Measuring Social Media performance - Noémie Moreau Ikidbachian
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Transcript of Measuring Social Media performance - Noémie Moreau Ikidbachian
I would like to thank
Andrew Walker for initiating a new passion.
Jim Sterne, Marshall Sponder and David Meerman-Scott for
feeding my growing interest and being so accessible.
Michael Barnes for accompanying me during this project.
Last but not least, this project would not have been possible without
the support of my family, my friends, and my dear Anatole.
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One in every 13 people on earth uses Facebook (Facebook, August 2011). Last year in
the US, $716 million have been spent by businesses in social media and it should
reach $3.1 billion in 2014 (Forester, 2011). However, 84% of companies do not
measure the return of their investment (ROI) in social media (Mzinga and Babson,
2009). In this context, the question is: why do they keep spending more and more, if
they can’t measure?
This project aims at determining if social media performance can be measured. For this
purpose, it explores the relationship between social media and ROI, the irrationality
surrounding the field and provides a set of metrics and best practices to optimize
measurement. It is based on the hypothesis that performance can be measured,
involving a combination of qualitative and quantitative metrics, and keeping a degree of
apparent irrationality.
The literature review sets the framework of this research, clarifying the social media
environment, especially the craze around it and detailing the various motives that may
explain the corporate engagement. It describes the relationship between ROI and
social media, emphasizing on the clash of cultures and the issues that derive from
such incompatibility. It finally focuses on the technological aspect, web analytics, the
lack of widely accepted standards, the immaturity of most organizations and the new
metrics and practices that the business world is not ready to accept and integrate yet.
The research has been lead following the post-positivist philosophy accompanied with
a deductive approach. Mostly qualitative methods have been used, but a triangulation
with other methods has also been necessary to moderate biases and optimize
reliability. Worldwide figures of the sector have accepted to bring their viewpoints,
providing sound and advanced insights. They have opened areas and directions for
further research.
Major findings include the necessity to discriminate existing key terms, an animated
discussion around a potential social media bubble, main challenges to social media
performance measurement and strong advice on best practices.
The conclusion confirms that social media performance can be measured. This implies
a redefinition of key concepts and to introduce new practices. Irrationality has been
clarified and the existence of an economic bubble has been rejected for now.
Eventually this project proposes a new framework to optimize social media
performance measurement, including good practice rules and a new set of metrics.
Table of contents
I. Research question, hypotheses and objectives ................................................ 2 i. Research question ............................................................................................... 2 ii. Hypotheses ......................................................................................................... 3 iii. Research objectives ........................................................................................... 3
II. Clarifying the key terms....................................................................................... 3 i. Social Media ........................................................................................................ 4 ii. ROI...................................................................................................................... 4 iii. Web Analytics..................................................................................................... 5
1. Critical literature review............................................................................6
1.1. Social media....................................................................................................... 6 1.1.1. Definitions and current context ..................................................................... 6 1.1.2. Infatuation or necessity?............................................................................... 8 1.1.3. Irrationality: is there a social media bubble? ................................................ 9
1.2. Return on Investment (ROI) .............................................................................. 9 1.2.1. ROI and marketing: a clash of cultures? .................................................... 11 1.2.2. No ROI without measure – what to measure and how? ............................. 11 1.2.3. The qualitative element: lack of accountability or simple incompatibility? .. 13
1.3. Web Analytics .................................................................................................. 13 1.3.1. Definition and current context ..................................................................... 13 1.3.2. Aligning offline and online activities ............................................................ 14 1.3.3. Social media ROI, a new framework? ........................................................ 15
1.4. Research gap ................................................................................................... 16
2. Methodology ............................................................................................17
2.1. Research philosophy ...................................................................................... 17 2.1.1. The positivist approach: suitability and limitations. ..................................... 17 2.1.2. From positivism to post-positivism.............................................................. 19
2.2. Research approach ......................................................................................... 20 2.3. Research strategy ........................................................................................... 21 2.4. Research choice: data collection and analysis ............................................ 22
2.4.1. Multiple-methods ........................................................................................ 22 2.4.2. Secondary data........................................................................................... 23 2.4.3. Secondary data contribution to qualitative primary data gathering............. 23
3. Findings/results.......................................................................................26
3.1. Measuring social media performance ........................................................... 26 3.1.1. Discriminating key terms............................................................................. 26 3.1.2. Managing data ............................................................................................ 27
3.2. Social media and ROI...................................................................................... 28 3.2.1. The nature of the relationship ..................................................................... 28 3.2.2. The main issues.......................................................................................... 29
3.3. Level of irrationality ........................................................................................ 31
4. Analysis and discussion of findings .....................................................33
4.1. Context of social media and ROI ................................................................... 33 4.1.1. Cultural aspect......................................................................................... 33 4.1.2. Data ......................................................................................................... 34 4.1.3. Tools and technology ................................................................................. 36 4.1.4. Motives and objectives ............................................................................... 36
4.2. The issue of irrationality ................................................................................. 37 4.2.1. Lack of accountability ................................................................................. 37
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4.2.2. Craze ...................................................................................................... 37 4.2.3. Bubble......................................................................................................... 37
4.3. Metrics and measurement .............................................................................. 38 4.3.1. Use of metrics............................................................................................. 38 4.3.2. Lack of standards ....................................................................................... 39 4.3.3. Frameworks ................................................................................................ 40 4.3.4. Best practices ............................................................................................. 41
Recommendations and conclusion...........................................................42
Appendices ..................................................................................................50
Bibliography ................................................................................................51
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MEASURING SOCIAL MEDIA PERFORMANCE
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Social Media are increasing their audience rapidly and consume nearly 25% of
people’s time online. The average visitor spends 66% more time on these sites
or platforms than they did a year ago. (Nielsen, June 2010). This is a worldwide
phenomenon.
As soon as this progress has been confirmed, the business world has dived into
social media: from a modest presence to sometimes a major investment
reaching millions of pounds, the corporate engagement and budgets allocated
to social media have been constantly increasing.
Forrester Research (2011) estimates that “in the US, $716 million was spent on
social media marketing in 2010, and that it will reach $3.1 billion by 2014”. At
that point, social media will be a greater channel than email or mobile, but still
quite smaller than search or display advertising.
The growth is rapid and widespread. 84% of companies and marketing
agencies have announced an increase of their social media investments in
2011 (E-consultancy, November 2010), no matter what economic sector they
operate in.
On average, social media investment represents 10% of the total marketing
budget of companies (9.9% in February 2010, Duke University, 2010) and is
expected to increase to 17.7 % within four years.
This new investment is funded either by increasing the overall marketing budget
(44 % of businesses expanded their marketing budgets in 2011 to fund their
social media strategy – Nielsen, 2011), but more often by reallocating existing
marketing lines to social media. As a result of the trade-off, telemarketing and
direct mail are experiencing the biggest declines in spending.
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Human resources account for nearly 60% of social marketing expenditures,
including staff salaries for blogging, content development, social monitoring…
and 20% of the budget are fees for agencies, consultancies and other social
marketing service providers (Marketing Sherpa, 2010).
Marketers have been captivated by the new range of social media tools, and
their essential concern has been to identify which latest social media platform
they could add to the marketing mix. Still today, intuition seems to be the
ground for many decisions rather than achievable objectives. In May 2010, 52%
of marketers had no plan for social media (Digital Brand Expressions, 2010).
In such a context it is not surprising, as research has highlighted, that 84% of
organizations do not measure the return of their social media actions and
investment (Mzinga and Babson Executive Education, 2009). Now, given all the
above, why do organizations keep on substantially increasing their social media
investment? Behind this question lies the main issue of social media
performance evaluation, which is the topic of this research project.
The first step of the reflexion will consist in looking at the genuine natures of
both social media and ROI. On the one hand, social media are defined by
users’ interactions that intrinsically cannot be numerically quantified, or only
partially. On the other hand, ROI rests on quantifiable values. This inherent
incompatibility impacts on social media legitimacy, acceptability and credibility
within the business world.
I. Research question, hypotheses and objectives
i. Research question
This research aims at providing a theoretical and empirical contribution to the
issue of evaluation of social media performance, focusing eventually on the
return on investment in social media.
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More specifically, it explores one main research question: “Is it currently
possible to measure a company’s social media performance. If yes, to what
extent?”
ii. Hypotheses
For the purpose of this project, two hypotheses are formulated.
First, that it is possible to measure social media performance, using a
combination of quantitative and qualitative indicators.
Second, that it involves a significant level of irrationality (possibly related to the
dot com bubble), which tends towards impossibility to accurately measure it.
iii. Research objectives
The research objective is to investigate the context of social media investment
and ROI calculation within companies, assessing the current context and
challenges.
Therefore the research will first clarify the current and complex relationship
between social media investment and ROI culture.
It will then identify the extent of irrationality around increasing corporate
investment in social media.
Last but not least, it aims at providing a set of metrics and good practice rules to
optimize social media ROI calculation.
II. Clarifying the key terms
Before exploring the subject in further details and critically reviewing the
literature, key research terms must clarified.
For each of these key concepts, social media (SM), Return on Investment (ROI)
and Web analytics will be presented and further discussed in the critical
literature review.
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i. Social Media
In the introduction of his book “Social Media Metrics”, Sterne (2011) suggests
that social media “allow anybody to communicate with everybody. In other
words, consumer-generated content distributed through easy-to-access online
tools.”
The term social media basically refers to web-based and interactive technology
and communication tools. Kaplan and Haenlein (2010) define it as “a group of
internet-based applications that build on the ideological and technological
foundations of web 2.0. and that allow the creation and exchange of user-
generated content”. A key element is social interaction.
Social media can take different forms and are used for many different purposes.
Hence, many categorisations have been developed. For the purpose of this
research, the Interactive Advertising Bureau (2009) classification has been
selected. It breaks down social media into three main categories: (1) social
media sites, (2) blogs, and widgets and (3) social media applications. This
classification will favour a reliable analysis of observations.
Among all social media platforms available, this research project will focus on
social media sites, such as Facebook, Myspace or LinkedIn, as this is where
companies are investing the most, and therefore where the ROI challenge
stands.
ii. ROI
Organizations aim at being profitable. Logically, any spending needs to be
justified, in terms of return (ROI) whether it is contribution to profit, market share
or strategic positioning among others.
Investment is commonly linked to profit. Profit can be defined as “the return that
a company makes on the resources it has invested. Investors expect to see a
profit return in line with the amount of money invested. The main measures of
profits used therefore are return on capital or return on investment” (Lomax and
Raman, 2008).
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In finance, the return is the measure of the result of any investment. The ROI is
a ratio that correlates the money gained or lost on an investment and the
amount of money invested.
There are several methods to measure ROI, but the most common calculation
(which will be used in this research) is the following:
ROI = (Gain of investment – Cost of investment)
Cost of investment
In the context of social media, only half of the equation seems to be
measurable: the amount of the investment. The other half is hardly measurable.
How to measure the presence or quantify the interactivity and relate it to the
money invested?
Lise Brende, director of marketing analytics at Bing, explains in The Wall Street
Journal her difficulty to convince executives “to cut big checks” for social media
without traditional analytics to prove its value (2011). Brende suggests a “lack of
a ratings system” as one reason for this challenge.
A source of inspiration for successful performance measurement would be the
new analytical and monitoring tools introduced by web analytics.
iii. Web Analytics
The Web Analysis Association (WAA) (2011) proposes a concise definition:
“web analytics is the measurement, collection, analysis and reporting of Internet
data for purposes of understanding and optimizing web usage”.
Chaffey (2008) defines it more specifically as “the techniques used to assess
and improve the contribution of e-marketing to a business including reviewing
traffic volume, referrals, clickstreams, online reach data, customer satisfaction
surveys, leads and sales”.
Web analytics are used by a minority of organizations. However, it is gradually
democratizing along with the increase of the maturity level of the companies.
However, as it will develop, the limitations of web analytics come from the fact
that they deal with figures and quantities only.
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The various aspects of these three central concepts will now be further
investigated in the available literature.
1. Critical literature review
As announced above, this literature review will cover the areas of social media
(SM), Return on Investment (ROI) and Web analytics. It will emphasize on
previous research insights and arguments, which can contribute to a better
understanding of the subjects addressed. It includes previous academic
research, academic journals, books and industry and marketing reports. We will
follow Saunders et al. (2009) when he defines literature review as a “detailed
and justified analysis and commentary of the merits and faults of the literature
within a chosen area, which demonstrates familiarity with what is already known
about [the] research topic”.
As a first step, conceptual clarification on the above-mentioned key terms will
be discussed, and definitions will be put into perspective.
1.1. Social media
1.1.1. Definitions and current context
One of the most comprehensive descriptions of the social media phenomenon
has been provided by Joshi (2007). “The past few years have seen the advent
of Web based social media systems such as blogs, wikis, media-sharing sites
and message forums. Such Web 2.0. systems have a significant amount of user
generated content, and have become an important new way to publish
information, engage in discussions and form communities around the Internet.
Their reach and impact is significant with tens of millions of people providing
content on a regular basis around the world.” (Joshi et al., 2007).
Indeed, the phenomenon is considerable. With over 750 million users,
Facebook is now used by 1 in every 13 people on earth, with over 50% who log
in every day» (Facebook, 2011). On a more local scale, an eMarketer survey
has highlighted that as of February 2011, “88% of Brits used social networking
sites” (eMarketer, February 2011).
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The phenomenon is new, diverse, somewhat controversial, and the existing
literature does not provide a uniform definition.
Kaplan and Haenlein’s definition has been presented in the introduction (2010).
On the other hand, Scott and Jacka (2011) define it as “the set of Web-based
broadcast technologies that enable the democratization of content, giving
people the ability to emerge from consumers of content to publishers.”
This lack of uniformity mentioned above also occurs when it comes to
suggesting a classification.
Kaplan and Haenlein (2010) identify six types of social media: collaborative
projects (e.g. Wikipedia), blogs (e.g. Blogger) and microblogs (e.g. Twitter),
content communities (e.g. Flickr, Youtube), social networking sites (e.g.
Facebook), virtual game worlds (e.g. World of Warcraft) and virtual social
worlds (e.g. Second Life).
Sterne (2011) breaks down the social media catalogue into six categories:
forums and message boards, review and opinion sites, social networks,
blogging, microblogging, bookmarking and media sharing.
The IAB (2009) divides social media into three main categories: social media
sites, blogs, and widgets and social media applications.
The above classifications tend to describe the different platforms and tools
available. However, the IAB also focuses on the uses and benefits. For the
purpose of this research the IAB classification will be used, with a focus on
social media sites:
“Social media Sites are characterized by the inherent functionality that
facilitates the sharing of information between users within a defined network.
The nature of Social media allows for the initiation of conversation by either
party, a key differentiator from established broadcast channels” (IAB, 2009). “It
has added a participatory element where an individual not only receives
information but has the ability to take part in the creation and distribution of
content” (IAB, 2009).
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1.1.2. Infatuation or necessity?
The growth of social media is comparable to a revolution (Smith, 2009) and the
case of Facebook is revealing. In 2010, the company generated revenues of
about $2,000 billion, a growth of 158% (Bloomberg, 2010). The attraction to
social media is like “an abiding love affair of consumers with social networks”
says Weide, an analyst at IDC (2010).
Logically, the business world has been attracted by this enormous population of
consumers, and quickly decided to engage in this new world. In 2010, Weide
could say “all the big brands are there” (Bloomberg, 2010). However, this initial
move from the companies was intuition-based (on the volume i.e. number of
users). It then gradually evolved.
In 2009, Tia Fisher identified two major reasons for companies to enter the
social media arena. First, Social media is where actual and potential customers
are interacting, and it shapes how they think. Then, because competitors are
already doing it, and “a company’s absence is only highlighted by such
accessibility” (Alston, 2009).
In 2011, Blanchard added that “social media is a communication tool, like the
telephone and email, that serves the purposes of critical business functions,
including public relations, marketing, lead generation, customer service, and
market research. Some of the best uses for social media activities are in
support of these particular business functions, and here is the best part: each
one of these business functions already has its very own objectives and targets,
which you can plug your social media into” (Blanchard, 2011).
Most of businesses, either small or large, record at least a presence on social
media, but above all allocate it with an increasingly significant budget. However,
surprisingly enough, 84% of companies do not measure ROI of social media
activities (Mzinga and Babson Executive Education, 2009) and 80%
acknowledged the difficulty in tracking ROI in the medium (e-consultancy,
2010).
How can this investment be constantly growing if its efficiency ie the ROI is not
measured? Or do they measure differently? How can they justify this spending?
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1.1.3. Irrationality: is there a social media bubble?
With a lack of ability to measure performance and return, one could argue that
the gradual growth of the investment is irrational, defined as « not logical or
reasonable » (Oxford, 2011).
Some people may even consider that in the absence of a reliable evaluation of
its efficiency, this growing investment is comparable to the formation of a
bubble. An economic bubble is defined as "trade in high volumes at prices that
are considerably at variance with intrinsic values" (King et al., 1993). Because
they are groundless, bubbles are always ephemeral. Bubbles cycles involve a
boom (prices reaching ‘absurd’ levels), followed by a crash (sudden drop of
prices).
The recent level of acquisitions of social media companies such as LinkedIn or
Skype have unveiled figures way above the traditional acquisition ratios.
Thompson (2011), senior editor at The Atlantic said that “LinkedIn has the
highest price-revenue ratio of any stock anywhere”. Answering Thompson,
Robak (2011), president of Pluris Valuation Advisors states that « from a
fundamental valuation standpoint, this looks like an incredibly rich – unusually
rich – valuation, even by tech company standards. It really reminds me a lot of
the late 1990s ».
This illustrates the irrationality that surrounds social media and arouse the idea
of a potential investment bubble. Some argue that bubbles cause misallocation
of resources into non-optimal uses, then having a negative impact on the
economy. Well-known bubbles include the tulip mania (recorded in 1637!) or
more recently real estate or dot com bubbles among others. Are we going
through a bubble with social media? Existing reliable and credible literature
does not provide answers to this question yet.
1.2. Return on Investment (ROI)
As said above, the traditional evaluation of the return of an investment is a ratio
that measures the correlation between the quantities invested, usually valued in
monetary terms, and the quantities gained. Applied to social media, the
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investment is easy to measure but when it comes to measuring the return,
businesses face difficulties. The main reasons are the lack of adapted tools, but
also a poor definition of the initial objectives by the companies. One evaluates a
return by setting objectives and comparing them with the results obtained. In
the reality, when it comes to social media, most companies define global targets
such as “exposure”, reputation”, branding, “traffic” (not sales), search ranking
improvement… hardly measurable.
It is essential to measure success or failure of actions, in order to be able to
build a strategy or make some decision about future investments or simply
marketing budget allocations. Since ROI is currently difficult or impossible to
measure in social media, success or failure cannot be assessed. Moreover, “the
truth is that you cannot succeed in anything if success is never defined” (Solis,
2011).
Due to the fact that often executives are digital immigrants (Prensky, 2001), and
therefore not mature and confident as digital natives regarding digital activities,
they interpret the current situation as uncertain and demanding caution and
prudence.
Solis’s foreword to Blanchard’s “Social Media ROI” gives a good description of
the challenge: “as in anything in business, the ability to tie activity to the
business values is critical. If we are to commit time, resources, and budget to
social networks, our investments must be justified. Indeed, social media
strategies must prove long-term value and contribution to the bottom line in
order to evolve into a pillar of business success. But how do you measure
something when best practices, case studies, and answers in general are
elusive? We are struggling to prove the merit of an important ingredient in the
future success of business because precedents have yet to be written or tested”
(Blanchard, 2011).
As seen, the ROI may not be easily measurable in traditional and quantitative
terms but this difficulty is not new and it is worth exploring the possibility of
using measures in qualitative terms as it has been previously experimented in
other fields.
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1.2.1. ROI and marketing: a clash of cultures?
Since its early days, the relationship between ROI and marketing has been
challenging. Marketing teams have “not been held accountable for showing how
marketing expenditures contribute to increasing shareholder value. As time has
gone by, this lack of accountability has undermined marketer’s credibility,
threatened the standing of the marketing function within the firm, and even
threatened marketing’s existence as a distinct capability within the firm” (Rust et
al., 2004).
While the marketing team will focus on measuring brand awareness, which is
naturally hard to quantify, the financial team will look for an accurate numerical
value to fill in a financial statement or the balance sheet. Effectiveness of
marketing activities such as PR or advertising is also delicate to measure. How
can you accurately measure the ROI of an outdoor advertising campaign?
This issue has been latent since the early days of advertising. Back in 1966,
Dean pointed out “the problem of how much to invest in advertising”, wondering
if it is “formal and objective, rather than intuitive” (Dean, 1966). The incapacity
to quantify the return of advertising triggers immediate concerns: is it an
investment or a current expense? Is it geared by objectivity or intuition?
This lack of accountability applies to digital activities and particularly social
media, as data about offsite actions is harder to capture, and must be combined
with other indicators, which are often qualitative.
1.2.2. No ROI without measure – what to measure and how?
1.2.2.1. The necessity to set a direction and objectives
“If a man does not know what port he is steering for, no
wind is favourable to him.”
Seneca, mid-first Century BC
The same applies to social media. If you invest without clear objectives, you will
never know if you have reached your destination. More specifically, Blanchard
(2011) highlights the confusion between strategy, tactics and objectives in the
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context of social media: “trying to come up with a social media strategy to
somehow justify the need for a social media program in the first place was
pointless. But using social media as a vehicle to support existing business
objectives, now that made a lot more sense”. He concludes by stating “a social
media program, in order to deliver results, must have a purpose rooted in
business objectives” (Blanchard, 2011). On the other hand, “when we need
benchmarks to evaluate return on investment (ROI) effectiveness, marketing
professionals should first determine the duration of a campaign and what
milestones or standards they want to reach” (Sponder, 2011).
1.2.2.2. The Key Performance Indicator (KPI)
It then appears essential to align social media with SMART (Doran, 1981)
business objectives and key performance indicators (KPIs) in order to measure
ROI.
In the case of an objective of brand awareness or customer engagement, what
is exactly meant? Would there be KPIs to measure brand awareness? KPIs
would probably be complex and uncertain. Can increased product or brand
attractiveness be measured as a ROI?
KPIs are especially hard to set for qualitative objectives such as brand
awareness or reputation, which appear to be common objectives of social
media presence.
Within social media, objectives can be of different natures. Social media and
specifically social networking sites aim at connecting people. These people
usually use them for various reasons: “it can be for altruistic reasons, it can be
for brand building, it can be for ego boosting, it can simply be a creative outlet.
For businesses it can be a way of getting closer to their customers, getting
direct feedback, creating a unique brand identity, being hip and cool – or some
may do it just because CEO wants it” said Kaushik (2007). This multitude of
motives and the lack of accountability (e.g. how would you accurately measure
a company’s closeness to customers?) make ROI measurement a real
challenge. However, the necessity to measure remains.
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1.2.3. The qualitative element: lack of accountability or simple
incompatibility?
The lack of access to quantitative data captured offsite and often the lack of
specific objectives for social media marketing among other factors, have
increased the necessity to consider qualitative data, trying to turn them into a
key measure of social media performance.
A common watchword in the business world says “if you can’t measure it, you
can’t manage it”. There are things that cannot be measured but still exist (think
about a company’s culture). This summarizes the dilemma between social
media benefits and ROI measurement. Can they both be conciliated?
Blanchard writes, “setting targets creates accountability” (Blanchard, 2011).
This is one of the major challenges of this research project.
1.3. Web Analytics
1.3.1. Definition and current context
Web analytics are the technology used to monitor online activities (including
social media), capture the coveted data, and make the analysis possible.
Chaffey (2008) defines web analytics as the “techniques used to assess and
improve the contribution of e-marketing to a business including reviewing traffic
volume, referrals, clickstreams, online reach data, customer satisfaction
surveys, leads and sales”.
The use of web analytics is currently democratizing and only used by a minority
of organizations, often at a low maturity level. This may partly explain the 84%
of companies not measuring ROI of social media (Mzinga and Babson, 2009).
However, as outlined by Sponder (2011), “the average business user, agency
owner, or stakeholder lacked (and still lacks) the understanding, patience, or
sophistication to utilize these listening platforms effectively. As a result, they
hire analysts to access the platforms for them and to provide reporting data and
dashboards. Dashboards are reports that give users a high-level snapshot of
how their campaigns and business initiatives are performing online”.
Then, it seems that although web analytics reports may be easy to share, they
are not clearly understandable by those who do not have the specific skills and
market knowledge, and thus do not give the same level of insight to everyone.
Social media has moved web analytics another steps forward, introducing a
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new level of analysis, a range of new actionable metrics and challenges. An
important one relies in the merger of web and social media analytics data.
Marketwire Sysomos, a social media listening platform has created a program
called Sysomos Audience, attempting to merge Web and social media data to
produce superior ROI metrics. « Audience fills the significant gap between Web
analytics services, which provide information about what is happening on your
Web site, and social media monitoring services that track who is talking about
your company or brand» (Sysomos, 2011).
As Benson, a social media expert, outlines, “Web Analytics provide traditional
attributes of page views, unique site visitors, etc. This alone isn’t adequate
anymore because customers are talking to each other (and much of it isn’t on
your site)” (Fischer, 2009).
This suggests that “returns from social media investments will not always be
measured in dollars, but also in customer behaviours (consumer investments)
tied to particular social media applications”. (MIT, 2010)
Yet, this qualitative element is theoretically not an acceptable ROI metric for the
financial department. “Without the ability to accurately measure social media,
however, it’s hard to see how ROI calculations can be done well” (Sponder,
2011).
1.3.2. Aligning offline and online activities
Thus, to measure the social media ROI, other sources of data must be used. It
can include traditional ROI techniques such as marketing research. However, in
order to be effective, a social media strategy must be integrated within a digital
strategy, in line with the offline strategy. Successful integration of the various
channels can lead to cross-channel synergies. How can those synergies be
measured? To which channel should the added value be attributed? When
there is synergy between different tools, how would the ROI be measured?
Would the tools be grouped or would the measure be individualized?
In order to integrate both offline and online aspects, the REAN framework
(Blanc, 2006, Jackson, 2009), standing for Reach, Engage, Activate and
Nurture, seems to be the most appropriate tool as it addresses cross-channel
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strategies. In the context of this project, it will enable the analysis of the
synergies between social media and other online activities, and offline or other
‘traditional’ marketing actions.
1.3.3. Social media ROI, a new framework?
A first review of the literature confirms that Social media ROI cannot be
measured like traditional marketing ROI. As outlined by MIT research
“marketers are struggling with social media measurement partly because the
frameworks are still largely driven by reach and frequency and are ill-suited to
the interactive media environment” (MIT, 2010).
1.3.3.1. Owyang’s framework
In an attempt to address the issues around ROI of social media, Owyang (2010)
has developed the social media ROI pyramid, a framework serving as a guide
to measure social media performance. Owyang provides a list of diverse
attributes to be measured, including activity, tone, velocity, attention,
participation and other qualitative items.
However, this framework does not solve all the issues associated to
measurement, as it is only qualitative and can tend to verse into subjectivity. It
poses the problem of credibility of measurement examined in this project.
1.3.3.2. The IAB framework
The Social Media Council of the above mentioned IAB has published “a new
framework for measuring social media activity” (Pentin, 2011) addressing these
issues.
Pentin (2011) identifies six main reasons why a framework is required:
accountability, efficacy, interpretation, optimisation, benchmarking, and
standardisation.
The framework consists of three fields:
• I: Intent
• A: Awareness, appreciation, action, advocacy
• B: Benchmark
This framework integrates most of the common issues of social media
performance measurement, providing KPIs sets and other good practice rules.
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In order to assess its usability for this research, its pertinence and adaptability
will be analysed further.
In summary, these frameworks are first step towards optimization of social
media performance measurement, but present some limitations that will be
addressed in primary research.
How to measure social media ROI in a way that would make it accountable?
Which metrics must be used as a pattern for social media measurement, and
which ones must be set case by case?
Considering the lack of information and access to data regarding Social media
ROI, why are the allocated budgets increasing? Would this be a form of trust, or
on the contrary, fear for exclusion?
1.4. Research gap
Extensive research has already been conducted on the issues surrounding
social media, including influence on branding or sales contribution among
others. In parallel, research around ROI of social media has also been
conducted, but remains incomplete. The research findings mostly converge
towards the combination of both qualitative and quantitative metrics.
A few points, such as the broad implementation and integration of a digital-
specific strategy (including web analytics) within the company are usually
considered as secondary, and not as primary influencing factors.
The discrepancy between ROI and social media, one being quantitative and the
other devoted not to be quantified, is usually not central in the existing literature.
The immaturity of the sector appears to be a key element that could partially
explain the issues faced by businesses when it comes to measuring digital and
more specifically social media performance. This point will be further discussed
with the respondents.
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2. Methodology
This section intends to demonstrate how knowledge will be developed and data
produced, in line with the research questions and objectives. For this purpose,
four fields will be covered: research philosophy, approach, strategy and choice.
2.1. Research philosophy
The research philosophy relates to the development of knowledge and the
nature of that knowledge in relation to research (Saunders, 2009). Even if the
importance of the philosophy is less prominent in business-related projects than
in social science or scientific projects, its importance and overall influence on
the whole project remains high. The philosophy helps to refine the research
methods to be used, to identify the evidence gathered, to interpret and hence to
contribute to the research questions. It also helps to recognize the limitations of
each approach.
The most common research philosophies in business projects are positivism,
realism, interpretivism and pragmatism (Saunders, 2009). Among these
research philosophies available, positivism and post positivism will be explored
in relation to research methodology.
2.1.1. The positivist approach: suitability and limitations.
“Positivism asserts that the real knowledge is based
on experience and positive verification”
Auguste Comte, 1865
Saunders defines positivism as “the epistemological position that advocates
working with an observable social reality. The emphasis is on highly structured
methodology to favour replication, and the end product can be law-like
generalisations, similar to those produced by the physical and natural scientists”
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(Saunders, 2009). Smith (1998) also proposes a useful descriptive analysis:
“Positivist approaches to the social science assume things can be studied as
hard facts and the relationship between these facts can be established as
scientific laws. For positivists, such laws have the status of truth”.
This stresses the importance given to induction and verification and the
establishment of laws, which, in principle, aims to avoid subjective viewpoints.
The inductive approach has direct implications for a research based on it.
At first, the idea of law-like principles behind positivism seems to suit this
project, as research objectives include the development of good practice rules
and a set of metrics to be used as a “template” for performance measurement.
However, considering the specificities of the topic (such as the fast pace and
evolution of the sector, importance of the qualitative aspect…), positivism
presents a number of limitations.
Research needs to be quantitative to become the basis of fundamental laws.
This approach is not adapted to provide an in-depth understanding human
beings and their behaviours.
Moreover, when it comes to measuring performance, numerous factors
intervene, which contribute to make each case specific and unique. Law-like
generalisations may thus not prove relevant to this subject, because of their
inflexibility and potential lack of adaptability among others.
Then, the structured approach of positivist reasoning may prove to be too rigid
for such an empirical project, in which new thinking fields and research areas
need be constantly considered in order to extend the reflection perimeter.
The variety of behaviours, perceptions, feelings and attitudes towards social
media are beyond the scope of positivism.
In order to address these limitations to suit this research project, further
research has been conducted, leading to post-positivism.
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2.1.2. From positivism to post-positivism
“Post-positivism provides an alternative to the
traditions and foundations of positivism for conducting
disciplined inquiry. For the post-positivist researcher
reality is not a rigid thing, instead it is a creation of
those individuals involved in the research.”
Hughes, 1994
The recognition of the limitations of positivism gave way to the emergence of a
new philosophy, post-positivism, which claims that “reality does not exist within
a vacuum, its composition is influenced by its context, and many constructions
of reality are therefore possible” (Hughes 1994). Proctor (1998) suggests that
“among the various factors that influence reality construction, culture, gender,
and cultural beliefs are the most significant”. Post positivism enables recognition
of the elaborate relationship between individual behaviour, attitudes, socio-
cultural context and external environment.
Post-positivism can be used to analyse the various factors that influence reality
and “the intricate relationship between individual behaviour, attitudes, external
structures, and socio-cultural issues such as cultural beliefs” (Proctor, 1998).
This applies to the analysis of social media use and related behaviours.
In conclusion, “positivism adopts a clear quantitative approach to investigating
phenomena as opposed to post-positivist approaches, which aim to describe
and explore in depth phenomena from a qualitative perspective” (Crossan,
2003).
The qualitative approach, as opposed to the quantitative approach of positivism,
is quite adapted to explore and understand the wide variety of behaviours and
attitudes around social media.
The limitations of post-positivism generally relate to qualitative methods, which
imply active participation of investigators, interactivity and proximity of the
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researcher to the investigation. This human intimacy can introduce a certain
level of bias.
Considering those definitions and critics, post-positivism has been chosen to
develop this research project. This decision also involves important implications,
in the research approach, strategy and choice.
2.2. Research approach
Two main research approaches are available. One, the deductive approach,
starts from the need to test theory with observation and the other one, the
inductive approach, starts from the need to develop theory from observation.
As further explained by Saunders (2009), the deductive approach involves “the
testing of a theoretical proposition by the employment of a research strategy
specifically designed for the purpose of its testing” (Saunders, 2009).
This approach is informally called the “top down” approach. It is concerned with
testing and confirming hypotheses.
This research being concerned with an initial question, or initial hypothesis, the
deductive reasoning has been considered as the most appropriate.
However, the conditions for sound deductive reasoning are the necessary
quality of the initial assumption and the empirical evidence to confirm it. As
Hume argues, “our everyday functioning depends on drawing uncertain
conclusions from our relatively limited experiences rather than on deductively
valid arguments”. The observations need to be unbiased and representative,
otherwise the whole process will be deteriorated.
For these reasons, triangulation with some inductive reasoning has been used
to maximize data reliability. “Triangulation is a powerful technique that facilitates
validation of data through cross verification from more than two sources. In
particular, it refers to the application and combination of several research
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methodologies in the study of the same phenomenon” (Bogdan and Biklen,
2006).
2.3. Research strategy
The research strategy determines the overall plan of how the researcher will go
about answering the research questions.
In business research projects, several strategies are available (e.g. experiment,
survey, case study, action research, grounded theory or archival research
among others).
On the one hand, Yin (2003) states that “each strategy can be used for
exploratory, descriptive and explanatory research. On the other hand, Saunders
(2009) argues that “some of these clearly belong to the deductive approach,
others to the inductive approach”.
Given the form of the research question and the focus on a contemporary event
(vs historical), the case study has clearly appeared to be the most appropriate.
The case study is “a strategy for doing research which involves an empirical
investigation of a particular contemporary phenomenon within its real-life
context using multiple sources of evidence” (Robson, 2002). Yin et al. (1984)
add that it is “an empirical inquiry which investigates a contemporary
phenomenon within its real-life context when the boundaries between
phenomenon and context are not clearly evident and in which multiple sources
of evidence are used.
Flexibility allowed by the case study is essential. As highlighted by Schell
(1992), “the case study is the most flexible of all research designs, allowing the
researcher to retain the holistic characteristics of real-life events while
investigating empirical events”. It enables the integration of reality, flexibility, a
holistic approach and therefore to understand this rather complex phenomenon.
Schell (1992) adds that “as a form of research, the case study is unparalleled
for its ability to consider a single or complex research question within an
environment rich with contextual variables”.
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The case study strategy has efficiently contributed to resolve both the research
questions and objectives of this project. It has lead to a combination of
exploratory and explanatory studies, as its main goal aimed at providing a deep
understanding of the context, enabling to develop theory about the relationship
between the different variables involved.
Observation, experimentation, interviews and literature have been used to
support this project.
2.4. Research choice: data collection and analysis
Although quantitative data has been essential to assess the relevance of this
project, this research project is a multi-method qualitative study, as the nature of
the topic is somewhat diverse, and biases need to be moderated. This choice
has been made to maximize reliability and generalization. The essence of this
project requires a combination of methods to describe and analyse the
complexity of the phenomenon studied.
2.4.1. Multiple-methods
Multiple-methods consist in blending qualitative and quantitative research as
well as designing, collecting and analysing data from different methodologies.
The social media and the business world are a multi-faceted and multi-layered
reality. The use of a single method would only partially reveal this reality. Hence
the need to combine methods and provide better opportunities to answer the
research questions, and allow a better evaluation of the extent to which the
research findings can be trusted and inferences made from them, as suggested
by Tashakkori and Teddlie (2003). The investment in social media appeals to
both quantitative and qualitative motives.
Qualitative data, defined as “non-numerical data or data that have not been
quantified” (Saunders, 2009) appeared to be more relevant to this project than
quantitative data. First it better suits the chosen research philosophy. Then, it
enables a tailored data collection and a deeper understanding of the context
and the various variables involved. The use of the above-mentioned
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triangulation technique has facilitated the validation of these data, combining
both secondary and primary sources.
2.4.2. Secondary data
Secondary data has strongly supported the development of this research, and
has contributed to the assessment of its relevance.
Academic sources
Academic literature on ROI is numerous and diverse, and the challenge
consists in identifying the most relevant pieces of work available. A critical
review of each selected source of information has been necessary due to the
fast development of social media and pace of technology changes.
Technological changes, shifts and upcoming issues may not be considered in
some articles written even a few months ago.
Reliability issues
Due to the nature of social media, belonging to the digital world and the fast
evolution of the sector, most of the information has been found online or in
public literature. Many of these sources are less reliable and credible than
academic journals or industry reports: this has justified further caution in their
use within this project.
Questionnaire design
To design the semi structured questionnaire used to interview opinion leaders
or prominent experts of social media, all the information gathered from
secondary sources (mentioned above) has been used. It has also involved a
critical literature review, involving further personal thinking and analysis on the
existing literature and schools of thought.
2.4.3. Secondary data contribution to qualitative primary data gathering
Secondary data has been used as a basis to design of the questionnaire used
to guide the semi-structured interviews, and minimize the gathering of already
existing information.
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The critical literature review, involving further personal thinking and analysis on
the existing literature and schools of thought, has supported the development of
the questionnaires, in order to optimize both the creation of a real contribution
to the topic, and the gathering of “tailor-made” data, directly related to the
specific research topic.
2.4.4. Semi-structured interviews
In order to investigate the current practices of an evolving phenomenon, it was
necessary to hear the opinion of the leaders of the sector who are decisive
actors and direct contributors to the shaping of the sector’s future. Despite their
numerous interviews in many publications in the world, a direct interview
appeared to be essential to cover the exact scope of the research and hopefully
gather new or different thoughts and opinions.
Process
A list of potential respondent profiles had been developed. This target included
various stakeholders of the sector, which would enable to reassemble ideas
from different point of views. The variety of selected profiles was a prerequisite,
in order to ensure that relative global objectivity through a balance of different
and sometimes contrasted opinions and findings.
A semi-structured interview guide has been designed and tested ensuring that
the questions asked would lead to the aspects needed to contribute to this
project. The tests have emphasized the necessity to broaden the questions to
make sure that each interview would stimulate and give space to all the
important thoughts and opinions had to share.
As each respondent has a specific role in the social media sector, the guide
seemed to be too broad and not-specific enough to clearly bring results and
contribute to the project.
The final questionnaire has been sent and answered via email, and then
completed by either a phone call or a meeting when physically possible.
Addressing interviews issues and limitations
25
As a qualitative approach, the selection of respondents has been established to
ensure that all the different opinions about the research would emerge. Hence
the variety of backgrounds, positions, philosophies and points of view.
Respondents are not representative of the sector and needed not to be. As one
of the objectives of this research is to propose new practice rules and a set of
metrics to optimise social media performance measurement, they have proved
to be the appropriate people to bring new insights and ideas, set standards and
put this topic into perspective, through a more holistic approach.
The selection of the respondents could bear some bias as:
• They are the most savvy and knowledgeable in this topic, therefore more
than any other stakeholder.
• They have all been involved and established in the field for a long time,
and therefore demonstrate high maturity levels and approaches which
may not be representative of the other stakeholders.
• The respondents are not the people who have directly invested in social
media, such as companies who spend drastically.
In order to limit the bias, which could arise from the proximity of the interviewer
with the investigation, all questionnaire answers have been received by mail.
Only after this phase was there a contact (oral or direct) with the respondent
used as an opportunity to explain or complement the written answers.
Overall suitability of this method
The initial doubt on the feasibility to obtain a direct contact with key experts and
leaders has been quickly dispelled. Most of the top experts and leaders related
to this research subject have been keen to participate. As an indicative
example, Jim Sterne, author of “Social Media Metrics: How to measure and
optimize your marketing investment” and founder of the world-respected Web
Analytics Association, accepted to both respond to my questions via email and
discuss them further over the phone. This high quality of contributors has highly
contributed to the purpose of this project, understand a growing and somewhat
unexplained phenomenon.
This qualitative approach to research through interviews has proved relevant
and efficient, bringing new ideas and areas of thinking and leading to findings.
26
Statistics have been used to identify or confirm trends, complement some
qualitative findings and validate some generalizations.
However, it needs to be said that the fast pace of the evolution of the sector
creates the necessity to monitor the trends and possibly amend some aspects
of the generalizations made.
The questions discussed during the semi-structured interviews have been
included in the appendix one.
3. Findings/results
Below are the main findings of the research.
3.1. Measuring social media performance
3.1.1. Discriminating key terms
There are different opinions regarding social media performance measurement.
In their publications, professionals such as Jim Sterne and Olivier Blanchard
assert that it is possible to measure social media performance. However, as
mentioned by Jim Sterne, “it’s never black or white”.
Andrew Walker, consultant and Web analytics and Digital marketing professor,
outlines that “it is zeros and ones, data is recorded automatically: something
can be measured”. However, along with Sterne who warns that “it’s never black
or white”, he points out the immaturity of this very new sector, requiring new
approaches and therefore bringing new issues. Marshall Sponder, web
analytics specialist and author of “Social Media Analytics”, joins Walker on the
fact that something can be measured. However, when it comes to the possibility
of measuring social media performance, he says:
“I have taken both points of view at different times -
constantly mentioning the lack of standards and
accountability while also stating we could measure
anything, to our heart's content, if we really wanted
to.”
Sponder, 2011
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Regarding the lack of standards affecting accountability, Walker adds that
“standards will start to emerge, just as the Web Analytics Association (WAA) did
for basic web analytics”.
Jim Sterne, founder and president of the WAA, in his first interview for this
research, questions the terms used:
“Accuracy, accountability and usefulness are all very different. I cannot
accurately measure the population of a country but I can be accountable for the
usefulness of the census. We have to admit that there is no true accuracy in
measuring anything - even your height. You are taller in the morning than at
night and your height is affected by temperature and humidity. Not to mention
how you stand and whether you washed your hair that day.” Jim Sterne, Author
of Social Media Metrics (September 1st, 2011).
Sterne suggests to take accuracy “off the table”, and emphasizes the
importance of measuring social interactions consistently, in order to “be able to
compare and contrast and come up with an analysis that is useful to the
business”.
A review of current measurement practices may prove to be necessary.
Following this idea of discriminating the basics, Walker insists on the
importance of differentiating data, information and knowledge.
3.1.2. Managing data
As far as data is concerned, Walker suggests that “many problems will be
resolved as data will become increasingly accurate”. To illustrate this point, he
uses an example of the traditional web, where companies have captured large
amounts of accurate data about their visitors. This has led their online
advertising to be micro-targeted. “The level of targeting is becoming
increasingly accurate, none of this would have never happened in the nineties.
Data standards have never been better, and industry specific standards are
currently being developed”.
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Regarding accountability, although financial people are mainly dealing with
numbers and quantitative indicators, some companies start to quantify
qualitative data.
Walker mentions the emergence intellectual capital (IC) accounts, aiming at
producing some sort of P&L of knowledge in the organisations.
“The term intellectual capital conventionally refers to the difference in value
between tangible assets (physical and financial) and market value” (Magrassi,
2002).
More specifically, a new component has emerged in this IC account: relational
capital.
“It consists of more identifiable items such as trademarks, licences, franchises,
but also the less definable, such as customer interactions and relationships”
(Skyrme, 1998).
Walker expects social media to start appearing on this IC report soon.
3.2. Social media and ROI
3.2.1. The nature of the relationship
How to relate social media and ROI that are, as we have seen, inherently
different?
Sponder insists on the fact that “Social Media ROI has to be defined in-terms of
social media, not brick and mortar stores, as it has been. Much of social media
doesn't not fit into (yet) a financial transaction and any value you put on a
"friend" or "fan" is subjective, and probably biased to some outcome.”
Walker indicates a “non existing relationship between social media and ROI”,
insisting on the fact that “we are not there, the relationship is not established
yet. It is currently impossible to prove ROI.” As pointed out by Sponder, Walker
asks: “Pepsi has 22 million fans on Facebook, what is their value?”.
To support the idea of a “non-relationship” Walker also uses Skype’s example.
Microsoft bought Skype for $8.5 billion, valuing the company almost 10 times its
$ 860 million turnover in 2010.
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Walker gives a final conclusion by saying that “ROI and social media are not
currently connected”.
These points confirm the main challenges about measuring the efficiency of
social media investment.
3.2.2. The main issues
3.2.2.1. Redefinition of values
Sponder introduces the idea that “the real issue […] is not ROI, but the lack of
any currency value around social media, itself. People keep wanting to treat it
as if it has financial value (which it does) but you can't trade your friends in for
dinner (or can you - I suppose with Influence, you could - but it's not something
you can bank on). So at the end of the day, we need to call ROI something else
for Social Media, or just redefine it so that it makes sense with Social Media, as
it doesn't, now.”
Sponder then warns:
“The main challenges are [that] we have no way to universally agree on the
value of friends, fans, followers, likes, votes, and so on, at any point in time.
Also, we don't have the right formulas to calculate the values, they need to be
re-written for social media - perhaps not all formulas can be universally applied
everywhere.”
This difficulty to find common evaluation and measure is exacerbated by “Fear”
and its consequences, a concept brought up by David Meerman-Scott,
marketing strategist and author of “The New Rules of Marketing and PR”.
He argues that « the insistence on old school ROI measures is fear based» and
says:
« Every day, I run across FEAR of marketing on the Web.
- Fear comes from bosses who insist on calculating the ROI of the new
rules of marketing & PR based on sales leads and press clippings.
- Fear comes from offline advertising and PR practitioners cautiously
making the transition to Web platforms to generate attention.
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- Fear comes from those who insist on copying the competition. »
Sterne also joins Meerman-Scott’s point of view, outlining that “people consider
the Internet in terms of TV and eyeballs”.
3.2.2.2. Consistency
Consistency appears to be a crucial element, and Sterne seems to be the first
advocate when he says:
“The main challenge to measuring the ROI of a social media campaign is to
make it consistent with other campaigns you are measuring and consistent over
time”. He points out the necessity for companies to standardize internally and
not change tools in order to remain consistent. He also recommends the use of
tools such as Hitwise and Comscore as generic measurements or external
benchmarks.
3.2.2.3. Defining standards
Most agree that the definition of both internal and industry standards appears to
be one of the main challenges for successful social media performance
measurement. Walker says that “defining standards will prove very hard”, using
the “bounce rate” example: several definitions are available for this single
simple metric.
3.2.2.5. Resources
Another issue is the recruitment of the right people in the role: it can currently
prove hard to find people who understand the different tools available and are
able to analyze, and it is especially harder for social media.
Human resources do not have any job spec yet to recruit the right people, and
usually do not have the skills to identify the challenges related to the position.
In terms of skills, Walker asks: “how many people could realistically consider
the range of tools and decide which one is the best?”
Walker also quotes Kaushik, using the Pareto principle, stating that 20% of the
budget should be allocated to the software, and 80% to the people. Finding the
right people then appears to be a major challenge.
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3.3. Level of irrationality
Is there a social media bubble that would evidence a certain level of
irrationality?
Respondents’ opinions diverged. While Sterne affirms that “we are definitely in
a social media investment bubble”, David Meerman-Scott is more radical,
stating that “the idea of ‘bubble’ is ridiculous”.
Sterne argues that “investors are still enthralled by the large numbers of users
[that] any given social platform claims, without enough consideration of the
business model”. About the bubble effect on social media performance
measurement, he mentions that it is “a very different subject from a company's
ability to measure the marketing and customer service value of using social
media. If the bubble bursts, will it impact the credibility of social media as a
business tool? Sure... but only for a while. Then, those who are doing it well will
continue to prove its value. Just as online advertising did after the Dot Bomb in
March, 2000.”
Sponder also thinks “there is a "bubble" around Social Media. But [...]
as business starts incorporating social media measurement into business
practice, [he feels that] the bubble will eventually dissolve - but [he doesn't] think
it will !pop"! “.
In the same way, Sterne suggests that the social media investment is based on
uncertain figures which makes it hazardous for businesses.
On the other hand, David Meerman-Scott has a quite opposite opinion when he
states that “the idea of a ‘bubble’ is ridiculous. We are in the middle of a
communications revolution. Was there a telephone bubble? A radio bubble? A
television bubble? No.”
Walker is more moderate regarding the idea of a bubble. “Social media is too
small to be a bubble: it is not a bubble from an economic perspective”.
In the 1990s, e-commerce was a bubble, there has been massive investment
and then it crashed. Unlike this Dot Com bubble, Walker says in his own words,
“social media is easy to screw up and cheap! How can you loose money in
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something that doesn’t cost anything?” He finally summarizes mentioning “a lot
of hype and nonsense talk around social media”.
Instead of a bubble burst, Walker forecasts that “there will definitely be a
backlash, but it might help to measure ROI, as it may force people to find
metrics to measure performance and justify the investment”.
Sponder moderates and reconciles the diversity of opinions when he says that
“as platforms mature and evolve and as people become more articulate about
what they want out of Social Media, the practice of it will become more precise
and standards will evolve (in the next 5 years or so).”
Would this bubble have a direct impact on measurement?
Sponder also thinks that the bubble affects measurement, but “again, as
people/users of this information and the platforms, become more sophisticated,
they will demand more and more accuracy from the SMM platforms and this will
lead to more creditable Social Media Analytics and measurement.”
Good practice rules and metrics have also been discussed with the
respondents, but will be contextualised and put into perspective in the next
section, in order to increase their relevance in the context of this research.
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4. Analysis and discussion of findings
Secondary research and primary research as well, have shown a widely shared
conviction that it is a necessity to measure social media performance and ROI.
Unanimity can also be observed on the current difficulty of achieving the
measurement and the fact that no realistic and exploitable solution is available
for the corporate world yet. As previously pointed out, without the ability to
measure ROI, success or failure cannot be assessed, strategic planning
becomes hazardous and the budget allocation to the most efficient and
profitable actions is not possible.
4.1. Context of social media and ROI
In order to analyse the relationship between social media and ROI, it is
necessary to explore the characteristics of each universe and their exigencies.
4.1.1. Cultural aspect
4.1.1.1. Clash of cultures
Within the organisations, two cultures coexist. One, led by finance people, relies
on figures and monetary evaluations or indicators. The other one, led by
marketing is concerned with human behaviour, reputation or awareness, which
figures cannot satisfactorily measure. As brought up in the literature review:
how would you measure the performance of a PR event?
This incapacity to quantify return in absolute numbers has turned the marketing
budget into an expense rather than an investment.
This uncovers the issue of quantification of intangible and qualitative data that
has been addressed in both the secondary and primary research.
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More specifically, intellectual capital, which has come out from primary
research, is a tentative to measure knowledge within organisations. The
concept was refined by the introduction of the concept of “relational capital”,
which values items such as customer interactions and relationships.
According to the findings of this project, we can expect social media activity to
become part of the intellectual capital, and soon be integrated in corporate
reports.
In order for this to happen soon, some issues described below will need to be
overcome.
4.1.1.2. Organizational culture
The organizational culture is an important barrier to social media measurement.
First, it is still difficult and slow to introduce a digital (and even social) culture
within companies, because of the current traditional managerial behaviours and
visions. This may change owing to the increasing number of digital natives, who
will integrate it more easily than digital immigrants (Prensky, 2001). As an
example, the companies (especially HR) may face difficulties while recruiting
web analyst profiles, because of the technical specificities of the position and
the risk of not being able to optimize their competencies and keep them
motivated.
Language, as discussed during primary research is another barrier to social
media analytics acceptance, as the jargon related to the field may not be
understood by the different employees.
In conclusion, most companies have a low maturity in terms of traditional web
analytics, which can obstruct social media performance measurement.
4.1.2. Data
4.1.2.1. Lack of uniformity
All data gathered on social media platforms is quite heterogeneous. The
source, the data collection method and the diversity of tools used give a
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disparate and incomplete description. This affects the capability to identify
consumer behaviour towards brands or products, adding to the complexity of
social media performance measurement.
As said before, some analysts like Walker point out the necessity to differentiate
data, information and knowledge.
4.1.2.2. Lack of accuracy
Visitors’ data captured on social media platforms is neither comprehensive nor
accurate.
As evidenced in the findings, traditional web has reached an acceptable level of
accuracy, which now enables to achieve personalised micro targeting (e.g.
display campaigns) and measure both its pertinence and efficiency. Social
media needs to follow the same path by gradually increasing both accuracy and
pertinence of the data that needs to be analysed.
The respondents interviewed show a real concern about accuracy, which does
not appear in the literature review, which rather undermines it.
4.1.2.3. Lack of consistency
As discussed, the attempts to measure social media performance to date are
not satisfactory, and this incapacity pushes the business executives to
permanently try new alternatives. The profusion of technological offers and
platforms adds to this phenomenon, creating inconsistency.
Aggregation of data can also sit under consistency. In order to be consistent
and meaningful, the disparate tools and data collection methods require
aggregation.
This inconsistency is exacerbated by the lack of widely accepted standards in
the industry.
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4.1.3. Tools and technology
Technology and marketing are becoming inseparable and the prediction of
Marshall McLuhan “the medium is the message” (McLuhan, 1964) has become
a reality: the medium has a determining effect on our perception of the
message.
Technology evolves at a fast pace, especially in social media. The main issue in
this field is that the coveted data cannot be captured yet.
First, because the complexity and subtleties of social interactions need to be
interpreted.
Then, because the actual impact on sales or other traditional KPIs cannot be
proven yet.
The existing monitoring tools demonstrate limitations, as qualitative content still
requires human analysis and appreciation in order to be useable. This
increases the importance of the role of the analyst, which as said before, is one
of the biggest challenges for successful social media performance
measurement. This also prevents generalization of both reporting and
monitoring.
Last but not least, the disparity of the tools usually makes aggregation more
difficult. The data gathered cannot be used and transferred from one tool to
another, and thus does not interoperate on different platforms or allow any
synthesis. The emergence of industry standards would help to solve this issue.
4.1.4. Motives and objectives
As discussed in the literature review and findings, corporate executives invest in
social media for the following reasons: increase brand value, increase sales,
increase customer satisfaction or reduce marketing expenses.
The existing literature insists on the fact that this incapacity prevents from any
ROI calculation.
37
Motives then result relatively clear in the executives’ mind. However, when it
comes to setting specific objectives, the impossibility to measure affect their
capability to set objectives following the SMART.
4.2. The issue of irrationality
4.2.1. Lack of accountability
Data accountability is another major issue. What do figures mean? Are they the
only way to express reliability? “Accounting is not a business analysis tool” says
Sterne, “and all figures contain a certain degree of assumption”. This is even
truer when it comes to social media, where figures are either absent or apply to
marginal features.
As a consequence, there is a risk: this lack of accountability can be a source of
interpretation, or irrational behaviours.
4.2.2. Craze
In spite of this lack of rationality, companies significantly increase their presence
and investment in social media, and this phenomenon keeps on growing. As
discussed in the literature review, most executives (84%) cannot justify this
investment by measuring return and seem to be more motivated by responding
to competitor’s presence. This lack of objective reasons confirms the high
degree of irrationality in social media.
4.2.3. Bubble
This trend for irrationality has struck the evaluation of the businesses involved
in social media.
The current overstated evaluations have brought the idea of a potential social
media bubble. As the existing literature remains rather silent on this idea, the
subject has been raised with the respondents during the primary research
interviews. They have expressed contrasted opinions about the idea of a
bubble. In summary, there are two main schools of thought.
38
One, led by David Meerman-Scott categorically denies the existence of a
bubble. He prefers to rationalize, and identifies fear as the main cause of
irrationality.
Arguments refer to the small size of social media, the investments are not as
massive as they were in the e-commerce in the nineties. While recognizing
certain irrationality around social media, Walker asks “how can you loose
money in something that doesn’t cost anything?”
Those who advocate the existence of a bubble explain it by a fascination
towards the phenomenon and the large number of users, which leads to a
biased correlation between the real economics of the company and the
investment.
A more moderated approach brings up the idea that the bubble will not pop, as
the introduction of measurements will eventually dissolve it.
Both opinions ultimately converge in saying that whether it bursts or not, metrics
will be found to measure and justify the investment.
However, an irrational phenomenon is occurring: it is currently impossible to
justify recent sales prices of Skype or LinkedIn, which are valued ten times their
annual turnover. From these two examples, Walker sees a confirmation that
ROI and social media are not currently connected.
Isn’t this disjunction between ROI and value an evidence of the existence a
bubble?
4.3. Metrics and measurement
4.3.1. Use of metrics
Social media metrics are currently facing the same challenges as web analytics
in their early days.
Actionable metrics have emerged with web analytics, enabling to measure
online activity and analyse behaviour on websites. Although most companies
39
still remain relatively immature in the field, the acceptance of their importance is
increasing, and they start to be integrated in performance measurement.
As an example, these metrics have become essential to assess the
effectiveness and ROI of any digital campaign, and are now reckoned as a very
useful tool.
However, in social media, the metrics are currently not performing or acceptable
for most businesses. The nature of social media is not compatible and does not
comply with existing data capture techniques and processes, and “values”
constituting the actionable metrics are not clearly defined and standardized yet.
The current technological and monitoring tools do not provide complete analysis
and reports because the qualitative aspect of social interactions that cannot be
addressed by algorithms.
In order to get round those difficulties, ROI metrics need to be redefined for
social media. As brought up during primary research, “the real issue is the lack
of any currency value around social media. ROI needs to be called something
else for social media” suggests Sponder.
4.3.2. Lack of standards
As a consequence of the impossibility to measure social media performance,
the industry has not been able to establish widely accepted standards that
could become the norm yet. The same applies at the company level: internally.
This lack of industry wide standards directly impacts on performance
measurement.
In the technological field, platforms are quite diverse but no one is universally
appreciated and accepted.
Due to this absence of shared standards, external benchmarking is not feasible
since a company cannot compare its social media performance against its
competitors
As an example, how would you define the value of a fan? As suggested in
primary research, the value of a fan is currently not determined, and cannot be
universally fixed, as it depends on objectives of the company’s presence on
social media.
40
Internal standards definition would enable companies to remain consistent, and
thus optimize social media performance measurement over time as well as from
one campaign to another.
Last but not least, metrics cannot be determined in isolation from specific
objectives: businesses first have to define what they want to achieve in order to
decide on what they should be measuring.
4.3.3. Frameworks
In order to structure and optimize ROI calculation, frameworks have been
designed. For social media, as shown in the literature review, focus has been
placed on two major frameworks: Owyang’s and Pentin’s.
One of the purposes of this project is to propose a framework that addresses
the current challenges of the industry and responds to the limitations of existing
frameworks.
Owyang’s framework appears to be theoretically advanced and very sound. It
provides good practice rules and qualitative. However, in the context of this
project, it shows a lack of hard financials, which affects its relevance. Moreover,
it may be quite complex and technical for most companies who have a low
maturity.
The IAB framework also presented in the literature review provides good
practice rules and successfully combines hard financials with soft metrics,
making it appropriate to most companies.
It also adapts the metrics to each social media platform. However, it does not
solve the issue of consistency of data from one platform to another, and thus
prevents integration of results.
These limitations of existing frameworks justify the development of a new
framework that integrates the findings of this research. Recommendations will
be presented in the next chapter.
41
4.3.4. Best practices
Walker and Sponder expect the field to mature, leading to further interest for
traditional web analytics. Sponder outlines that further accountability and
acceptance is “only a matter of time - hopefully, sooner than later. Standards
would help (…) I would say, as we nail down social media Analytics, best
practices and eventually, standards, will come into being - with them, will come
accountability and the ability to meaningfully compare platforms and
perhaps, inter-operate with the data. Today, that's not yet the case.”
On the other hand, Sterne argues that the qualitative element can be
quantitatively assessed. “Qualitative metrics can be quantitatively used to make
business decisions about anything. In government, it's called Polling. In
customer service, it's Customer Satisfaction. In social media, it's a combination
of attention, engagement and polarity.” However, as developed above, these
qualitative measures will still be controversial in terms of pure accountability,
and should be balanced with hard financials as well as perspective on existing
practices (e.g. accounting): “there is no such thing as hard numbers (…) It’s
never black or white” (Sterne, 2011)
I believe that the existing lack of accountability will remain for a few more years,
but in the end, data capture will enable the collection of the necessary data,
standards will emerge, organizational culture will partially shift to digital and
social media metrics will be better accepted, despite their qualitative aspect.
The qualitative data will be further quantified, and better accepted, it is only a
matter of time. (Sponder, 2011)
I believe the emergence of standards will optimize consistency, and
comparability, which are key elements of successful performance
measurement.
42
Recommendations and conclusion During this project, it has been highlighted that the current tools available
cannot overcome all the obstacles to social media performance measurement
and ROI calculation. Until consistent and universally accepted standards
emerge and adequate technological solutions are designed, the best immediate
orientation is probably to optimize the use of available tools and practices.
This process involves the selection of the most efficient and appropriate ones,
in order to ensure that their mutual role will enable consistency and that their
association will create synergies.
This process could start by adapting the organizational culture towards further
digital maturity, reviewing the current processes, improving the quality and
reliability of the data captured, selecting the adequate the metrics and the best
technological tool available to each structure.
Ten good practice rules have been derived from the combination of secondary
and primary research findings. These good practice rules would enable
optimization of measurement, providing a solid structure that ensures
consistency and increases data and analysis reliability.
From all the above good practices to be used by the companies are
recommended below:
1. Invest in social media competence in marketing and analysis
Competence and expertise should represent 80% of the social media
investment versus 20% for the software.
2. Define specific goals for Social Media
Develop SMART goals specific for social media activities and actions.
3. Define accurate and consistent data to be collected
Data collected needs to be appropriate, accurate and consistent over time and
campaigns.
43
4. Do not measure social media in isolation
Basis for selecting items to be measure needs to encompass the holistic
context of social media
5. Define simple KPIs
Develop KPIs using the REAN framework and its four dimensions using a
combination of soft metrics and hard financials
6. Be consistent
Ensure consistency in time and tools when measuring performance of actions.
7. Avoid relying on simple metrics
Performance measurement should not rely on one simple metrics such as
“Number of Likes”, “Number of Fans” which give a biased image of a complex
situation.
8. Benchmark internally and externally
Benchmark social media performance should be benchmarked either
against:
- another time period – historical data
- other platforms which share similar objective (e.g. blogs, content sharing,
networking…°.
- other channels which share similar objectives (e.g. CRM, e-marketing,
sales promotions, public relations, advertising…)
- other competitors which share the similar objective on the same platform.
9. Use the “So What technique”
When designing or testing new measurement use the ‘so what’ technique to
determine if it actually brings value and pertinence to the evaluation process
10. Review business processes and organization
As you introduce new tools, new measurements, review existing business
processes and organization.
44
As suggested, it is necessary not to measure social media in isolation, to
combine experience from other fields and to identify complementarities. As an
example, a merge of web and social media data would support the production
of superior ROI metrics.
This systemic approach and cross-channel analysis would stimulate the
identification of new and unknown patterns and synergies. This illustrates why
the approach to social media metrics needs to be holistic. The interactions are
not restricted to simple relationships between users, they are also related to
one’s behaviour and relieving the message, possibly making its spreading viral.
Users are now enabled to have a maximized strike force. How to monitor and
measure this amplified word-of-mouth and its actual influences and
consequences?
Only a limited set of diverse indicators would provide an understanding of such
a complex reality. However, these metrics should be aligned with more
traditional ones such as the data captured by the POS or sCRM systems in
order to provide insights that would be more acceptable to executives.
These challenges have been closely addressed while developing the set of
metrics presented in the Table 1 (page 48). This framework has ensured that
hard financials (acceptable by the executives) are included and closely aligned
with soft metrics, initially more relevant to social media. The set of metrics is
based on Sterne’s main objectives for social media (2011), the REAN
framework (Jackson, 2009) and derived from Pentin’s framework (IAB, 2011).
The corresponding tools and their limitations have also been included in order
to ensure critical analysis.
Technology will keep on evolving, and so are the web analytics tools. Some
listening platforms already come up with ROI calculations. However, with
‘inaccurate’ data, social media performance measurement results inaccurate,
making the ROI calculation impossible. Technology will keep on being
dependent on the quality of data collected, and this is also true for social media
measurement.
45
With social media, users have been able to generate content. However, the
analysis of this content is still mostly keyword-based and so is the basis for
collecting social media data. Tools available (e.g. SM2 Alterian) will soon
improve the understanding of the user through further use of advance
intelligence and semantic analysis, but subtleties cannot be interpreted and
analysed by technology.
The absence of digital and social media culture is a decisive obstacle and the
degree of maturity of the organization is currently considered as the most
prominent factor to a successful measure of social media. This justifies a
rigorous and competent recruitment process and the permanent involvement of
the senior management.
Today, it can be stated that we are able to measure (only partially though) the
“what” (ie data that give a quantitative description of actions and behaviours).
The challenge for tomorrow is to explain the “why”. Why does a customer act
this way? Why is he a fan? Without this knowledge, social media will still remain
an unexplored field.
Considering the timeframe of this project, the three research objectives have
been met. Elements of answers have been provided through the collection of
unpublished information that has given further insight to this topic.
As this project only focuses on social media sites, it gets round of certain
issues, which may need to be considered while developing a larger framework.
The two hypotheses have been confirmed to some extent.
It is possible to measure social media performance, as pointed out by Walker,
“something can be measured” (2011). Many barriers intervene in social media
performance measurement, but combination of both soft metrics and hard
financials enable to provide a holistic approach and consistent monitoring that
enable performance measurement over time.
Considering the above, there is definitely irrationality around social media
investment. It is not related to the Dot Com bubble, but rather to a social media
46
bubble, considering that there is one. It seems to be early to mention an
investment bubble in financial terms, but there is surely a genuine social and
engagement bubble that is worth investigating further. Investments should keep
increasing over the next years, and may reach unexpected levels, but it is
premature to talk about an economic bubble, with all the characteristics
involved. The ‘bubble’ may not ‘pop’ but rather dissolve with further maturity,
leading to the emergence of new metrics and new performance measurement
approaches, practices and tools.
In summary, it is possible to measure social media performance, and the
framework recommended can be a support to new metrics acceptance and
recognition. An investment in social media may never produce figures that are
identical to those of a financial statement, and this will probably remain so in the
next years.
The obsolescence or inefficiency of the current metrics does not mean that
there is no return. The challenge is the corporate capacity to accept other types
of measurements abandoning the traditional opposition between quantity and
quality.
As for all the deep shifts required, they will take a few years to be widely
accepted and implemented.
Social media’s role within companies may also evolve and revolutionize internal
functions such as internal communications and social relationships.
Today, the relationship between ROI and social media still needs to be defined
and built. As suggested by Sponder (2011), “at the end of the day, we need to
call ROI something else for Social Media, or just redefine it so that it makes
sense with Social Media, as it doesn't, now.”
Social media do represent a considerable progress for the exchange, the
information and communications around the world. Obviously, the business
world will be increasingly present and involved in this universe. However, the
difference in culture and values will force the companies to adapt their
47
organizations, tools and practices in order to optimize their presence and design
new criteria for performance evaluation. This project has the objective to be a
contribution to this challenge.
48
Table 1: Set of metrics * GATC: Google Analytics Tracking Code
Objective KPI (derived from REAN) Soft metrics Hard financials Benchmark Tool used Limitation
Increase the number of fans
- Number of monthly active users - Proportion of active users / total users
- Cost per unique fan/visitor - Cost per impression
- Other channels - Other platforms - Competitors’ pages
-Facebook Insights - Hitwise, Comscore
- Lack of hard financials - Comparability of data from different sources - Datacapture methods limitations (e.g. reliability)
Expand the audience
geographically
- Users’ provenance
- Visitors coming from any social media platform provenance
- Sales records by country - Historical data
- External benchmark (competitors)
insights - GATC* - Sales records - Hitwise, Comscore, Alexa
- Lack of hard financials
- Comparability of data from different sources - Datacapture methods limitations (e.g. reliability)
Increase the number of positive mentions
- Number of positive mentions - Proportion of positive mentions/all mentions
- Cost per engagement - Cost per referral
- Historical data - Facebook insights - SM2, Audience
- Comparability of data from different sources - Datacapture methods
limitations (e.g. reliability) Engage
Increase the engagement
Post feedback - Patronage - Historical - Other platforms
- Facebook insights
- Datacapture methods limitations (e.g. reliability)
- Content shared - Size of final audience reached
- Cost per lead (share) - Cost per impression
- Historical data - Facebook insights - GATC*
- Datacapture methods limitations (e.g. reliability)
- Incremental traffic generated on site
- Cost per lead - Historical data - Other platforms
- GATC* - Datacapture methods limitations (e.g. reliability)
- Incremental number of fans generated
- Cost per acquisition - Other platforms - Other channels - Historical data
- Facebook insights
- Datacapture methods limitations (e.g. reliability)
- Total audience reached - Cost per impression (final audience)
- Facebook insights
- Datacapture methods limitations (e.g. reliability)
Activate Content shared
- Incremental audience reached
- Cost per impression (final incremental audience)
- Historical data - Other platforms/channels - External
- Facebook insights - SM2 - Competitive intelligence tools
- Comparability of data from different sources - Datacapture methods limitations (e.g. reliability)
Purchase intentions - Number of unique visits on store
locator - Action: add to basket
- Conversion rate to sales
of these intentions - Historical data - GATC*
- Lack of trackability
Revenue
generation Engage
Coupons /Promotional codes
- Coupons downloads - Coupons downloads/promotional code uses vs number of - Impressions of coupons/promotional codes
- Incremental sales value generated by coupons/promotional codes - Cost per incremental sale
- Other platforms/channels - Other sales promotion campaigns
- Sales records - Facebook insights - GATC*
- Comparability of data from different sources - Datacapture methods limitations (e.g. reliability)
48
Table 1: Set of metrics (part 2) * GATC: Google Analytics Tracking Code
Objective KPI (derived from REAN) Soft metrics Hard financials Benchmark Tool Limitation
Incremental sales value generated
- Income - Expenses - Customer satisfaction
- Incremental sales value - Cost per incremental sale
- Historical - External (industry, competitors) - Other channels campaigns
- Sales records - Hitwise, Comscore, Alexa - GATC*
- Comparability of data from different sources - Datacapture methods limitations (e.g. reliability)
Convert Facebook fans into new customers
Conversion rate to sales of Facebook fans
Frequency of purchase - Historical - Other channels
- GATC* - Sales records
- Datacapture methods limitations (e.g. reliability)
Revenue generation
Activate
Conversion rate to sales of new visitors coming from any social media platform
- Conversion rate to sales of new visitors coming from any social media platform
- Average basket value - Incremental sales value generated
Other campaigns Historical
- GATC* - Market research (eg coupon)
- Datacapture methods limitations (e.g. reliability)
Reach Incremental audience - Number of impressions - Incremental audience reached through content sharing
- Cost per acquisition - Cost per impression
- Other campaigns - Other channels - Historical - External (industry)
- Facebook insights - Sales records
- Datacapture methods limitations (e.g. reliability)
Activate/Nurture Customer service standards
- Number of calls from social media users (use a differentiator) vs other channels
- Requests solved on social media platforms - Requests solved on social media platforms through other users
- Cost reduction in customer centres
- Historical data - Other platforms - Other channels
- P&L - Call records
- Trackability - Lack of hard financials
Conversion rate to repeated sales of new visitors coming from
any social media platform
- Repeated sales - Repeated visits
- Retention rates - Repeated sales of new visitors coming to social
media
- Historical data - Other channels/other
platforms
- GATC* - Sales records
- Datacapture methods limitations (e.g. reliability)
ActivateNurture
Repeated sales (loyal customers)
- Repeated sales of social media customers
- Incremental sales - Cost per incremental sale
- Historical data - Other channels/other platforms - External (industry)
- GATC* - Sales records
- Thresholds for loyalty to be clearly defined - Comparability of data from different sources - Datacapture methods limitations (e.g. reliability)
Cost reduction
Nurture Customer satisfaction - Positive vs negative mentions - Content shared
- Cost per referral
- Historical data - Other channels/other platforms
- GATC* - SM2, Audience - Facebook insights
- Comparability of data from different sources - Datacapture methods limitations (e.g. reliability)
50
Appendices
Appendix A: Semi-structured questionnaire
1. In your books and articles, you assert that it is possible to measure social media
performance. Others point out the current lack of accountability or accuracy of
data and metrics available. How do you react to their point of view?
2. Some argue that ROI and social media are opposed: ROI being mostly
quantitative and involving exact figures, whereas the qualitative aspect is key to
social media. In the current context, how would you describe the relationship
between social media and ROI?
According to you, what would be the main difficulties/challenges when it comes
to Social Media ROI calculation?
3. To some extent, there is certain irrationality around social media investment,
what do you think about the idea of a “social media bubble”?
Do you think it can affect the accuracy/credibility of ROI measurement?
4. Putting aside the different goals related to social media, which key good
practice rules would you recommend to use systematically, in order to optimise
social media ROI calculation?
5. If a company with a low level of maturity in social media and web analytics had
to choose 3 metrics to measure the incremental sales value generated, which
ones would you recommend?
6. Some monitoring tools already exist (Alterian SM2, Sysomos Audience), do you
think that they can be considered as an added value for social media
performance measurement?
7. Social Media are developing fast and with it, the necessity to measure
performance and return. Do you think this development will lead to further
accountability of social media engagement and investment, or on the other
hand, will qualitative metrics be better accepted, despite their certain lack of
financial accountability?
51
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