7.2.5 Decision levels and latency

In this section we will examine what types of decisions can be made on the basis of the analytics activities that we have seen in our case companies. In the decision typology framework presented above we see how decisions are actions carried out by firms to realize their goals and objectives. Furthermore decisions can be strategic, tactical or operational, each requiring a different level of automation and human involvement. Hence data and analytics play different roles in each decision type, but the theory prescribes that all decision processes should be based on some level of facts. Our analysis shows that at the current stage of mobile app analytics, decisions on the strategic level cannot be made solely on the basis of mobile app analytics.

113

Cropper suggests that the mobile platform as a data source cannot stand by itself when it comes to high-level decision-making. From our interviews we can draw the same conclusion, as the metrics and reports disseminated to the managerial levels lack the complexity needed to inform strategic decisions. Furthermore our analysis reveals a data validity issue, since the tools present limited explanations as to what they actually measure. This decreases the trustworthiness of the data, and thereby its appropriateness as a foundation for strategic decision-making. We do not find that the operational decisions can be taken based on mobile app in the current app analytics landscape. Even though the data can be collected and analyzed in real time, the tools lack the technological capabilities needed to serve as an automated decision system. With the current functionalities of the app analytics tools examined in this thesis, they do not allow an integrated continuous data feed that is needed in order to automate decisions. However our analysis suggest that app analytics allow for tactical decisions since this can be said to be lower-level decisions that require some human involvement and is not based on automation. The interviews reveal that the case companies currently use their mobile app analytics for app optimization purposes, and as an internal political tool used to allocate budgets. This suggests that the types of decisions that can be made on the basis of mobile app analytics has to do with the app itself and the local department in which it is placed. Even though mobile app analytics cannot function as an automated decision system, the mobile app as a data source and the tool capabilities allow decision makers to reduce latency in several areas. As mentioned, the data-driven decision-making process entails three types of latency; data, analysis and decision latency. Both the data source and the analytics tools have the capability of capturing data, analyzing it, and delivering results in more or less real time, and thus reduce data- and analysis latency. Decision latency, however, cannot necessarily be reduced since the types of decisions that can be made require some human involvement. This is further cemented by the fact that the tools are not capable of providing data feeds into a central decision support system in their current stage. Our empirical analysis illustrate that a real-time decision process is further prevented by the time lag between an event can be measured by the tool until the report is shared with the relevant stakeholders. In the three companies app analytics data is reported monthly or quarterly, which naturally decreases the

114

timeliness of the data. In the following section we will conclude our empirical analysis by placing our three case companies in an analytics maturity framework.

7.2.6 App analytics maturity level

Throughout this chapter we have investigated how our case companies use mobile app analytics for decision support and what the theory prescribes such a process to entail. The following section will discuss our findings by placing the three case companies on the Analytics Maturity latter that Davenport et al. (2010) have developed. Since this framework contains many dimensions, we find that each company can be classified as several stages, depending on the target of the measurement.

Figure 9: Five-stage model of analytics progress. Davenport et al. (2010)

Føtex has recently taken its first steps towards a more data- and analytics oriented culture, which according to Davenport et al. places them as stage one: the ‘analytically impaired’. The previous lack of resources and prioritization devoted for analytics activities, and the organizations’ general view on data and analytics,

115

means that Føtex still have a long way to go before they can call themselves a data driven organization. For instance the analytics tool was not implemented until six months after the app launch, and even now a year after, the poor quality tracking set up means that they have little viable data to analyze. Furthermore the app and its performance measures have very few stakeholders within the company. This observation is supported by the fact that the app owners across the business concern are the only ones who see the final analytics reports. Hence the app analytics activities has received very little managerial interest, although this tendency seems to be changing as their new CEO is more data oriented than has previously been the case. Finally the interview revealed that the mobile app analytics reports are currently not used as a basis of any types of decisions, although Kristine Salmonsen expresses an ambition to make decisions regarding the design of the app and thus optimize the user’s experience when the tracking set up is completed. The central analytics department where Thomas Nielsen is employed can be said to be a ‘pockets of analytical activity within the organization’, which is one on the characteristics of the next level of analytics orientation. Nevertheless the prerequisite for good analytical work - data quality - is not in place, why this must be a central priority if they want to achieve more with their analytics activities. From a pure organizational perspective Nykredit displays conditions that place them on the fourth stage as an ‘analytical company’. The overall orientation is highly data-driven, both in regard to the daily work processes and in decision-making. We will argue that Nykredit on most levels follow the constituent of Davenport’s DELTA framework. They have access to data, an enterprise wide analytics-orientation, the leadership and management buy-in. The company possesses the required skills to perform advanced analytics, and the app and its performance measures have many internal stakeholders, counting both content providers and managers. Yet, in regards to their current mobile app analytics, its application is very limited, and the targets are quite simple and only focused on app optimization and budget allocation. On this parameter they resemble the stage defined by having ‘localized analytics’, which means that they have some analytics set up in place, but it is not particularly coordinated or focused on strategy. Hence Nykredit has the potential to make fast progress towards becoming mobile app ‘analytical competitors’ as the basic constituents are in place. The main hindrance is that their ambitions with app analytics do not reach beyond the app itself and the department in which it is placed, and the decisions will therefore be tactical, rather than strategic or automated.

116

AO too falls within several categories dependent on the parameter, but mostly qualify as ‘analytical aspirations’, which place them somewhere in the middle of the spectra. The company puts high value in data in general and in particular data about their customers. They can thus be said to have a data oriented culture, and as with Nykredit, they have many of the prerequisites in place as described in the DELTA framework. They have access to data and are constantly looking to refine its quality. Furthermore the app has many stakeholders within the company, not least the management that supports their analytics activities. They currently have similar targets as Nykredit in terms of app optimization and budget allocation. Yet AO sees far more opportunities in the mobile platform as a data source, than both Nykredit and Føtex. Søren Thingholm reports that for him, mobile app data can become a highly valuable source if the tools offer a higher data granularity level than is the case today. Hence he envisions an integrated system where the user can be individually identified and where the mobile platform can add value by its unique features such as location awareness. However AO currently lack the knowledge about analytics work and the skills to perform advanced analytics on their data.

7.3 Discussing App Analytics

As the preceding analysis has shown, the app analytics tools that our case companies currently employ have an effect on how well suited the data is for decision support. As mentioned our research reveals two deviating understandings of analytics; one associated with the tool industry, another concerned with the organizational process of turning data into actionable insights. Before we are able to fully answer our third and final research question, we find it necessary to provide a discussion of how these two concepts may each have implications for how mobile app analytics can be used to support organizational decision-making. Our analysis shows that the analytics terms can be associated with app analytics tools as shown in chapter six. This field is a direct continuation of web analytics and the more recent social media analytics field and has a strong emphasis on automated data analysis and visualizations. The other analytics understanding is associated with the more comprehensive business analytics approach, which has been the focus of this present chapter. This concept is not focused on any particular data source nor tool functionality, but rather on how companies can systematize and utilize their data in a more efficient and strategic manner. As mentioned this entails data collection, analysis and the processes involved with using insights to guide decision-making and recommend action.

117

Since the overall purpose of this study is to determine the maturity level of the emerging field of mobile app analytics, we find it important with a discussion of how these two understandings vary in their value propositions and in the decisions they are able to inform. The following sections includes a discussion of how the two terms refers to the skill and resources needed to perform analytics activities, how tool and system integration is regarded, and finally which levels of decisions each approach is suited for.

7.3.1 Analytics resources

In the two definitions of analytics there are deviating demands for skills and resources required to extract value from a data source. In the tool orientation a main value proposition for analytics is that it requires few resources and a limited amount of specialized skill. This is illustrated in the way the tool providers market themselves as ‘plug-and-play’ solutions. Oppositely the broader business analytics orientation emphasizes the allocation of resources, management priority and specialized skill as key components of analytics activities. This means that a broader segment can utilize the tools, while business analytics require expertise. This clearly illustrates a difference between the value propositions of the two understandings. The popularization of analytics tools creates a democratization of data analysis (boyd and Crawford, 2011). We find that this means that many types of companies, departments and employees can engage in analytics and thereby begin to extract valuable insights from their data sources. As we illustrate in our cases, analytics activities that used to be a domain exclusive to data scientists and experts, is now the responsibility of ‘analytical amateurs’ with varying skills and data understandings. This democratization tendency contrasts the data- and analysis divide that boyd and Crawford (2011) have identified as one of the downsides to the big data hype since a larger number of people, in principle, can engage in analytics. According to boyd & Crawford (2011) the fast and easy access to analytics tools means that this type of data collection and analysis will be approached by a lack of critical appraisal, since the analysts do not possess the skills to evaluate the data quality and validity. In essence the tools provide the analytical capabilities to those without the otherwise required analyst skill. As a downside to the fast and easy access, we find that the tools on the market present limited options for metrics and measurement appraisal mainly due to a lack of information regarding how metrics are essentially measured.

118

With the focus on skill and resources that the broader business analytics orientation recommends, we raise the question of whether this type of analytics is equally feasible for all types of companies. In this regard we mainly see the company size as a compromising factor when it comes to obtaining sufficient skill and resources needed to implement an enterprise-wide analytics set up. The analytics literature suggests that when informing decisions an organization can benefit substantially from bridging the gap between their online, offline and mobile activities, which can be achieved by pushing data to a centralized data warehouse or business intelligence tool. While Davenport advocates company-wide analytics alignment we find it questionable whether this large-scale approach will be viable for small and medium sized enterprises alike. Data warehousing and specially trained analytics experts are a costly and demanding affair, and we will thus argue that this analytics orientation is more appropriate for large corporations. On the other hand we find that the analytics approach applied by the tool industry is concerned with easy access, and demands a limited amount of resources and skill, perhaps more suitable for smaller businesses with limited human and financial resources. As mentioned in the introduction to this chapter, there is an identified performance gap between those who have access to data and knows how to use it, and those to do not. If this hypothesis holds true, the data- and analysis divide can be said to be retained as larger companies, with greater access to resources, will be able to engage in deeper and more sophisticated levels of analytics.

7.3.2 Tool and system integration

Data integration is another area where the current app analytics tools present a hindrance towards achieving the value propositions of the broader analytics understanding. The literature strongly advocates data integration as an important activity in business analytics, particularly when it comes to informing strategic decisions. As our analysis shows, the analytics tools place constrains on the companies’ ability to base decisions on their current analytics set up. The mobile app analytics tools reviewed in this thesis are highly siloed, and neither of them provides their users the opportunity to integrate their app data with other systems or data warehouses.

119

As we have discussed, the current stage of app analytics tools additionally present data validation issues, an inability to fulfill the complete app data potential, and further limits the company’s ability to integrate data. The question is thus whether the companies would gain more value by connecting and integrating the mobile app data with other systems such as a CRM or BI system. As we have seen in our analysis, Søren Thingholm from AO finds that the lack of integration capabilities decreases the value potential of the analytics tools. The overall strategic target concerning the data- and analytics activities is to refine their user data as much as possible. As mentioned above, Google Analytics does not allow the mobile app data to be integrated directly into another system, why the transaction data from AO’s app users’ orders is currently tracked by a different system that integrates it with their other sales channels. Kristine Salmonsen from Føtex has equally had valuable experiences with surpassing the tools and pulling data directly into their content management system. By analyzing search logs Føtex has been able to create an intelligent search function and thesaurus. AO correspondingly uses the logs of failed searches in order to create a redirect function for future searches with typical misspellings or other common errors. Though neither of these examples increases their ability to inform decisions on a strategic level, our cases display examples of obtaining actionable insights by collecting app data in other systems than their associated analytics tools.

7.3.3 Decision levels

Through our analysis we find similarities in the value propositions derived from the tool industry and the way in which our case companies perceive the app analytics value. In chapter six we illustrated that the industry and tool providers emphasized the value of mobile app analytics, as its ability to say something about the way a collective group of users interact with the app. As the preceding analysis has demonstrated, this is similarly the prevalent orientation towards app analytics in our cases, where all three companies have app optimization as a primary target. The app analytics activities happen in the departments that are responsible for the app development, and the data therefore resides within its local environment. Hence the decisions that are currently taken on the basis of mobile app analytics happen on a very local scale and mainly concern the app itself or the department in

120

which the app responsibility is placed. The broader strategic orientation of app analytics is only mentioned as a future possibility in AO, but not applied in neither of our case companies. Furthermore the focus on the collective group of users means that the tools do not allow for analysis on individual behavior, but rather present aggregated data pools. Avinash Kaushik strongly states that, for him, data loses its true value if it is only treated in aggregates. On his blog Occam’s Razor, Kaushik puts it very straight-forward: “all data in aggregate is essentially crap" (Kaushik, 2010), he writes. He points out that aggregated data is well suited for reporting activities, but can be problematic when trying to account for actual user behavior, since behavior is individual (ibid.). In his opinion the emphasis should rather be on the analysts’ ability to segment users into smaller groups, with particular similarities, which will allow marketers to target specific customer segments, instead of treating all users as one unity (ibid.). In this relation the current stage of the tools present a disadvantage as they hinder the use of app data for personalization purposes or other marketing activities facilitated by personal and individual data analysis. We find that one of the opportunities for both the mobile app as a data source and mobile app analytics, is that insights can be provided in a timely fashion due to reduced data and analysis latency. However, in our cases the approach to data reporting is to make a monthly or quarterly report to management with key benchmarking metrics. Furthermore the analytics tools can currently not be integrated with other information systems, for instance allowing automated decisions based on app data. Hence decision latency is still an issue and a real-time analytics system is thereby hindered. The reporting practices in our case companies additionally hinders just-in-time analytics where the focus is on timely insights, meaning that the data is provided when it adds the most value. In summary our discussion finds that the two deviating orientations towards app analytics in some ways work against each other. Specifically, we find that the current app analytics tools present a hindrance towards achieving the value propositions of the broader analytics understanding. In the analytics understanding, that has been the focus of this chapter, we find that data integration is a key feature especially important in relation to strategic decision support. The fact that the tools do not offer any integration features makes it less suitable for supporting decisions of a strategic nature. We find that the decisions, that our case companies are currently involved with, happen on a local and tactical

121

level and are mostly centered on app design optimization. This understanding of analytics corresponds more with the value creation that the tool industry prescribes, rather than the more profound analytics approach identified in the analytics theory.

122

8 CONCLUSION

The purpose of this thesis has been to explore the emerging field of mobile app analytics, establish its potentials and maturity level, and thereby fill a void in the academic literature. The novelty of this field has called for an exploratory and descriptive approach, which has led us to focus on three interrelated elements – the mobile app as a data source, the analytics tools needed to turn the data into insights, and finally the organizational aspect of using actionable insights to inform decision-making. Given the complexity of this new and unexplored field our study has undertaken a combination of methodological inquiries. Since mobile app analytics happen in an organizational context, we have chosen a multiple case study approach that involves three Danish companies in three very different industries. All three companies have developed an app to support their existing business areas, and recently initiated tracking by implementing an analytics tool. The case study approach involves in-depth interviews and thorough feature inspections of the apps and tools. In addition to the empirical analysis a comprehensive literature review has enabled us to build a body of knowledge for mobile app analytics by combining an array of literary sources. Since this topic has not previously been covered academically, our literature review consists of both scholarly sources as well as influential professionals from the analytics industry. In line with the three perspectives on mobile app analytics this thesis has been divided into three main parts, allowing us to approach the topic from these three different angles guided by our research questions:

123



3. How are mobile app analytics used to support organizational decision-

making?

This chapter will conclude on the findings from our three research questions and subsequently determine the maturity level of the field while emphasizing the unfulfilled potentials that call for further development and investigation.

8.1 Characteristics and Value Propositions of Mobile App Data

As the first part of a trinity aimed to uncover the emerging field of mobile app analytics, we have explored the medium from which the data is collected. Through a literature analysis and theoretical discussions we have illustrated the unique features of mobile app data and the value propositions that can be ascribed to it. We find that the mobile platform is characterized by being personal, interactive, ubiquitous, location aware and multimodal. We argue that these characteristics make it a unique platform, capable of providing a series of different data types. Since smartphones are equipped with a multitude of sensors, they are increasingly becoming capable of sensing their surroundings. Apps have the proficiency of accessing these hardware capabilities, which adds a contextual layer to the data they provide. Our analysis shows that the spatial awareness gained by using location-data based on GPS coordinates adds a time/space dimension to the data, which we find to be the most profound potential of the mobile app and thus truly unique to the app as a data source. Though the ubiquitous and interactive characteristics of the app affect the data streams, they are less essential in regard to the specific data content. From the examples seen in research communities we find that data from the mobile platform is often applied as a mean to measure human behavior, an activity which has been prone to many challenges in the past, due to the lack of scalability and biased results. Furthermore, the interactivity connected to the digital realm and the nature of apps lead us to conclude that the app is a platform highly suitable of portraying interactions and behaviors rather than content, communication and sentiments.

124

By applying theory on personal data and big data to the mobile realm, we illustrate that app data is personal in nature and corresponds with the big data indicators of variety and velocity. Furthermore, we argue that volume is an ambiguous indicator that in the right context can be ascribed to mobile app data as well. We thereby show that the data versatility along with the velocity that is inherent in digital data sources enables holistic analysis, the creation of timely and actionable insights, and facilitation of both aggregated and individual data analysis. The personal nature of the app data enables personalization, which can take place on an individual level and in large scales. We find that individual data analysis allows for precise tailoring and unique customization, which makes it particular valuable for marketing, as well as customer relationship management. Aggregation, on the other hand, allow for the detection of patterns and collective behavior among a large group of people.

8.2 Current Stage of Mobile App Analytics

We find that analytics tools play a significant role for mobile app analytics, since they present an intermediary between the value of the data source on one side, and the potentials of organizational data usages on the other. The aim of our second research question has thus been to assess the current stage of mobile app analytics from a tool perspective. Answering this question entails an analysis of the current app analytics tool industry, the analytics processes that our case companies represents, and a comparison between the data that the apps capture and the data that the tools are currently able to provide. From our tools inspection we find that Flurry, SiteCatalyst and Google Analytics all offer a variety of automated data analysis options. None of the tools provide access to raw data, but facilitates a series of reporting features and visualizations. We find that the tools generally excel at measuring app usage, in addition to detailed information on the technical specification of the mobile device carrier and operating system. Flurry ads a supplementary layer by offering their customers additional information about user personas and demographic inferences. This is based on big data capabilities, supposedly made possible by their extensive costumer network. These are unique features, although the measurements behind the personas and demographic profiling remain unclear.

Our comparison between the value propositions of app data and the data that the tools can provide, presents major gaps. We find that all data relating to the

125

individual becomes aggregated in the tool presentations, and the value potential of personal data is hereby lost. Furthermore the inherent value potential of location data is not fully utilized in the tools, since the sensor input from the GPS is not tracked by any of the tools. They all offer geographical measures, but the analysis is based on meta-data from the mobile device and the granularity does not surpass city-level. We further identify gaps between the data inputs outlined in our app review, and the data output of our tool inspections. Of the seven categories we find characterize the app input, only navigation is detectable by the app analytics tools, hence providing insights and patterns on the users’ interaction with the app on a collective level. Common for these is that they track the amount of navigation, and not the specific content. These data types, however, can be tracked quite detailed and thoroughly in all three tools, living up to the value propositions that we have derived from our tool industry analysis. We can therefore conclude that the tools are suited to answer questions of ‘how much’, ‘how often’ and ‘with what frequency’, as opposed to details on ‘who’, ‘what’ and ‘where’.

We have detected a general lack of strategic considerations in relation to analytics work within our three case companies, which oppose the analytics best practices identified in our analysis. Our case representatives did not have any strategic goals for what they wish to achieve with their app analytics initiative and their tool selection process was characterized by system alignment rather than a consideration for specific features or the content of the app.

Finally we detect a general lack of transparency when it comes to how the different metrics and variables are measured. The tools offer limited information and explanations as to what the metrics and variables measure and we find that our respondents therefore lack trust in the tools and related data. Through our tool analysis we have encountered a general tendency to use web terminology. The journey from web to mobile does not seem to be complete, also providing an explanation as to why some of the data shows inconsistencies when the measures do not apply to app functionalities. We can hereby conclude that the tools included in this study are useful to optimize the app design and functionalities to improve the user experience.

126

However they are less mature when it comes to utilizing the full potential of mobile app data that we have identified through our study. Furthermore, we detect an evident issue with system and measurement transparency, which we find impair the possibility to assess the validity of the app analytics results.

8.3 Mobile App Analytics as the Foundation for Decision-Making

We cast light on the third and final perspective on mobile app analytics by exploring the concept in its organizational context. While the previous question has been concerned with mobile app analytics tools, the aim of our third research question is to answer how mobile app analytics can be used to drive decision-making within an organization. We define analytics as the process of turning raw data into insights that can be used to guide decision-making. If such processes permeate an entire company, it can be said to have a data- and analytics-oriented culture. The theory prescribes that an efficient analytics process is focused on creating timely and actionable insights, where the terms timely and actionable emphasize that insights must be delivered at the right time and place. Hence we find that the value of analytics lies in the decisions and actions that can be taken upon it, and not in mere reporting. Through our analysis we find that our case companies currently use mobile app analytics to extract simple top-line metrics. They benchmark against other digital products in their respective organizations and distribute reports to stakeholders and management on a monthly or quarterly basis. We find that in neither of our case companies were the analytics process oriented towards any predefined strategic objectives, KPIs or guided by problem definition. Neither did we find any evidence of exploratory data analysis which is an activity prescribed by the literature useful for revealing unexpected findings. We can thereby conclude that the case companies’ approach to mobile app analytics are characterized by reporting activities, not how it can guide decision-making. Furthermore, the sequential reporting approach prevents timely insights as the focus is on report frequency rather than report relevancy. Through the analysis of our qualitative interviews we show that the companies in general share two goals for their mobile app analytics. The first is to use their

127

mobile app data to optimize the functionality, architecture and design of the app and hereby improve user experience. The other is to use the app reports as an internal political tool to ensure continuous flow of resources to the department engaged with mobile app development. Hence our analysis show a local decisional affiliation, meaning that mobile app analytics are used to guide decisions that is concerned with the app itself or the department in which it is placed. Throughout this study we operate with two distinct analytics orientations, one associated with analytics tools, another related to the business process of using data to support decision-making and guide actions. We argue that the two deviating orientations towards app analytics in some ways work against each other. Specifically, we can conclude that the current app analytics tools present a hindrance towards achieving the value propositions of the broader analytics understanding. This is particularly true in terms of skills and resources as well as integration capabilities. Our analysis show that mobile app analytics allow for decisions on the tactical level, since these are lower-level decisions that require some human involvement and are not based on automation. We find that decision-making on a strategic level is hindered by the lack of complexity and reliability of the data extracted from the tools. Furthermore, the data cannot currently be integrated into a central automated decision system, which is a prerequisite for operational decision-making. We can hereby conclude that for mobile app analytics to be used to support decision making on all levels, it should be integrated, either with other data sources to increase its complexity, or with other systems to allow for automation.

8.4 The Maturity Level of Mobile App Analytics

This thesis has served to provide an exploration of the emerging field of mobile app analytics. By identifying potentials of app data and highlighting industry and scholarly advice, we have determined the maturity level of mobile app analytics in our particular cases and in the tool industry. On the basis of our conclusions outlined above, we can construct a matrix that illustrates the current stage of mobile app analytics against its unattained potentials and hereby determine its maturity level. Our maturity matrix is inspired by constituents of the Web

128

Analytics Maturity Framework (Hamel, 2009) and modified into a mobile app analytics context.

Table 2: Mobile App Analytics Maturity Metrix

The model accentuates that both in regards to our case companies and in the tool industry, there is room for developments and improvements. This is to be seen in relation to the recommendations derived from the analytics literature, and in regard to utilizing the potential values that we have identified for the app as a data source. While such a maturity level can be expected of a novel field, we believe that a main barrier to progress lies in perception. In order for organizations to maximize the outcome of their mobile app analytics activities, both analysts and industry players must broaden their horizon to embrace value propositions particular to the mobile app.

129

9 LIMITATIONS

The purpose of this section is to delineate the general limitations of our study. This involves a discussion of some of the inherent weaknesses of our research design and the applied data collection methods, as well as the experiences that we have encountered during this research process. Our aim has been to uncover a novel field where limited amounts of prior academic writing exist. This presents a limitation in the sense that we did not have a very strong academic literary foundation to build on, nor a research tradition to lean against. In our literature review this became evident in the lack of scholarly articles and books written on the topic. We have hence had to supplement our research with less scientifically validated online sources, such as blogs and magazines, but also biased sales material and white papers written by the tool providers themselves. While these serve to provide an overview of what is on the ‘new tech’ agenda, they have implications for our ability to judge the quality of the available tools and subsequently prove the current stage of app analytics. For this reason we have had to be particularly thoughtful and critical in our evaluations of the available sources, taking these existing biases into account. An issue related to the novelty of the field has also been apparent in our interviews. Our interviewees had little prior knowledge about analytics, and mobile app data in general, which meant that they have limited expectations as to what insights they can gain from their analytics tools. In the case of Føtex this turned out to present a limitation, since we could deduce that our respondents

130

were unfamiliar with some of the terms and concepts introduced during the interview, which could be the reason why their answers, to some degree, seemed to lack proper consideration. Since the app responsible served as our key informants, we were further reliant on their knowledge and insights into the organization. Through the interview with Føtex we found that the respondents had difficulties answering some of the questions regarding data usages, as they had limited knowledge as to how the organization was structured. This was not the case with Nykredit and AO, why the three interviews varied in quality, which could present implications for our findings. We have accommodated this challenge by seeking additional answers after the interviews with Føtex, giving the respondents more time to gather the needed insights. The feature inspection is a method usually employed in the usability field, which we have adapted to fit our research purpose. This is thus not a well-documented method for this purpose, why it could limit the quality of our results in regard to methodological soundness. In the tool inspections it additionally turned out to be problematic to isolate tool functionalities, due to the individual conditions regarding tool set up and customization. Whether the missing data sections were due to lack of tool capability, or a result of the way the tool had been set up, was difficult to determine with this method. However, scrutinizing the tracking set-up code for each app has been beyond the scope of this paper, why this is a potential source of uncertainty inherent in our results. Another area that we find could present a limitation is our approach to case company selection. As described in the methodology chapter, we have chosen three companies that possessed some degree of similarity in relation to both the size of company and the type of app. On the other hand the basis of the selection has also been guided by which companies were able to give us the proper access and devote the appropriate amount of resources for us to be able to complete our empirical research, which made the final selection less deliberate than the theory would prescribe. At the time of selection we still had a series of ‘unknowns’, such as how the companies had set up their tools, the actual capabilities of these tools, and to what degree they were currently working with data in the organization. Retrospectively we find that we had a rather slim basis for case decision-making, why the companies have also proved to deviate more from one another than first anticipated.

131

10 PERSPECTIVE AND FUTURE WORK

Since our thesis has been an exploration of mobile app analytics and its constituting elements, this section will provide a prospective outlook of its possibilities, challenges and future research perspectives. As we have illustrated throughout this paper, mobile app analytics is an emerging field with little academic research affiliated. Consequently our explorations, empirical enquiries and discussions present the groundwork needed before other types of studies can be carried out. Our empirical foundation is based on companies that operate in very different sectors and industries. Throughout our project we have become familiar with major organizational and structural differences, which arguably can have an impact on how mobile app analytics is applied and appraised. Further studies could thus take its point of departure in this hypothesis and focus a study on specific sectors and their appliance of mobile app analytics. One question that might be worthy of some attention in this regard, is whether mobile app analytics is equally valuable for all types of companies, and whether the business environment has an effect on how valuable the app data becomes. Similar studies can be done with companies of different sizes; for instance examining how mobile app analytics can be valuable for small and medium-sized enterprises, or for companies with different types of apps. Such studies will most likely be able to illustrate whether mobile app analytics is particularly valuable to certain types of companies, and outline how different companies can maximize the value of their mobile app analytics initiatives.

132

Since our case companies only base basic tactical decisions on their app analytics reports, further investigation of how mobile app data is used in decision-making is needed. Therefore we find it relevant for a future research project to ‘zoom in’ on the decision making processes, and examine specifically how various data types can be used to guide decisions and actions in an organizational context. This means following the data reports from entry point, through all its particular touch points, until the insights are reviewed by decision makers and possibly used to support decisions. If we want to fully understand the mobile app data’s role in such a process, it can be argued that the study object should be an organization with a higher mobile app analytics maturity level than we have found in our three case companies and their respective tools today. Due to the novelty of the field we have chosen to undertake a study of breadth in order to increase the likelihood of bringing forth as many relevant elements of the notion as possible. The multi-case research approach, however, means that we cannot exhaust every relevant topic in the respective cases. A single-case approach could be particularly useful to deepen the scope and understand the connection between constituent elements brought forth in this thesis. For instance, a single case study could devote more attention to understanding an organizations specific data needs, and examine how the analysis of mobile app data could fulfill these needs. Taking its point of departure in the companies specific data needs we could ask the question of whether the app should be designed with the purpose of maximizing the data value. This approach suggests a clear data- and analytics-focus, and is hence in direct opposition to the approach carried our in our cases today. Our findings show that the mobile app analytics tools leave room for improvement when it comes to taking full advantage of the mobile app data value propositions. For instance the tools can become better at measuring and displaying location data, they can increase the granularity of the data or they can provide more transparency in their metrics and measurements. As the tools evolve and become better at storing and analyzing personal behavioral data in greater detail, it can be expected that privacy will be a greater concern than is currently the case. Since the data will become more holistic and personal, we expect privacy to play a more predominate role in mobile app analytics, as well as other related data- and analytics driven activities. As mentioned in chapter four the mobile sensing discipline is engaged with using mobile data for research. One of the main focal points of this field is to create a ‘data ecosystem’ in which a

133

mutually beneficial data-exchange can occur. This is particularly relevant for companies, since the sharing of data, and the trust that this data is treated with respect, is a prerequisite of the entire data stream. Moving forward it becomes even more important to understand how to build apps that utilize user data to improve user experience, and hence provide sufficient value and trust to uphold customers’ willingness to share. We expect that studies concerned with mobile data sources will only become more imperative in the coming years, why the need to clarify relevant aspects of mobile app analytics, and document its value, will become ever more essential moving forward. We therefore hope that this thesis can bring focus on mobile app analytics as a valuable data source for organizations, and a topic that requires additional research and scholarly attention.

134

REFERENCES

Online Resources AO http://www.ao.dk/OmAO/GenereltomAO/KortomAO/tabid/1196/language/da-DK/Default.aspx (20.01.2013) Apple http://www.apple.com/pr/library/2013/01/07App-Store-Tops-40-Billion-Downloads-with-Almost-Half-in-2012.html (07.02.2013)

Apptamin http://www.apptamin.com/blog/app-analytics-tools/ (22.02.1013) Avanish Kaushik, A. (2010). Web Analytics Segmentation: Do Or Die, There Is No Try! Available at http://www.kaushik.net/avinash/web-analytics-segments-three-category-recommendations/ (03.02.2013) Big Data http://en.wikipedia.org/wiki/Big_data (01.11.2012) Crunchbase http://www.crunchbase.com/company/localytics (12.11.2012) Danske Medier http://danskemedier.dk/wp-content/uploads/2012/05/PM_INDEXDK_FINAL_20120510.pdf (08.02.2013) Dansk Supermarked http://www.dsg.dk/da/Hvem_er_vi/Pages/Hvemervi.aspx (20.01.2013a) http://www.dsg.dk/da/Nyheder/Pages/NewsDetails.aspx?ItemID=211 (20.01.2013b) Digital Footprints http://en.wikipedia.org/wiki/Digital_footprint (21.12.2012) Elliott, T. (2011). Business Analytics vs Business Intelligence

135

Available at http://timoelliott.com/blog/2011/03/business-analytics-vs-business-intelligence.html (17.12.2012) Flurry http://www.flurry.com/flurry-analytics.html (12.11.2012) Flurry Blog http://blog.flurry.com/bid/79682/iOS-Android-Shatter-Records-on-Christmas-Day (12.11.2012) Flurry Support http://support.flurry.com/index.php?title=Analytics (17.01.2013) Helles, R. (2010). Digitale medier Available at http://www.diku.dk/ominstituttet/jubilee/dikus_jubilaeumsskrift/16.Digitale_medier.pdf/ (21.12.2012) Laney, D. (2001). 3D Management: Controlling Data Volume, Velocity and Variety Available at http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf (23.11.2012) Localytics http://www.localytics.com/app-analytics/ (12.11.2012) Marketingland http://marketingland.com/google-analytics-mobile-app-15382 (06.01.2013) Matzner, R. (2012). Get The Most Out of Mobile App Analytics With These 8 Tips Available at http://mashable.com/2012/11/14/mobile-app-analytics-tips/ (05.01.2013) McCarra, D. (2013). Google Play will hit one billion apps this June Available at http://sociable.co/mobile/google-play-will-hit-one-billion-apps-this-june/ (07.02.2013)

136

Ricci, M. (2012). 10 Reasons why mobile analytics are mission critical to a sustainable mobile strategy Available at http://mobithinking.com/webtrends-mobile-anlytics-interview (05.01.2012) Nikeinc http://nikeinc.com/nike-fuelband/news/nike-fuelband-makes-life-a-sport (23.11.2012) Nykredit http://www.nykredit.dk/omnykredit/info/virksomhed/vores-virksomhed.xml (20.01.2013) O’Reilly, T. (2005). What Is Web 2.0? Design Patterns and Business Models for the Next Generation of Software Available at http://oreilly.com/pub/a/web2/archive/what-is-web-20.html (01.11.2012) Our Mobile Planet: Denmark. Understanding the Mobile Consumer http://services.google.com/fh/files/blogs/our_mobile_planet_denmark_en.pdf (08.02.2013) Small and medium enterprises http://en.wikipedia.org/wiki/Small_and_medium_enterprises (06.02.2013) Smolan, R. & Erwitt, J. (2012). The Human face of Big Data Available at http://www.humanfaceofbigdata.com/ (27.01.2013) Takahashi, D. (2012). Flurry Personas uses analytics to target apps at consumers Available at http://venturebeat.com/2012/08/02/flurrys-personas-platform-uses-analytics-to-target-apps-at-consumers/ (27.01.2013) Webanablog http://www.webanablog.com/ (12.01.2013)

137

WebTrends http://webtrends.com/ (12.01.2013) Wired Magazine. (2008) Issue 16.07 http://www.wired.com/wired/issue/16-07 (25.11.2012) E-books Fish, T. (2009). My Digital Footprint Available at http://book.mydigitalfootprint.com/footprint-cms/Cover.html Books Babbie, E. R. (2007). Human inquiry and Science (p. 61 - 81). In The Practice of Social

Research. Wadsworth, Cengage Learning. Becker, M. & Arnold, J. (2010). Mobile Marketing for Dummies. Hoboken, NJ: Wiley Publishing, Inc. Blumberg, B., Cooper, D., & Schindler, P. (2005). Business research methods. McGraw-Hill. Crotty, M. (1998). The Foundations of Social Research: Meaning and perspective in the

research process. Sage Publications. Davenport, D. H., Harris, J. G., & Morison, R. (2010). Analytics at Work: Smarter

Decisions, Better Results. Boston, MA: Harvard Business School Publishing Corporation. Gilje, N. & Grimen, H. (2002). Samfundsvidenskabernes forudsætninger: Indførelse i

samfundsvidenskabernes videnskabsfilosofi. Hans Reitzel. Kvale, S. (1997). InterView: En Introduktion til det Kvalitative Forskningsinterview. Hans Reitzels Forlag. Kvale, S. (2007). Doing Interviews. Sage Publications Ltd. Ling, R. (2008). New Tech, New Ties. Cambridge, MIT.

138

Saldaña, J. (2009). An introduction to Codes and Coding. In The Coding Manual for

Qualitative Researchers. Sage. Turban, E., Sharda, R., Delen, D., King., D., & Aronson, J. E. (2011). Introduction to Business Intelligence (p. 27 - 43). In Business Intelligence: A Managerial Approach (2nd ed.). Upper Saddle River, NJ: Pearson Education. Yin, R. K. (1984). Case study research: Design and methods (2nd ed.). Newbury Park, CA: Sage.

Zikopoulos, P. C., deRoos, D., Parasuraman, K., Deutsch, T., Corrigan, D. & Giles, James. (2013). Harness the Power of Big Data: The IBM Big Data Platform. McGraw-Hill. Reports Datatilsynet (2011). What does your app know about you: Data protection challenges

in the mobile application market. Årnes, A. & Nes, C. The Economist. (2010). Data, data everywhere: A special report on managing

information. London. Gartner. (2012). Hype Cycle for Big Data. International Telecommunication Union. (2005). The Internet of Things. Geneva. McKinsey Quarterly (2010). The Internet of Things, Issue 2, 70-79. Chui, M., Löffler, M. & Roberts, R. McKinsey Global Institute. (2011). Big data: The next frontier for innovation,

competition, and productivity. Pew Internet. (2011). Half of adult cell phone owners have apps on their phones. Washington, DC: Purcell, K. Pew Research Center. (2012). The future of money: Smartphone swiping in the mobile

age. Washington, DC. Social Semantic.eu. (2012). Networked Business Factbook.

139

World Economic Forum. (2011). Personal data: The emergence of a new a New Asset

Class. Articles Bauer, H. H., Barnes, S. J., Reichardt, T. & Neumann, M. M. (2005). Driving Consumer Acceptance of Mobile Marketing: A Theoretical Framework and Empirical Study, Journal of Electronic Commerce Research, 6 (3) Baumeister, R. F., & Leary, M. R. (1997). Writing Narrative Literature Reviews. Review

of General Psychology, 1 (3), 311-320 boyd, D., Ellison, N. (2008). Social Network Sites: Definition, History, and Scholarship. Journal of Computer-Mediated Communication 13, International Communication Association boyd, d. & Crawford, K. (2011) Six Provocations for Big Data. A Decade in Internet Time. Symposium on the Dynamics of the Internet and Society, September Brynjolfsson, E. & Wu, L. (2009). The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales. Technical report, MIT, 2010 Brynjolfsson, E., Hitt, L. M. & Kim, H. H. (2011). Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Perfromance. ICIS Cooper, A. (2012). What is Analytics? Definition and Essential Characteristics. JISC

CETIS Analytics Series: Vol 1 No. 5 Cropper, E. (2012). Mobile Whitepaper: Overcoming primary obstacles to effective mobile analysis. Stratigent Eagle, N., Montjoye, Y. & Bettencourt, L. (2005). Community Computing: Comparisons between Rural and Urban Societies using Mobile Phone Data. The

Santa Fe Institute Eagle, N. and Pentland, A. (2006). Reality mining: Sensing complex social systems. Pers Ubisquis Comput 10, 255-268

140

Eagle, N., Pentland, A. & Lazer, D. (2009). Inferring Social Network Structure using Mobile Phone Data. Proceedings of the National Academy of Sciences (PNAS), 106(36), 15274-15278 Feldman, S. & Sherman, C., (2001), The High Cost of Not Finding Information, IDC Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S. & Brilliant. L. (2009) Detecting influenza epidemics using search engine query data. Nature, 1012-1014, 2009 Golafshani, N. (2003): Understanding Reliability and Validity in Qualitative Research. The Qualitative Report, 8 (4), 597-607 González, M., Hidalgo, A., et al. & Barabasi, A. (2008). Understanding Individual Human Mobility Patterns. Nature, Vol 453/5 Gruber, T. (2007). Collective Knowledge Systems: Where the Social Web meets the Semantic Web. Journal of Web Semantics Hambrick, D. C. & Fredrickson, J. W. (2001). Are you sure you have a strategy? Academy of Management Executive, 15 (4), 48-59 Hamel, S. (2009). The Web Analytics Maturity Model: A strategic approach based on business maturity and critical success factors Johnson, J. E. (2012). Big Data + Big Analytics = Big Opportunity. FinancialExecutive July/august 2012, 51-3 Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. T. (2010). A Survey of Mobile Phone Sensing. IEEE Communications Magazine, Dartmouth College Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A., Brewer, D.,Christakis, N., Contractor, N., Fowler, J.,Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., & Van Alstyne, M. (2009). Computational Social Science. Science, 323, 721-3 Lim, W. M., (2012). Challenges in Strategic Decision Making and the Corresponding Solution Approaches. Advances In Management, 5 (7), 28-31

141

Ling, R. & Svanæs, D. (2011) Browsers vs. Apps: The Role of Apps in the Mobile Internet. Internet and Society: Challenges, Transformation and Development. University of Beijing Manovich, L. (2011) Trending: The Promises and the Challenges of Big Social Data, Debates in the Digital Humanities, ed M.K.Gold., Minneapolis, MN: The University of Minnesota Press McAfee, A. and Brynfolfsson, E. (2012). Big Data: The Management Revolution. Harvard Business Review McManus, P. & Scornavacca, E. (2005). Mobile Marketing: Killer Application or New Hype? International Conference on Mobile Business (ICMB'05), 294-300 Nielsen, J. (1994). Usability Inspection Methods. CHI '94 Conference Companion on

Human Factors in Computing Systems, 413-414 O’Reilly, T. & Battelle, J. (2009) Web Squared: Web 2.0 Five Years On. Web 2.0

Summit Oviatt, S. (2002). Multimodal Interfaces. Handbook of Human-Computer Interaction, 286-304 Panian, Z. (2007). Just-in-Time Business Intelligence and Real-Time Decisioning. International Journal of Applied Mathematics and Informatics, 1 (1), 28-35 Panian, Z. (2012). The Evolution of Business Intelligence: From Historical Data Mining to Mobile and Location-Based Intelligence. Proceedings of the 4

th WSEAS World

Multiconference on Applied Economics, Business and Development (AEBD’12), Porto, Portugal, July 1-3, 118-127 Pentland, A. (2009). Reality Mining of Mobile Communications: Toward a New Deal on Data, chapter 1,6 in Reality Mining of Mobile Communication, Massachusetts Institute of Technology (MIT) Reades, J., Calabrese, F. & Ratti, C. (2009) Eigenplaces: Analyzing Cities Using the Space-time Structure of the Mobile Phone Network. Environment & Planning B, vol.

142

36, 824-836 Rickson, E. (2012). Measuring the Success of Your Mobile Strategy. Webtrends. Wexelblat, A. & Maes, P. (1999). Footprints: History-Rich Tools for Information Foraging. CHI’99 Proceedings. ACM Press Zhang, D., Guo, B. & Yo, Z. (2010). Social and Community Intelligence. Institut

Telecom

Mobile App Analytics

Technology

Transcript of Mobile App Analytics