Internet Marketing

A dissertation submitted to The University of Manchester for the degree of Master of Science

in the Faculty of Engineering and Physical Sciences

2009

Ali Houshangi

School of Computer Science

Table of ContentsAbstract...........................................................................................................4Declaration......................................................................................................5Copyright Statement.......................................................................................6Acknowledgements.........................................................................................71. Introduction.................................................................................................82. Literature Review.....................................................................................12

2.1. Current Trends in Internet Marketing...............................................122.2. Electronic Markets and Electronic Hierarchies................................152.3. Electronic Marketplace Research......................................................172.4. Winner-Take-All in Networked Markets..........................................222.5. Using Power Curves to Assess Industry Dynamics..........................262.6. Summary...........................................................................................29

3. Research Problem.....................................................................................324. Research Methodology.............................................................................36

4.1. Nature of the Data Sources...............................................................364.1.1. Web Measurement Methodologies............................................374.1.2. Comparison of Measurement Techniques.................................404.1.3. Summary...................................................................................43

4.2. Statistical Distributions.....................................................................434.2.1. Normal Distributions.................................................................444.2.2. Power Laws...............................................................................45

4.3. Measures of Industry Concentration.................................................474.3.1. Concentration Ratio (CR)..........................................................474.3.2. Herfindahl-Hirschman Index (HHI)..........................................49

4.4. Framework for Data Analysis...........................................................505. Data Analysis............................................................................................52

5.1. Commercial Airlines...................................................................525.2. Consultancies...............................................................................575.3. Insurance......................................................................................625.4. Search Engines.............................................................................655.5. Social Networking Services.........................................................69

6. Discussion of the Results..........................................................................736.1. Case-by-case Analysis.......................................................................73

6.1.1. Commercial Airlines.................................................................736.1.2. Consultancies............................................................................766.1.3. Insurance...................................................................................786.1.4. Search Engines..........................................................................816.1.5. Social Networking Services......................................................83

6.2. Cross-industry Analysis....................................................................857. Conclusions ..............................................................................................88Appendix A: Industry Power Curves – Logarithmic Scale..........................93Appendix B: Consumer Behaviour Models..................................................96

1

List of TablesTable 1: Commercial Airlines Visits Share - 2009 Quarter 1.......................55Table 2: Consultancies Visits Share - 2009, Quarter 1.................................59Table 3: Insurance Visits Share - 2009 Quarter 1.........................................63Table 4: Search Engines Visits Share - 2009 Quarter 1................................66Table 5: Social Networks Visits Share - 2009 Quarter 1..............................70Table 6: Cross-industry Analysis - Summary: Characteristics, Results, and Measures of Industry Concentration.............................................................87

2

List of ChartsFigure 5.1: Commercial Airlines - top 4 market structure dynamics - 2008/2009.....................................................................................................56Figure 5.2: Commercial Airlines Power Curve - 2009, 1st Quarter.............57Figure 5.3: Consultancies - top 4 market structure dynamics – 2008/2009 – based on top 4 companies identified in 1st quarter of 2009 .........................60Figure 5.4: Consultancies - top 4 market structure dynamics - 2008/2009 - based on average market shares over 12 months..........................................60Figure 5.5: Consultancies Power Curve - 2009, 1st Quarter........................61Figure 5.6: Insurance – top 4 market structure dynamics - 2008/2009........64Figure 5.7: Insurance Power Curve - 2009, 1st Quarter...............................65Figure 5.8: Search Engines - top 4 market structure dynamics - 2008/2009...................................................................................................68Figure 5.9: Search Engines Power Curve - 2009, 1st Quarter......................69Figure 5.10: Social Networks - top 4 market structure dynamics - 2008/2009...................................................................................................71Figure 5.11: Social Networks Power Curve - 2009, 1st Quarter.................72Figure A1: Commercial Airlines Power Curve – Logarithmic Scale...........93Figure A2: Consultancies Power Curve – Logarithmic Scale......................93Figure A3: Insurance Power Curve – Logarithmic Scale.............................94Figure A4: Search Engines Power Curve – Logarithmic Scale....................94Figure A5: Social Networking Power Curve - Logarithmic Scale...............95

3

Abstract

Over the past decade, a growing number of companies, operating in various

industries, have been moving online, with an increasing proportion of their

revenues being generated there. The extensive use of the internet has had an

effect on many traditional markets, by influencing levels of competition

amongst firms and altering established market structures. This paper

addresses the broad problem of virtual market structures, through a case-by-

case inspection of a number of identified online markets, and by carrying

out cross-industry analyses. A range of statistical models and measurement

techniques are applied to industry data, gathered from online research

companies. Moreover, a number of factors influencing the structural

outcomes present in online industries are identified. The results are then

combined with relevant models to provide a synthesis of online trends and

theoretical developments, in an attempt to explain online market structures

in the UK. Subsequently a framework for analysing virtual markets is

developed, with potential implications for online marketing practices and

the analysis of economic factors related to market structure, such as

regulations and policies concerning market dominance.

4

Declaration

No portion of the work referred to in the dissertation has been submitted in

support of an application for another degree or qualification of this or any

other university or other institute of learning.

5

Copyright Statement

i. Copyright in text of this dissertation rests with the author. Copies (by any

process) either in full, or of extracts, may be made only in accordance with

instructions given by the author. Details may be obtained from the

appropriate Graduate Office. This page must form part of any such copies

made. Further copies (by any process) of copies made in accordance with

such instructions may not be made without the permission (in writing) of the

author.

ii. The ownership of any intellectual property rights which may be described

in this dissertation is vested in the University of Manchester, subject to any

prior agreement to the contrary, and may not be made available for use by

third parties without the written permission of the University, which will

prescribe the terms and conditions of any such agreement.

iii. Further information on the conditions under which disclosures and

exploitation may take place is available from the Head of the School of

Computer Science.

6

Acknowledgements

I would like to take this opportunity to thank my supervisor Professor

Christopher P. Holland for his time and invaluable feedback. I would also

like to thank Daniel King and his team from Hitwise UK for allowing me to

make use of their online industry data, without which the completion of this

project would not have been possible.

Finally I would like to thank my parents Sholeh and Hamid, my sister Saba,

and my friend Mike for their unwavering support and encouragement.

7

1. Introduction

In the late 1980's, scientists at The European Council for Nuclear Research

(CERN) were in the midst of preparations for one of the most inspired,

elaborate, and challenging projects they had ever embarked upon: The Large

Hadron Collider (finally completed in September 2008, but has since been

shut down due to a Helium leakage [1]). The World Wide Web, was then

proposed by Tim Berners-Lee, as a solution to a specific problem; to store,

manage, and keep track of the data collected from the LHC on multiple sites

across Europe. Twenty years on, and a myriad of applications have

transformed the web into a far more beneficial, complex, and effective tool

than its creators had ever imagined. See [2].

As the internet gained popularity and became more widespread, scientists

were not the only group benefiting from it any more. People and businesses

alike started using the Internet as a means to communicate with one another.

Towards the end of the 90's, the internet had become widely accessible. This

rise in usage was perceived by many as a niche in the market. There was

money to be made. Many internet firms sprung up. They put traditional

business models aside, and started providing free content and services on

the web, in an attempt to generate some revenue from advertising; this

eventually led to the “dotcom crash”. See [3]. 2003 saw a well-known

technical writer, Nicholas Carr, produce sound arguments encouraging

businesses to cut IT spending. In his highly-debated article “IT Doesn't

Matter” [4] he claimed that increasing levels of IT spending would not

necessarily help companies gain a competitive advantage. Low levels of

investment were threatening the future of the internet and IT spending.

8

In 2004 however, with the listing of Google on the stock market, a new

bubble was inflated. Google had come up with an innovative way of

targeting and placing text advertisements next to search results. Google's

success had validated many business models thought to have been flawed

after the dotcom crash. The markets started re-gaining their lost confidence.

“The only reason it had not worked the first time around, it was generally

agreed, was a shortage of broadband connections. The pursuit of eyeballs

began again, and a series of new Internet stars emerged: MySpace,

YouTube, Facebook. Each provided a free service in order to attract a large

audience that would then—at some unspecified point in the future—attract

large amounts of advertising revenue. It had worked for Google, after

all.”[5]

The internet, in its relatively brief history of existence, has found a way into

most - if not all - aspects of human life. At its most basic level of

functionality, it has enabled us to manage information, communicate, and be

part of an enormous global network, which otherwise would not have been

possible. The internet has allowed us to make huge strides in many areas of

science and technology. We can perform numerous commercial transactions

online, in a manner and speed incomparable to any other alternatives. The

internet's lurking presence can even be felt when we make highly personal

decisions such as “who to vote for in an upcoming election”. According to

the Pew Internet & American Life Project, a polling organisation, “Some

74% of internet users--representing 55% of the entire adult population--went

online in 2008 to get involved in the political process or to get news and

information about the [US] election .”[6]

9

Currently an estimated quarter of the world's population [7] and 70% of

British households [8] have access to the internet. The new US

administration is planning to make the internet available to every American

[9]. The British government too, has pledged to provide access to high-

speed (2Mbps) broadband in every household in Britain by 2012 [10]. The

increasing level of broadband penetration, despite the current economic

crisis, highlights the significance of the internet's broad reach, and presents a

unique opportunity to businesses and marketers. A survey of marketers from

around the world, conducted by McKinsey in 2007 [11] identified that by

2010 the majority of consumers in most industries (estimates vary from

industry to industry) are expected to find new products and services online,

and as many as a third are expected to purchase goods there. In a separate

survey “Understanding Online Shoppers in Europe”, also conducted by

McKinsey [12] , online sales in Europe are expected to grow, despite the

recession, and levels of growth seem to be increasing at the same rate as

levels of broadband penetration.

Over the past decade, a growing number of companies, operating in various

industries, have been moving online, with an increasing proportion of their

revenues being generated there. The extensive use of the internet has had an

effect on traditional markets, by influencing levels of competition amongst

firms and altering established market structures. These new market

structures have determined the success and failure of many businesses.

Furthermore, electronic trading has affected the way consumers approach

businesses and conduct transactions. While the traditional market structures

are a well-studied area in business and strategic management, virtual market

10

structures, due to their relative novelty, and the transient nature of some of

the technologies involved, have not enjoyed the same amount of attention.

This paper will address the broad problem of market structures on the web,

by carrying out a close inspection of a number of existing e-markets. A

number of factors influencing the structural outcomes present in online

industries have been identified. Subsequently a framework for analysing

such markets is developed. The presented framework has potential

implications for online marketing practices and the analysis of economic

factors related to market structure, such as regulations and policies

concerning market dominance.

This article is intended to be accessible to an audience of computer

scientists, business strategists, technology and management consultants,

managers, regulatory bodies, and hopefully a more general audience outside

these disciplines. A range of statistical models and measurement techniques

are applied to raw data gathered from online research companies. The

results are then combined with relevant theories and models to provide a

synthesis of online trends and theoretical developments across a number of

selected web industries in the UK.

The next chapter aims to present a conceptual background to this study and

identifies some relevant research themes and theories of interest. The

research questions are then formulated and presented according to these

identified themes.

11

2. Literature Review

This chapter aims to provide the reader with an overview of the relevant

literature required to understand some of the key aspects of this project. The

intention here is to give a brief summary of some relevant topics, related

approaches, and theories, in order to situate the project into a wider research

theme. The following are a selection of topics and excerpts chosen from a

range of academic and business literature, as well as a number of online

sources (see citations). Note that many other topics related to this project,

such as consumer behaviour models, were explored and presented in an

initial project report which took place at an earlier stage. However, in the

interest of brevity, some of these topics have been omitted from the main

body of this report. Interested readers may refer to the appendix for more

details.

2.1. Current Trends in Internet Marketing

“E-commerce involves buying and selling processes supported by electronic

means, primarily the Internet... E-marketing is the marketing side of e-

commerce. It consists of company efforts to communicate, promote, and sell

products and services over the internet.”[13] E-commerce and internet

marketing bring a number of benefits both to buyers and sellers of goods

and services. For the buyers, the web is a convenient, interactive,

immediate, and private medium in which they are able to perform

comparative shopping. Customers have greater access and selection and

won't need to deal with sales personnel, or go to a physical store. The sellers

also enjoy faster, more efficient transactions at lower costs, and may take

12

advantage of the numerous tools available for customer relationship

building.

“Although online commerce still represents less than six percent of all retail

sales, its growth and future prospects show that it has finally become as

established and mainstream as a trip to the local mall.”[14] Despite the

economic crisis we are currently facing, many internet research companies

have predicted a steady growth in e-business. According to Forrester

research “web businesses are certain to fare better than their offline

counterparts as consumers continue to shift their daily activities online.”[15]

Marketing on the web is carried out by “click-only” and “click-and-mortar”

marketers. Click-only companies have no presence in the physical market

and conduct all transactions online. Examples of click-only companies

include Amazon, Google, eBay, MSN, etc. “click-and-mortar” companies

are essentially traditional “brick-and-mortar” companies now extending

their operations by adding e-marketing. The four major e-marketing

domains consist of business to consumer (B2C), business to business (B2B),

consumer to consumer (C2C), and consumer to business (C2B). The four

major e-marketing domains from [16] are shown below.

Targeted to Consumers

Targeted to Businesses

Initiated by Business

B2C (business to consumer)

B2B (business to business)

Initiated by Consumer

C2C (consumer to consumer)

C2B (consumer to business)

According to a McKinsey survey of marketing executives carried out in

13

2007 “spending on digital advertising seems set to increase significantly.

Today, a third of the companies that advertise online are already spending

more than 10 percent of their advertising budgets there... Large companies

are the most digital. In four of the five major areas of marketing, a majority

of executives say that online tools are at least somewhat important for

companies in their industries. At least two-thirds of companies are using

these tools in all the areas they deem most important.”[11] Figure 2.1 shows

the results for this part of the survey. Evidence suggests that a large number

of highly successful companies (with a high concentration in Europe and in

the High Tech industry) make frequent use of online tools for all aspects of

marketing (service management, sales management, advertising, product

development, and pricing). The importance and levels of usage of these

tools vary from industry to industry.

The survey also finds that due to their efficiency compared to traditional

marketing methods, digital tools/techniques have become extremely

important in the whole range of marketing activities. It concludes that some

14

Figure 2.1: Usage and Importance of Digital Tools [11]

of the main reasons behind relatively low use of online tools in some

companies are the lack of capabilities to work with online tools in the

organisation with the other main reason being lack of access to high speed

broadband. Some other trends of interest identified in this article include:

• Many companies – with growing numbers – are spending a lot of

their advertising budgets online. With a huge increase in spending on

digital-advertising, in particular search advertisement, as well as

social networking, paid keyword searches, and branded

sponsorships.

• Spending on collaborative technologies and web 2.0 are

comparatively low, but this may be caused by the relatively cheap

price tag for these services. Web 2.0 is an umbrella term which refers

to a collection of technologies including blogs, collective

intelligence, mash-ups, web services, social networking, P2P

networking, RSS feeds, and podcasts, etc.

2.2. Electronic Markets and Electronic Hierarchies

In Neoclassical Economics, perfect competition is defined as a market

which has the following characteristics:

1. Many buyers/sellers

2. Homogeneous products

3. Low entry/exit barriers

4. Perfect information

5. Transactions are costless

15

In 1987, Malone et al. in [17], predicted the emergence of electronic

markets, and the conditions under which these are likely to take place. This

article argues that with information technologies being utilised to a greater

extent, the “electronic communication effect” will take place. Consequently

communication costs will be reduced, allowing larger amounts of data to be

transmitted in the same amount of time, tending towards perfect information

conditions.

The “electronic brokerage effect”, which takes place through the use of

centralised databases, can help connect many buyers to many suppliers. This

is also said to reduce the cost of finding alternative products and services,

particularly in “computer based markets”.

As traditional markets shift towards electronic ones, employing IT is said to

have another consequence termed the “electronic integration effect”. This

takes place when the process of information management in organisations is

changed, for example by interfacing between suppliers and procurers in the

supply chain, which due to information only being entered once helps avoid

human errors and saves time.

This influential paper signifies the effects of utilising IT and how electronic

markets can satisfy a number of perfect competition conditions, namely

perfect information, numerous buyers and suppliers, near-costless

transactions, and lowering entry and exit barriers. As we move towards

perfect information, caused by costless transactions and searches online, the

popularity, and in turn number of transactions taking place on a website start

to become dictated by the price of goods and services, and the availability of

alternatives presented.

16

Malone et al. conclude by saying “if our predictions are correct, we should

not expect the electronically interconnected world of tomorrow to be simply

a faster and more efficient version of the world we know today. we should

expect fundamental changes in how firms and markets organize the flow of

goods and services in our economy. Clearly more systematic empirical study

and more detailed formal analyses are needed to confirm these

predictions.”[18]

The ideas presented above, provide a mere sample of the arguments put

forward in this paper; a more in-depth analysis of which is beyond the scope

of this study. Interested readers can refer to [17].

2.3. Electronic Marketplace Research

Wang et al. in [19], present a comprehensive literature review of electronic

commerce research, consisting of 109 articles from 19 journals related to

this topic. They identify eight research themes, five methodologies, and six

categories of background theories, in an attempt to review the current status

of Electronic Marketplace (EM) research. They then go on to propose an

integrative framework of EM which concludes EM research is mainly

approached from three angles: information systems, inter-

organisational/social structures, and strategic management. This section

presents some of their findings. The eight research themes identified in this

paper are discussed below.

17

EM Success

Establishing reasons behind the success and failure of various EMs is by far

the most explored topic in EM research. Some of the reasons behind EM

success, as identified by this paper, include:

• EMs owned by established brand names and companies are more

likely to succeed than those owned by financial companies and

venture capitalists.

• Lenz et al. [20] have carried out a study of 248 European EMs and

conclude those with better partnering skills are more likely to

improve their competitive positions than others.

• A gradual expansion of service provision has been identified as an

appropriate strategy. In other words, market operators should focus

on their core competencies before providing a broader range of

services.

• Product/Service differentiation based on quality has been identified

as a profit maximisation strategy for many EMs.

• Other important factors contributing to EM success include internal

capabilities such as IT competencies, financial support behind EMs,

and strategic manipulation (early mover advantages).

EM Adoption

Some reasons for adopting various EMs by individuals and businesses

found in research literature have been presented, with the majority of these

18

falling into the following four categories:

• Performance Expectancy: the extent to which the EM aids users'

career development and performance at work.

• Effort Expectancy: the user's perceived level of ease when it comes

to utilising and navigating the EM. Suitable EM designs may lead to

lower effort expectancies.

• Institutional Expectancy: whether highly regarded individuals

believe the user should adopt the new system.

• Facilitating Conditions: the perceived level of support provided by

organisations or technology for the EM.

EM Design

EM design research has been grouped into two categories:

• Designs proposed for new types of e-markets, in particular in the

financial industry.

• Designs proposed to improve current e-markets, for example by

providing better privacy/security features in existing systems.

EM Impact

The research literature analysis found that studies on realised impacts of

EMs are normally empirically based, while those on potential impacts tend

to be more descriptive. Some of these effects are listed below:

19

• Economic benefits: transaction and inventory cost reductions, an

increase in the customer base, etc.

• Higher competition levels: due to lower costs and higher efficiency,

however, benefits buyers more than sellers.

• Adverse effects: collusion, information overload.

• Market structure alterations: [17], which was discussed in the

previous section, is an example research paper found in this area.

EM and Trust

The levels of trust of users of EM, is an important factor contributing to

their success and adoption rates. A number of studies have been carried out

to establish ways of measuring, building, and enhancing trust by EMs.

EM and SMEs

Some research has been directed at Small and medium sized companies

(SMEs), and their relation to EMs. SMEs seem to fare much better in

fragmented markets, than in concentrated ones, usually due to the high

technical investment levels required in highly concentrated industries.

Intelligent Agents in EMs

Intelligent agents attempt to search for trading partners and negotiate prices

on the internet, and therefore can replace or alleviate centralised EMs. This

research theme falls into a wider EM design category, but has been singled

20

out due to its perceived importance.

Overview of EMs

Research papers with a more diversified range of topics fall into this

research theme category. These studies are normally found to lack an

empirical base and tend to be more descriptive and general in nature.

Figure 2.2, extracted from [19], displays the number of research papers in

each identified research theme, grouped according to their research

methodology.

The conclusions and recommendations for further study in this paper call for

more scientific research to be made. It is claimed, however, that these efforts

may be restrained by the difficulty of collecting data. One potential solution

is the use of electronic data, although readers are forewarned that utilising

such data may raise questions of validity. The nature of such data sources

21

Figure 2.2: EM research methodology by theme [19]

will be discussed in the following chapters.

The authors of this comprehensive literature survey of Electronic Market

research make the following concluding remarks: “although many

researches about EMs have appeared in recent years, they are still not

mature. Many other issues are still not fully explored, and demand attention

from future research. In sum, the future research of EM should be conducted

in a way that’s grounded in both theories and field data.”[21]

2.4. Winner-Take-All in Networked Markets

“Winner-Take-All in Networked Markets” [22] is the title of an article by T.

Eisenmann , published by The Harvard Business School in 2007. In this

paper, Eisenmann introduces a novel perspective on “Networked Markets”,

and the likelihood of these markets to be dominated by one platform. The

following passages extracted from this article provide some definitions of

key concepts and terminology.

“A platform encompasses the components and rules employed by users

across most of their network transactions...two platforms are part of the

same networked market, if changing the cost to users affiliating with one

platform, influences the volume of transactions mediated by the second

platform...every platform facilitates user interaction on one, and only one,

network. Every platform-mediated network has one, and only one, platform

at its core...rival platforms employ non-compatible technologies...by these

definitions, Visa, MasterCard, and American Express are three different

networks, each served by a distinct platform. Together, the three networks

(along with Discover, Diners Club, and a few others) comprise a networked

22

market: the U.S. Credit card industry.”[23]

Before discussing the main ideas presented, it is worth noting that the focus

of this paper is on two-sided networked markets, which happen to be the

most common type; put differently, networked markets with two separate

user groups who always resume the same roles. For example the two sides

of the PDF network are readers and writers (document creators). Similarly,

the two sides of the Windows desktop O/S network are PC users and

application developers. Another noteworthy fact is that the main emphasis

here is on platform structures, which is related to the number of competing

platforms in a given networked market.

The following platform structure outcomes in networked markets have been

identified:

• With multi-homing: a networked market in which most users on at

least one side use multiple platforms. For example in the Online

Music Subscription industry, all music companies tend to support

various platforms (e.g. Napster, iTunes, etc.), in order to reach the

full range of consumers.

• With mono-homing: a networked market in which most users on at

least one side, use only one platform. Consumers in the video games

industry tend to pick one of the available platforms, be it the Xbox,

Play Station (PS), or any other available platforms.

• With mixed-mode homing: a networked market in which a

considerable number of users mono-home, and the rest multi-home.

Credit cards provide a suitable example for this, where a significant

23

portion of consumers stick with only one credit card, while others

choose to make use of many.

Clearly, these platform structure outcomes are present in the absence of a

Winner-Take-All (WTA) platform structure, which is defined as 'one in

which a single platform captures over 90% of the relevant networked

market'[24]. In other words, there are not that many significant rival

platforms for users to pick from in the first place. In such cases, we say

WTA outcomes have prevailed. The table below, extracted from this article,

provides a number of examples for various platform structure outcomes.

Eisenmann continues his assessment of networked markets, and their

platform structures, by identifying four factors which influence the chances

of a new networked market being dominated by a single platform. These

are:

24

Figure 2.3: Platform Structure Outcomes [22]

1. Natural Monopolies

2. Multi-homing costs

3. Network effects

4. Differentiated platform functionality

Many businesses take advantage of economies of scale, to minimise their

unit costs. This, typically, only works to the point where these benefits are

offset by diseconomies of scale, which are normally caused by large

overheads. 'Minimum Efficient Scale (MES) is the lowest level of output at

which it is possible to minimize a company's unit cost.'[25] Certain

industries have a particularly large MES in relation to the level of output or

the size of their mature market. This is, in order to maximise economies of

scale, and in turn, perform with maximum efficiency, particularly high

levels of output are required. These are called natural monopolies. The

railroads, postal delivery, and telephone services are all examples of natural

monopolies. This condition alone can determine WTA outcomes.

The remaining three factors are said to have a joint effect on determining

WTA outcomes:

Multi-homing costs refer to the expenses endured by users, both in a

monetary sense, and in a less tangible sense (such as inconveniences), when

they decide to affiliate with multiple platforms. For example in the video

games industry, a user may wish to use both an Xbox and a PS3, and

therefore incurs costs associated with both platforms. The odds for a

network being served by a single platform tend to increase, when the

(perceived) costs of multi-homing increase. Note that multi-homing costs

25

differ from switching costs, which are associated with leaving one platform

to use another. Although, in many cases, a positive correlation between the

two has been observed.

Network effects are said to be strong when users give high value to the

ability to interact and associate with many other users of the same platform.

Note that users of a platform exist on both sides, and wanting to interact

with many other users can simply indicate wanting to have multiple

transaction partners. For example, PC users may highly value the ability to

interact with many application providers. Strong network effects are

identified as a factor contributing to WTA outcomes.

Users are typically offered a number of different functionalities and

technical features by competing platforms. The PS for example, has

differentiated its platform (PS3) from other consoles, such as The Nintendo

Wii and Xbox 360, by offering blu-ray. This is a transaction-specific point

of differentiation. Rival platform Wii, on the other hand, has set a generic

point of differentiation with its motion-detecting wireless controllers. The

likelihood of WTA conditions prevailing in a networked market are said to

increase when users have homogeneous needs and care less about platform

differentiation.

A more detailed analysis of this paper is outside the scope of this document.

Interested readers are referred to [22].

2.5. Using Power Curves to Assess Industry Dynamics

Michele Zanini's article in the McKinsey Quarterly Journal (2008) [26],

describes a long-term tendency of increasing inequality observed in the size

26

and performance of large companies. These observations have been made on

the basis of results gathered from plotting the distribution of various

indicators of size and performance (e.g. net income, market value, and

available assets), among the top corporations in a number of industries. The

results from these distribution lay-outs, signify the presence of a power

curve, as opposed to a normal distribution or bell curve, which indicate 'a

relatively even spread of values around a mean'[26].

“Such a curve is characterized by a short “head,” comprising a small set of

companies with extremely large incomes, and drops off quickly to a long

“tail” of companies with a significantly smaller incomes. This pattern,

similar to those illustrating the distribution of wealth among ultrarich

individuals, is described by a mathematical relationship called a power

law.”[26]

In an attempt to explain such phenomena, Zanini states a number of factors

resulting in power law behaviour.

An industry's competitive intensity is an important factor in increasing

inequality amongst its players. A larger number of competitors and more

consumer choice, (unexpectedly) increases inequality, and the gap between

the top rank and the median spot, as opposed to resulting in a flatter curve.

The presence of intangible assets (e.g. trademarks, patents, talent, networks,

etc.) is also said to advance power curves. Intangible assets assist

consumers in performing value propositions, and create economies of scope,

which among many other benefits, allow firms to promote a wider range of

products. Intangible assets also bring about increasing returns to scale; put

differently, intangible assets allow firms to continue growing their profit

27

margins as the firm grows. Note that most firms only enjoy constant returns

to scale.

Figure 2.4 below, extracted from [26], depicts the distribution patterns of

market values in ranking order, across five specified industries. The rank

distributions below highlight the differences in the nature and size of

leading players in these industries. As can be seen, capital-intensive

industries such as Chemicals and Machinery, have a much “flatter” power

curve, compared to intangible-rich industries like Software and Biotech.

These findings suggest that firms, in order to improve their position along

the power curve, need to concentrate on a “strategic thrust”, as opposed to

an “incremental strategy”. The field of Mergers and Acquisitions (M&A)

provides a number of suitable examples of strategic thrusts, when

companies merge with or acquire another. Taking advantage of network

28

Figure 2.4: Distribution of Market Values [26]

effects (discussed in the previous section), has also been identified as a

factor responsible for driving power curves.

Power curves, due to their ubiquitous and consistent nature, can be utilised

as an invaluable tool to predict and analyse an industry's evolution, as well

as helping us benchmark an industry's performance. Zanini ends the article

with the following closing remarks: “Unlike the laws of physics, power

curves aren’t immutable. But their ubiquity and consistency suggest that

companies are generally competing not only against one another but also

against an industry structure that becomes progressively more unequal. For

most companies, this possibility makes power curves an important piece of

the strategic context. Senior executives must understand them and respect

their implications.”[26]

2.6. Summary

E-commerce and online spending is on the increase, with online businesses

predicted to fare better than other businesses in the wake of the current

financial crisis. A number of technologies which will play a significant role

in the further growth of digitalisation, in the foreseeable future, have been

identified. Many companies, not just internet companies, make use of Web

2.0 technologies. Throughout this project, we expect to encounter many

cases of the utilisation of this technology, which will give us better insight

into this emerging IT trend.

Research themes analysing the causes of EM success, impact, and adoption

rates may prove to be particularly relevant in developing a framework and

presenting strategies to improve website popularity. The studies carried out

29

by Lenz et al. (on the effects of partnerships), due to their focus on

European EMs, and the number of studied markets, are likely to be of

significant interest here.

The ideas discussed in [22] are particularly relevant to internet markets, and

this study will aim to determine the presence (or lack thereof) of the factors

contributing to WTA outcomes in the markets studied, and assess their

accuracy. Note that the main contributing effects to these outcomes which

are expected to be encountered are multi-homing costs, network effects, and

differentiated platform functionality, as natural monopolies tend to occur in

very rare circumstances.

A number of classical and contemporary theories and models concerning

electronic markets and market structures were presented in this chapter.

Some statistical models used to assess traditional market structures (e.g.

power curves) have also been discussed.

The classical arguments put forward by Malone predict that digital markets

will primarily lead to perfect information, followed by other effects which

will satisfy perfect competition conditions. These conditions bring about the

emergence of industry structures with large numbers of approximately

equally sized companies. On the other side of the spectrum, the arguments

seen in [26], based on traditional market structures, seem to somewhat

contradict these predictions. The effects caused by the presence of a large

number of companies, and more consumer choice, are said to increase

inequality amongst firms. Assessing the relevance and usability of these

assertions, in the context of online markets, will be an area of focus in this

project. The effects of intangible factors which drive inequality (e.g. talent,

30

networks/partnerships, reputation, experience, etc.), as well as some

monetary factors will also be evaluated.

31

3. Research Problem

The previous chapter highlighted the ever-growing importance, and relative

novelty of the internet. Over the past decade, a growing number of

companies have been moving online, with an increasing proportion of their

revenues being generated there. Despite a number of models being

developed to explain and analyse online market structures and outcomes, it

still remains a fairly new area in research, leaving room for the development

of frameworks and models to better describe virtual market structures.

The research themes and theories presented in the previous chapter,

highlight the extent to which various areas of e-commerce have been

explored. Based on this information, it can be deduced that a number of

classical models, such as perfect competition, were originally proposed in

an attempt to create a framework to analyse and predict electronic market

structures and online behaviour. However, a more contemporary set of

theories and models, such as the ones seen in [22] and [26], demonstrate that

the perfect competition model, does not necessarily have as significant an

impact, in the case of online markets.

Over the years, many aspects of electronic markets have been studied by

researchers in many fields. The success and failure of electronic markets,

mainly through the use of case studies and qualitative research, has received

a lot of attention. Descriptive research methods have also been employed to

study the potential impacts of such markets. In recent years, web analytics

too, have been explored to a great degree, both by researchers, and

businesses aiming to optimise their online performance.

32

Many of the research papers and other literature surveys studied, have

identified the need for more conclusive, empirical, and systematic

approaches in electronic market research, as the majority of the research

carried out thus far tend to be subjective. The lack of reliable and

representative sources of online data is said to be an important factor

contributing to this gap in the knowledge base. This has led to the call for

research grounded in theories and field data to be carried out.

Virtual market structures and inter-organisational competition in online

markets are relatively understudied areas in electronic marketing research,

and given their significance (as pointed out by [22] and [26]), should be

studied in more depth. This inattention may be due to the relative novelty of

the theories discussed, or the unavailability of appropriate web industry

standard metrics to measure the performance and size of online

organisations and industries.

The modest number of studies which use quantitative research methods and

mathematical models in electronic market research, as well as the

inattention towards virtual market structures and their concentration levels

have motivated this study. The composition of research literature in this

field is used to derive the following research questions behind this study.

How is the internet and online performance measured? What is market

share defined as in the virtual world? How is the validity and reliability of

online data from internet marketing companies determined? How do

different online industries compare to each other? How are concentration

levels in these markets measured? What are the implications of a given

industry's concentration levels? What mathematical models can be

33

employed to better describe online performance? Finally, what strategies can

businesses and individuals alike employ to improve their online

performance and website hits? This research project aims to shed light on

the answers to these questions.

There are a number of organisations, employing various measurement

techniques in order to provide tools to accurately “measure the internet”.

Hitwise [27] and comScore [28] are two such organisations. Some of the

tools and methodologies used by these organisations will be discussed in

more detail in the following chapter. This project has made extensive use of

Hitwise, as its main source of primary data. Hitwise provides an extremely

rich dataset, however, there are certain limitations regarding the data which

may be accessed. Namely the region for which the data is available, which

is limited to the UK, and the date range for the available data, which is

limited to twelve months. More details regarding the nature of the data

source and its limitations will be provided in the next chapter.

In the course of this study, an empirical regularity in the pattern of visits to

websites has been observed. These empirical findings and their relevant

implications have been presented and discussed, in a bid to shed light on

some aspects of virtual market structures. Due to the large number of firms

and industries present on the internet, a number of vertical markets have

been chosen, in order to narrow down the scope of this investigation. “A

vertical market is a particular industry or group of enterprises in which

similar products or services are developed and marketed using similar

methods (and to whom goods and services can be sold).”[29] These chosen

vertical markets are given below:

34

• Commercial Airlines

• Consultancies

• Insurance

• Search Engines

• Social Networking Services

35

4. Research Methodology

This chapter provides an overview of the research methodology and the

nature of the data gathered. A number of models have been explored to

explain and predict traditional and online market structures. These models

have been used to support the development and processing of a new

framework in online marketing, with the objective to improve marketing

efforts carried out in these areas.

The next section examines the nature of the data sources. This is carried

forward by a discussion of statistical models and measures of industry

concentration which have been used in the development of the ideas

presented. Finally, a concluding section sets the scene for the data analysis

carried out in the following chapter.

4.1. Nature of the Data Sources

Primary sources of data are gathered by researchers in order to address a

current research problem at hand. Gathering primary data can be a hard and

time-consuming process. Primary research usually gives greater control and

more specific answers to marketers' questions, but due to high costs and the

time it takes to carry out, is not always a feasible solution. The primary

research which needs to be carried out in this project generally falls within a

category known as “quantitative data collection”. This involves the use of

numerical figures and statistics to assess information. Fortunately, this type

of research can be outsourced to marketing research companies that

specialise in data collection and can offer full and partial support in

generating primary data. As previously indicated, data from one of the

36

leading internet research companies, Hitwise, has been used extensively in

the development of the ideas discussed in this paper. This section aims to

describe the methodologies used to carry out online market research by

companies such as Hitwise, as well as providing a comparison between

some of the leading web analytics tool providers.

4.1.1. Web Measurement Methodologies

Methods for measuring internet usage falls into the following three main

categories: User-centric, Website-centric, and Network-centric. This section

provides a brief description of these methods.

User-centric

The user-centric, or panel based method, employed by companies such as

comScore, involves keeping track of a large number of users who have

previously agreed to take part in an online panel. This is achieved by

installing special monitoring software on these users' PCs.

These type of software measure user activities in the digital environment, by

registering and reporting back users' online browsing habits, and e-

commerce purchase behaviour. This includes user activities in secure mode,

i.e. Hypertext Transfer Protocol Secure (HTTPS), which is normally used

as a means to make payment transactions and transfer sensitive data online.

These type of data are captured without personally identifying users.

Examples of usage information which are gathered include site visits, time

spent, viewed ads, and search queries conducted. See [30].

37

There are a number of issues regarding the accuracy and levels of

representation of the user-centric method. One of the main concerns is the

make-up of the users in these panels. The generated data is gathered from

users willing to sign up, and not necessarily other individuals with higher

privacy concerns. Organisations which refuse to divulge such information,

regardless of the privacy agreements in place are also excluded. comScore,

in their own words, describe some of the main incentives being offered to

participants as:

• “Security software applications such as server-based virus

protection, remote data storage, encrypted local storage, Internet

history removal

• Attractive sweepstakes prizes

• Opportunity to impact and improve the Internet”[28]

These incentives, while they may appeal to a substantial number of users, do

not necessarily provide assurances regarding the diversity and

representativeness of the selected demographic samples.

Website-centric

Site-centric measurement is made possible through cooperation with

websites and their respective owners who agree to install web analytics

software on their web pages.

This is usually carried out using web server log files, or by adding page

tracking code to website links. The free service provided by Google

Analytics [31] is an example use of such page tags.

38

One of the main criticisms of this measurement technique is similar to that

of panel-based methods, in that website owners/managers have to agree to

take part and deploy such software on their sites in the first place. This can

potentially limit the diversity and range of data which is collected.

Network-centric

The network-centric or ISP-based method employed by Hitwise, is a novel

technique, involving the use of software to record website usage logs

generated by Internet Service Providers (ISPs). This is done by intercepting

information between users and sites at the network level. Some of the

metrics recorded in these logs include page visits, visit length, and other

industry standard metrics. Figure 4.1 provides a simple illustration of this

ingenious method. Note the arrows represent the flow of information form

one data-store to another.

39

Figure 4.1: Hitwise Methodology Diagram

User's PC

ISP

Web Server

The Internet

Website Usage Logs made available to Hitwise

By working with ISPs, Hitwise is able to anonymously keep track of

considerably more users than its panel-based counterparts (25 million

worldwide, including 8 million in the UK [32] compared to comScore's 2

million panellists worldwide [33]).

In order to assess the effectiveness of this method, the sampling strategies

applied need to be taken into consideration. In other words we need to know

which ISPs are sharing their usage logs and what user demographic they

represent.

Hitwise, in their own words, “collect aggregate usage statistics from a

geographically diverse range of ISP networks in metropolitan and regional

areas, representing all types of internet usage including home, work,

educational and public access. To ensure the ISP and opt-in data is accurate

and representative, it is weighted to universe estimates in each market.”[34]

4.1.2. Comparison of Measurement Techniques

One of the main ways of gathering online competitive intelligence is by

measuring websites' quantity of unique visits. The unique visitor count is

“the number of inferred individual people (filtered for spiders and robots),

within a designated reporting time-frame, with activity consisting of one or

more visits to a site. Each individual is counted only once in the unique

visitor measure for the reporting period.”[35] this is the measure of a

website's true audience size (similar to “reach” in other media). Therefore

we need to determine whether the data sources used measure unique visits.

Note that all the methodologies discussed produce mere estimates of the

number of unique visitors, as it is practically impossible to measure every

40

single visit to any website in the vast space of the internet. As with any

estimate, there will be a certain degree of uncertainty, as figures may be

understated or overestimated.

A straightforward way of counting the number of unique visits is by

registering users, so every time a particular user accesses a website, we can

determine whether that is a unique visit or not. Panel-based techniques can

establish this with relative ease, as they keep track of registered users when

they access various websites. “comScore has developed a patented Session

Assignment Technology that identifies the individual using the computer at

any point in time based on an identification of the individual’s keyboard

keystroke patterns. This means that comScore can identify the person using

the computer without requiring an intrusive pop-up survey each time the

panelist begins a user session.”[33] Site-centric measures, despite portraying

unique visitor counts with relative ease, have certain limitations too. One

problem affecting the accuracy of this measure is when a portion of the

users visiting a website come from an area, organisation, or sector which has

not been represented. This will cause the unique visitors' count to be under-

estimated.

Hitwise too, measures unique visits, but this is presented in terms of shares

of unique visits on a ranked list. Figure 4.2 is an example screen-shot of

such a list, for the UK Aviation websites in February 2009. Network-centric

methods may face a similar problem to that of panel-based data when it

comes to how representative an estimate they are. Although, to much lesser

extent. This is caused by the lack of access to all ISPs and their data, as

some providers won't agree to share their data.

41

Site-centric measurement methods suffer from a different class of problems.

Some site-centric methods make use of web cookies and IP addresses in

order to determine the identity of users. Dynamic IP allocation, has resulted

in the use of static IPs to determine the number of unique visits to become

an obsolete concept. Using IP addresses can lead to underestimating the real

number of unique visits. Cookies don't provide the basis for an entirely

sound measurement system either, due to many users deleting their cookies,

either manually or automatically. Another complication here is caused by

unique visits being counted several times when several browsers are used.

Note the science of measuring the web is still not perfect. There are a

number of benefits and limitations associated with using any of these

methodologies. Hitwise's methodology means a more diverse range of

participants will be monitored, which provides richer traffic data than any

other method available, and the sample size is a number of times larger than

that of comScore. However, ISP-based data, unlike panel-based methods,

can not always collect secure (HTTPS) information. comScore, on the other

hand, gets a good depth of data from their panellists and is ideal for for

42

Figure 4.2: Top 10 Aviation websites in February 2009 from Hitwise

advertising purposes, while Hitwise is more suited as a marketing tool.

4.1.3. Summary

This section has described the different methods employed by various web

analytics data providers. A comparison of these techniques were also

presented. As previously indicated, in this project Hitwise has been used as

the main source of primary data. The Hitwise methodology is generally

more representative and diverse, because it monitors all internet traffic, and

not just a self-selected group of users. Thus, the involuntary nature of the

data gathered makes Hitwise an ideal choice. Readers are reminded that

Hitwise is not a free service, and the access level granted is limited to the

UK, and within a twelve month time-frame.

There are many other organisations providing similar tools and services

which in the interest of brevity have not been discussed in this paper.

Interested readers may refer to Alexa: (www.alexa.com), Compete:

(www.compete.com), and Quantcast: (www.quantcast.com) for more

information on some of these internet research companies and their

methodologies.

4.2. Statistical Distributions

A number of statistical models have been employed to determine the

distribution of visits amongst users of websites in the chosen online

segments. This section provides groundings for the distribution patterns

observed in the number of website visits.

“Probability is a measure associated with an event A and denoted by

43

Pr A which takes a value such that 0≤Pr A≤1 . Essentially the

quantitative expression of the chance that an event will occur.”[36]

Many random variables appearing in natural and man-made phenomena are

distributed in a certain way. “If a variable can take on any value between

two specified values, it is called a continuous variable; otherwise, it is called

a discrete variable.”[37] Based on this definition, percentage shares of visits

to websites are continuous variables, as they can take any value between 0

and 100.

“If a random variable is a continuous variable, its probability is called a

continuous probability distribution. Sometimes, it is referred to as a density

function, or a Probability Density Function (PDF).”[37]

4.2.1. Normal Distributions

A wide range of probability distributions have been used to describe the

distribution of many entities of interest. One of the most important and

widely discussed probability distributions are Normal Distributions (also

known as the Bell curve). The normal distribution curve is most suited to a

range of variables with a high number of values close to the mean. For

example, Figure 4.3 depicts the typical bell-shape a normal distribution

takes.

44

Statisticians have identified numerous statistical distributions which appear

frequently in a diverse range of physical phenomena. The following section

describes a certain type of distribution identified by what is known as a

power law.

4.2.2. Power Laws

The following definitions have been given to introduce power law

equations, and to provide a mathematical framework which has been used to

interpret power curves in the stage of data analysis.

“When the probability of measuring a particular value of some quantity

varies inversely as a power of that value, the quantity is said to follow a

power law.”[38] The phrases power law, Pareto's principle, and Zipf's law

are sometimes used interchangeably in the literature.

“A non-negative random variable X is said to have a power law distribution

if

Pr [X ≥x ]~cx−

for constants c0 and α0 .

45

Figure 4.3: Typical Normal Distribution Curve

If X has a power law distribution, then in a log-log plot of Pr [X ≥ x ]

, also known as the complementary cumulative distribution function,

asymptotically the behavior will be a straight line. This provides a simple

empirical test for whether a random variable has a power law given an

appropriate sample.”[39]

One of the most commonly used cases of the power law, which will be used

extensively in the following chapters, has the simplified form

f x =Cx−

where C is a (non-important) normalisation constant, and is a

constant parameter known as the exponent. The exponent is defined as

the gradient of the power curve; put differently, is the measure of how

steep the curve is, with higher values of indicating steeper curves.

Figure 4.4 depicts a typical power law graph.

It has long been known that many empirically observed phenomena display

power law behaviour. Examples include word frequency measures, wealth

46

Figure 4.4: Example Power Curve [38]

of richest Americans, populations of cities, telephone calls, magnitude of

earthquakes, intensity of wars, etc. See [39] for more examples.

In this paper, the data under consideration is ranked by the popularity of

websites according to their share of unique visits, and in some special cases

their share of online sales opportunities. This is sometimes referred to as a

rank-frequency distribution.

A more detailed description of the power laws, Pareto's principle, and Zipf's

law are beyond the scope of this document. Interested readers can consult

[38] and [39].

4.3. Measures of Industry Concentration

“Concentration refers to the extent to which a small number of firms or

enterprises account for a large proportion of economic activity such as total

sales, assets or employment...industry or market concentration (also often

referred to as seller concentration) measures the relative position of large

enterprises in the provision of specific goods or services.”[40] A number of

methods are used to measure industry concentration to assess market

structure. Some of these methods, which have been used in this study, are

described below.

4.3.1. Concentration Ratio (CR)

Concentration Ratio is “the percentage of total industry output (or other

such measure of economic activity, e.g., sales revenue, employment) which

a given number of large firms account for. The four-firm concentration ratio

( CR4 ) measures the relative share of total industry output accounted for

47

by the four largest firms.”[41] In other words, CRn is the percentage

market share held by the largest n firms in an industry such that

CRn=s1s2s3...sn

Where sr represents the percentage market share held by the r th

company. This offers a simple measure of industry concentration, and levels

of competition, as well as highlighting the scope available for economies of

scale. In this paper, a four-company measure ( CR4 ) has been used to

determine concentration ratios across identified (online) markets of interest.

For example in an industry consisting of seven firms, where market shares

(in descending order) are 20%, 20%, 15%, 15%, 10%, 10%, 10%, the four-

company concentration ratio is 70% ( CR4 = 20% + 20% + 15% + 15% =

70%).

However, this measure does not distinguish between the distribution of sizes

for the top n ( n=4 for our purposes) companies. For example, top-

four market shares of 50%, 15%, 10%, and 5% would yield the same result

as 20%, 20%, 20%, and 20%. This makes the concentration ratio an

imperfect measure for certain cases.

All through the data analysis presented in the following chapter, CR4

results have been accompanied by graphs depicting the percentage share

held by the top four websites over a period of twelve months. This is carried

out in an attempt to tackle the problem stated above, and to show market

share dynamics and fluctuations amongst the top companies in each

industry.

48

4.3.2. Herfindahl-Hirschman Index (HHI)

“This measure is based on the total number and size distribution of firms in

the industry. It is computed as the sum of the squares of the relative size of

all firms in the industry. Algebraically it is:

HHI=∑i=1

n

S i2 where ∑

i=1

n

S i=1

si is the relative output (or other measures of economic activity such as

sales or capacity) of the ith firm, and n is the total number of firms in

the industry.”[41]

For example in an industry consisting of nine firms with the following share

percentages in descending order: 20”%”, 3 * 15”%”, 10”%”, 5 * 5”%”, we

get

HHI = 20%23∗15%210%25∗5%2 = 13%

The HHI has certain advantages over CR, in that it gives more weight to

larger firms, and therefore is often a good indicator of levels of competition

amongst firms. The US uses HHI to regulate mergers and to decide whether

a company take-over may result in monopolistic behaviour. The following

classifications for different values of the HHI have been extracted from the

U.S. Department of Justice and the Federal Trade Commission's Merger

guidelines [42]:

• Unconcentrated: HHI below 1000 (below 10%)

• Moderately Concentrated: HHI between 1000 and 1800 (10%-18%)

• Highly Concentrated: HHI above 1800 (above 18%)

49

4.4. Framework for Data Analysis

The following passages describe some restrictions set on the gathered data,

as well as describing how a number of complications were addressed to

provide comparable datasets across the various identified online markets.

Hitwise data for website rankings are provided for a large number of

industries, grouped according to Hitwise's classifications. The vertical

markets identified for the purpose of this project, however, needed to be re-

classified according to categories and datasets defined by the author. For

example, in order to study online social networking services, social

networking sites' rankings needed to be separated from the “social

networking & forums” category, as defined by Hitwise. The “social

networking & forums” category returns some 7000 websites, many of which

are websites whose primary function is different to that of plain social

networking (e.g. YouTube). For this reason, websites with non-negligible

shares of visits belonging to each online market were selected, and their

percentage shares of visits were adjusted accordingly.

As the percentage share of visits for each website gets close to 1%, it

becomes negligible. The bulk of the data has been restricted to websites

with visit shares of no less than 1% of their respective market. The

percentage shares of unique visits have been adjusted to represent the share

of each website among the top n selected websites in each sector, as

there are numerous (typically 500+) websites in each category, the majority

of which hold negligible percentage shares of visits. The author

acknowledges that this will exclude websites with very small shares of

visits, however, this is not thought to have affected the analyses and/or

50

conclusions of this study.

Unique visit counts are always identified within a certain time-frame

(normally no longer than a month), and are non-additive metrics. This

implies that unique visits cannot be added over a time period, or over groups

of content, as adding unique visit counts over different time periods or

websites may result in over-representation. See [35].

The fact that unique visits are non-additive causes some complications

regarding the data analysis in this study. In order to accurately represent

each website's unique visits share, to perform a static analysis of each sector,

a long enough time period had to be assigned. For this reason, the decision

was made to go with quarterly periods, however, the longest period for

which visit shares were available on Hitwise was a month. Consequently,

websites' shares of unique visits in three consecutive months were averaged

to arrive at their average share of unique visits for a given quarter.

Another complication stemming from the non-additive nature of unique

visits, was in the case of certain websites essentially being a regional

version or part of another (e.g. Google and Google UK, Microsoft and

Microsoft Windows). In cases like these the unique visit shares for different

websites were added together, and referred to as their “share of online sales

opportunities”. The use of online sales opportunities as a metric similar to

the share of the online market, has been indicated in the following chapter .

However, subsidiary companies and their respective parent companies

operating in the same sector (e.g. bmi and bmibaby) have not been grouped,

as they operate as two separate, distinct, legal entities.

51

5. Data Analysis

This chapter aims to report the findings and observations made in each

vertical market. A description of each internet market segment is provided,

followed by a discussion of the figures, market structures, and their

respective power curves.

Note that to make the generated power curves comparable, the number-one

rank was fixed at 100%, and the other websites' visit shares were adjusted

accordingly. This was done by dividing the percentage visit shares for each

website by the top ranking website's visit share percentage, and multiplying

the result by 100.

Readers are once-again reminded that the popularity of websites discussed

in this chapter are based solely on their share of (unique) visit counts from

within the UK; put differently, only ISPs within the UK have contributed to

this data (accessed via Hitwise).

5.1. Commercial Airlines

This category includes any websites offering air-travel bookings and charter

flight services. Based on the figures on shares of visits in the first quarter of

2009 (see Table 1), Easyjet has the leading website in this category (21%

visits share), followed closely by RyanAir (18.5% visits share). The

majority (almost 75%) of visits are directed at low-cost airlines (e.g.

easyJet, RyanAir, bmibaby, etc.) , which is almost three times as many as

52

traditional or full-service airlines (e.g. BA, KLM, American Airlines, etc.).

Therefore it is important to distinguish between the two, and the services

they offer.

Low-cost airlines, as the name suggests, compete mainly on price. They

manage to cut their costs, and operate more efficiently, mainly by foregoing

certain passenger services offered by most of their traditional counterparts.

One of the main ways in which budget airlines manage to slash their prices

is by cutting out the middle-man, in this case the travel agents. They

encourage their customers to make direct bookings, especially over the

internet, by advertising special offers and cheaper fares for those who go

online. This allows them to avoid excessive paper-work and unnecessary

staff wages, while customers enjoy faster and cheaper transactions. The

internet and the use of electronic systems allow these companies to exercise

dynamic pricing strategies, which are crucial to any low-cost airline's desire

to remain competitive. Budget airlines also try to maximise their ancillary

revenues; these are those revenues generated from non-ticket sources. See

[43]. Ancillary revenues fall in the following categories:

• Offering commission-based services (such as insurance, hotel

reservations, and car hiring services) outside their core competency

area.

• Adopting à la carte pricing strategies; this is, offering a separate

price tag for various services not included in the advertised price,

such as the purchase of food, snacks, and beverages on board, excess

baggage charges, and better seating options for those who are willing

to pay extra fees.

53

• Frequent flyer programmes, which are conceptually similar to

loyalty cards, have also been identified as an emerging source of

ancillary revenues. Airlines are able to generate revenues by

partnering with a number of companies in various industries,

offering a range of (free and discounted) products and services to

passengers wishing to part-take in such programmes. See [44].

A noteworthy fact is that due to the successful employment of these

strategies, airlines worldwide (including traditional ones) are adopting

similar approaches. However, ancillary revenues remain an important

financial component of budget airlines.

The values for the concentration ratio of the top four websites, ( CR4

=56.5%), and the Herfindahl measure ( HHI =0.11) in this sector, based

on their share of visits, indicate a moderately concentrated industry, with

relatively high levels of competition amongst firms. The similar nature of

the services offered, i.e. flights, in particular with budget airlines which

suffer from lack of differentiation, can drive competition and lead to price-

wars.

54

Figure 5.1 shows the share of visits amongst the top four companies in this

category: easyJet, RyanAir, BA, and Flybe.com. Over the period of a year

(2008/2009), the top four's online market shares relative to each other

remain more-or-less the same, with low levels of variation (for each

website) from the average visit shares over the same period. This indicates

the lack of any significant trends in market concentration in this category

over the given period. However, Figure 5.1 suggests high levels of

competition amongst the top two rivals, easyJet, and RyanAir.

55

Table 1: Commercial Airlines Visits Share - 2009 Quarter 1

Rank Domain

1 21.03%2 18.68%3 British Airways 10.61%4 6.18%5 5.91%6 Jet2 www.jet2.com 5.88%7 Virgin Atlantic 4.31%8 Thomson Airways 3.79%9 Monarch Airlines 3.46%10 3.33%11 2.50%12 2.23%13 2.18%14 2.04%15 KLM Royal Dutch Airlines 1.96%16 Emirates Airlines 1.75%17 1.34%18 American Airlines 1.16%19 Lufthansa 0.87%20 Air France UK 0.80%

Most popular websites in Commercial Airliners - 1st quarter 2009

Share of Unique Visits

easyJet www.easyjet.co.ukRyanAir www.ryanair.com

www.britishairways.comFlybe.com www.flybe.combmibaby www.bmibaby.com

www.virgin-atlantic.comwww.thomsonfly.comwww.flymonarch.com

bmi www.flybmi.comAer Lingus www.flyaerlingus.comflythomascook.com www.flythomascook.comFlyglobespan www.flyglobespan.comTuifly www.tuifly.com

www.klm.comwww.emirates.com

Wizz Air www.wizzair.comwww.aa.comwww.lufthansa.comwww.airfrance.co.uk

Figure 5.2 shows the power curve for successively-ranked unique visit

shares to airline websites, over a period of three months, in early 2009. The

Probability Density Function (PDF) for the curve is described by the

following equation:

f x =156.3 x−1.1 with R2=0.94

This power curve shows the top two ranks receiving a considerably larger

portion of visits to their websites than other websites in this category. R2

is the proportion of variance explained by regression. The high value of

R2 indicates the data is described by the power law to a striking degree of

accuracy. Recalling the equation of a power curve ( f x =Cx− ), the

exponent is defined as the gradient of the power curve. , in the

case of commercial airlines, takes the value of 1.1, indicating a rather flat

curve for this online industry. Some implications (based on the gradient and

shape) of this power curve, and others, will be discussed in later sections.

56

Figure 5.1: Commercial Airlines - top 4 market structure dynamics - 2008/2009

July, 2009May, 2009

March, 2009January, 2009

November, 2008September, 2008

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

Commercial Airlines - Top 4 Share of Visits2008/2009

easyJet RyanAir British Airways Flybe.com

shar

e of

vis

its

5.2. Consultancies

This category includes any firms offering help and advice to organisations,

aimed at optimising their business performance and improving their

efficiency. This is achieved by tapping into a vast pool of knowledge,

process methodologies, and industry best-practice guidelines which

consultancy firms have access to. By offering expert advice and know-how

to businesses seeking improvement, consultancies have been able to

establish themselves as an important tool for boosting businesses'

competitive stance in their respective industries.

There are certain areas consultancy firms can specialise in. The majority

offer management consulting services, followed by IT consulting,

accounting, Human Resources (HR), outsourcing services, etc. There is

some confusion as to how distinct these speciality areas are. This is caused

57

Figure 5.2: Commercial Airlines Power Curve - 2009, 1st Quarter

0 5 10 15 20 250

20

40

60

80

100

120f(x) = 156.3 x̂ -1.1R² = 0.94

Commercial Airlines - 2009, 1st Quarterindex: most popular website = 100

Rank by Popularity

by the overlapping nature of such services. For instance, in order to improve

a company's performance, a consultancy firm may need to improve the

company's current business processes, take advantage of relevant

technologies, and outsource certain tasks outside the company's core

competency area. Larger consulting firms tend to incorporate many of these

speciality areas in the range of services they offer, however, medium and

small-sized companies in this category may offer a more limited range of

services.

A noteworthy fact is that some of the major companies considered in this

category, comprise a network of firms, owned and managed independently,

while being coordinated by one separate entity. These, offer a more diverse

range of professional services, especially in the fields of financial auditing

and accounting, which sets them apart from smaller companies merely

offering management and/or technology consulting services. These are,

PWC, Deloitte Touche Tohmatsu, Ernst & Young, and KPMG, also knows

as the “Big Four Auditors”. Many large public and private companies have

made a “big four” audit a necessary requirement for their financial audits.

58

Table 2 shows the share of visits to consultancy firms' websites in the first

quarter of 2009. The figures, for PWC and Capgemini, consist of both UK

and international websites' visits (from within the UK) and are therefore

referred to as their share of online sales opportunities. The figures show

PWC clearly leading with a 27% share, followed by CRM Metrix, with a

10.5% share of the online market during this period. CRM Metrix, which is

not a major consultancy firm, offers a range of services which are to an

extent different to other consultancies discussed, with the main emphasis

being on measuring website impacts on building businesses. Some

implications of this will be discussed in the next chapter.

The values for the concentration ratio of the top four websites, ( CR4

=52.5%), and the Herfindahl measure ( HHI = 0.12) in this sector, based

on their share of visits, indicates a moderately concentrated industry, with

59

Table 2: Consultancies Visits Share - 2009, Quarter 1

Rank Domain

1 27.26%

2 CRM Metrix www.crmmetrix.fr 10.50%3 Deloitte Touche Tohmatsu www.deloitte.com 8.23%4 Ernst & Young International www.ey.com 6.56%5 Serco www.serco.co.uk 6.08%6 Accenture www.accenture.com 5.74%7 Atkins Global www.atkinsglobal.com 5.19%8 BSI Group www.bsi-global.com 5.37%9 KPMG UK www.kpmg.co.uk 4.64%10 Arup www.arup.com 3.50%

11 Capgemini + Capgemini UK 3.22%

12 Grant Thornton www.grant-thornton.co.uk 2.46%13 Aberdeen Quality Associates www.aqa.co.uk 1.46%14 Scott Wilson www.scottwilson.com 1.85%15 McKinsey & Company www.mckinsey.com 1.70%16 The Gallup Organization www.gallup.com 1.68%17 PA Consulting www.paconsulting.com 1.61%18 Aon www.aon.com 1.58%19 PKF UK www.pkf.co.uk 1.37%

Most popular websites in Consultancies - 1st quarter 2009

Share of Online Sales Opportunities

PricewaterhouseCoopers Global + UK

www.pwc.com + www.pwc.co.uk

www.capgemini.com + www.uk.capgemini.com

relatively high levels of competition amongst firms.

Figures 5.3 (top four according to 1st quarter of 2009: PWC, CRM Metrix,

Deloitte, and Ernst & Young) and 5.4 (top four with highest average visit

shares over twelve months: PWC, CRM Metrix, Accenture, and Deloitte)

show the share of visits among the top four companies in this category, over

the period of a year (2008/2009). Both figures suggest a gradual increase in

the concentration levels among the top four websites in this online market

segment, with the top two ranking firms increasing their respective market

shares.

60

Figure 5.3: Consultancies - top 4 market structure dynamics – 2008/2009 – based on top 4 companies identified in 1st quarter of 2009

June, 2009 April, 2009

February, 2009December, 2008

October, 2008August, 2008

0.00%5.00%

10.00%15.00%20.00%25.00%30.00%35.00%

Consultancies - Top 4 Visits Share2008/2009

PWC CRM Metrix Deloitte Touche Tohmatsu

Ernst & Young In-ternational

shar

e of

vis

its

Figure 5.4: Consultancies - top 4 market structure dynamics - 2008/2009 - based on average market shares over 12 months

June, 2009April, 2009

February, 2009December, 2008

October, 2008August, 2008

0.00%5.00%

10.00%15.00%20.00%25.00%30.00%35.00%

Consultancies - Top 4 Visits Share2008/2009

PWC CRM Metrix Accenture Deloitte Touche Tohmatsu

shar

e of

vis

its

Figure 5.5 shows the power curve for successively-ranked unique visit

shares to consultancy websites, over a period of three months, in early 2009.

The PDF for the curve is described by the following equation:

f x =101 x−0.97 with R2=0.93

This power curve shows the top ranking website receiving a considerably

larger portion of visits than other websites in this category. The high value

of R2 indicates the data is accurately described by the power law. The

exponent takes the value of 0.97, indicating a flat curve for the online

market.

61

Figure 5.5: Consultancies Power Curve - 2009, 1st Quarter

0 2 4 6 8 10 12 14 16 18 200

20

40

60

80

100

120f(x) = 101 x̂ -0.97R² = 0.93

Consultancies - 2009, 1st quarterindex: most popular website = 100

Rank by Popularity

5.3. Insurance

This category consists of any websites offering insurance services. This

includes insurance policies on home, travel, car, health, pets, etc. Note that

comparison websites (e.g. moneysupermarket.com, gocompare.com) have

not been included, however, some aspects of such comparison websites and

their impacts will be discussed in later sections.

Some of the leading websites in this category offer financial services such as

loans, credit cards, and online banking, in addition to insurance (e.g. Tesco

Personal Finance, The AA, Virgin Money UK), while others focus on

insurance services in particular areas such as health (e.g. BUPA) or car

insurance (e.g. RAC). However, the majority of websites offer a range of

insurance services including car, home, and travel insurance.

Another important aspect of the nature of websites in this category is that

many insurance companies are subsidiaries or affiliates of banks, building

societies, and other organisations (e.g. Direct Line is a subsidiary of RBS,

Virgin Money is part of the Virgin group, Tesco Personal Finance is owned

by Tesco).

It is important to distinguish between personal insurance and insurance

purchased by businesses. Individuals are encouraged to perform insurance

transactions online, taking advantage of better deals and speedier

transactions, while this process is far more complicated for businesses.

Hence the main target audience for the websites in this category are

individuals, as opposed to businesses.

All websites in this category offer online quotes. The majority allow users to

62

purchase personal insurance on their websites, while others perform the rest

of the process (once the quote has been given online) manually.

Table 3 shows the share of visits in the first quarter of 2009. The AA is the

market leader in this category with 34.5% of the online market share,

followed by Tesco Personal Finance with 17%, and Direct Line with 11%

online market share.

The values for the concentration ratio of the top four websites, ( CR4

=70%), and the Herfindahl measure ( HHI = 0.18) in this sector, based on

their share of visits, indicate moderate-high concentration levels in this

industry.

Figure 5.6 shows the share of visits among the top four companies in this

category, over the period of a year (2008/2009): The AA, Tesco Personal

Finance, Direct Line, and Virgin Money UK. Figure 5.6 shows a significant

growth in The AA's market share in this industry. Over the same period,

63

Table 3: Insurance Visits Share - 2009 Quarter 1

Rank Domain

1 The AA www.theaa.com 34.45%2 Tesco Personal Finance www.tescofinance.com 17.26%3 Direct Line uk.directline.com 10.87%4 Virgin Money UK uk.virginmoney.com 7.53%5 BUPA www.bupa.co.uk 6.35%6 Aviva UK www.norwichunion.com 3.46%7 RAC www.rac.co.uk 4.13%8 More Than www.morethan.com 2.45%9 Churchill Insurance www.churchill.co.uk 2.50%10 Legal and General www.legalandgeneral.com 2.12%11 eCarinsurance.co.uk www.ecarinsurance.co.uk 1.96%12 Standard Life UK uk.standardlife.com 1.67%13 Admiral Insurance www.admiral.uk.com 1.35%14 Insure & Go www.insureandgo.com 1.36%15 Scottish Widows www.scottishwidows.co.uk 1.35%16 Essential Travel - Insurance insurance.essentialtravel.co.uk 1.20%

Most popular websites in Insurance - 1st quarter 2009

Share of Unique Visits

Tesco Personal Finance's share of visits has dropped by 5%, with the

remaining two showing no significant changes in their online market share

levels. The AA's online market share gains since the start of 2009 suggest a

gradual increase in levels of industry concentration, however, a time period

of six months is not considered to be long enough to make firm predictions

regarding the structure of this seemingly competitive market.

Figure 5.7 shows the power curve for successively-ranked unique visit

shares to insurance websites, over a period of three months, in early 2009.

The PDF for the curve is described by the following equation:

f x =116.52 x−1.27 with R2=0.99

The high value of the R2 co-efficient indicates the data is a remarkably

good fit. This is the highest value of R2 across all studied sectors. The

exponent takes the value of 1.27, indicating a relatively flat curve.

64

Figure 5.6: Insurance – top 4 market structure dynamics - 2008/2009

June, 2009April, 2009

February, 2009December, 2008

October, 2008August, 2008

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

Insurance - Top 4 Visits Share2008/2009

The AA Tesco Personal Finance

Direct Line Virgin Money UK

shar

e of

vis

its

5.4. Search Engines

This category includes web sites whose main function is to provide results

in the form of links to other websites, based on the entered set of keywords.

Table 4 shows the share of visits for leading search engines in the first

quarter of 2009. The figures, for all engines consist of regional (.co.uk) and

international (.com) figures. The figures for Google include the share of

visits to Google Image Search too, as image searches are considered to be

part of this category. Other search engines, such as Yahoo!, also provide

image searching facilities, however, their percentage share of visits were

particularly low in comparison (0.08% visit share for Yahoo! Image search

in the 1st quarter of 2009), and therefore have not been included.

Based on these figures, Google is clearly the dominant market leader in this

65

Figure 5.7: Insurance Power Curve - 2009, 1st Quarter

0 2 4 6 8 10 12 14 16 180

20

40

60

80

100

120f(x) = 116.52 x̂ -1.27R² = 0.99

Insurance - 2009, 1st quarterindex: most popular website = 100

Rank by Popularity

category with a staggering 91% market share. The next best search engine

holds a modest 3.26% share which is almost thirty times smaller than

Google's share of visits.

One of the main ways in which search engines generate revenues is by “paid

inclusion programmes”; these are, companies/websites paying to appear

before other search results (usually in a section called the sponsored links or

results) for certain search terms. Major Search engines (including the top

four shown in Table 4) have devised ways (by manipulating their search

algorithms) to include “paying websites” in their organic search results as

well.

One of the main perceived benefits of Google's search engine is brought

about by its sophisticated search algorithm. This makes the search results

relevant and informational. Google is particularly good at differentiating

between real links and spam. This is mainly carried out by aggressively

66

Table 4: Search Engines Visits Share - 2009 Quarter 1

Rank Domain

1 91.34%

2 3.26%

3 3.09%

4 2.31%

Most popular websites in Search Engines - 1st quarter 2009

Share of Online Sales Opportunities

Google (Google UK + Google + Google UK Image Search + Google Image Search)

www.google.co.uk + www.google.com+images.google.co.uk + images.google.com

Windows Live Search + MSN UK Search + MSN

www.live.com + search.msn.co.uk + search.msn.com

Yahoo! Search - UK & Ireland + Yahoo! Search

uk.search.yahoo.com + search.yahoo.com

Ask.com UK + Ask.com www.ask.co.uk + www.ask.com

filtering out pages focusing on a particular search term or phrase, i.e. if the

phrase appears in the link, the page title, and several times within the

content itself. Another factor distinguishing Google from other search

engines, is its attention to link reputation (based on link's age, source, link

type e.g. .gov and .edu are less likely to be manipulated than .com) and rate

of link acquisition. The consistent relevance and accuracy of Google search

results have contributed to this search engine establishing a dominant

position in the online search market. A more detailed discussion of the

methodologies employed by various search engines are beyond the scope of

this document. Interested readers can consult Google's webmaster guidelines

[45] for more information on some of the factors influencing the perceived

relevance of various links. Note that the specific details on search

algorithms, in particular with online search giants such as Google, are

closely guarded trade secrets, and remain outside the public domain.

Because only four (groups of) search engines are being considered in this

category (due to the negligible market shares of others), it may be plain to

see the resulting concentration ratio ( CR4 =100%). A concentration ratio

of near 100% in traditional markets, signifies the existence of a monopoly.

The Herfindahl measure ( HHI = 0.84, which is the highest measured

across all studied online market segments) in this sector, indicates a very

highly concentrated online industry.

Figure 5.6 shows the share of visits among the top four search engines over

the period of a year (2008/2009). Note that since June 2009, The group of

search engines belonging to the Microsoft Network (MSN) have been

operating under a new name, Bing, and users trying to access the MSN

67

search engines are redirected to this site. The graph below shows no

significant trends in the levels of concentration in this industry, with Google

maintaining its position as the dominant search engine.

Figure 5.9 shows the power curve for successively-ranked unique visit

shares for the considered search engines, over a period of three months, in

early 2009. The PDF for the curve is described by the following equation:

f x =60.81 x−2.65 with R2=0.84

This power curve shows Google receiving a considerably larger portion of

visits than other websites in this category. The value of R2 , despite not

being as high as the other co-efficients seen, indicates the data can still be

described by the power law to an acceptable degree of accuracy. One reason

for the value of R2 not being as high as other vertical markets considered,

is due to the sample size not being large enough. The exponent takes

the value of 2.65, highlighting the huge level of inequality between the top

ranking website and the lower ranks.

68

Figure 5.8: Search Engines - top 4 market structure dynamics - 2008/2009

June, 2009April, 2009

February, 2009December, 2008

October, 2008August, 2008

0

0.2

0.4

0.6

0.8

1

Search Engines - Top 4 Visits Share2008/2009

Google Yahoo! MSN Search/Bing Ask.com

shar

e of

vis

its

5.5. Social Networking Services

This category includes websites which enable users to engage in social

networking activities, through the use of their profile pages. This includes

many online networks, however, forums have not been included in this

category. One reason for this is the main functionality of some popular

websites which feature forums being other than plain social networking (e.g.

YouTube, which is mainly used for sharing videos, but also features

forums). Social networking websites allow users to share information and

communicate with each other through a variety of methods, such as email

and instant messaging, as well as enabling blog-like entries, photo-sharing,

group memberships, and in recent years applications and gadgets which can

69

Figure 5.9: Search Engines Power Curve - 2009, 1st Quarter

0.5 1 1.5 2 2.5 3 3.5 4 4.50

20

40

60

80

100

120

f(x) = 60.81 x̂ -2.65R² = 0.84

Search Engines - 2009, 1st quarterindex: most popular website = 100

Rank by Popularity

be added onto a users profile.

Note that for the purposes of this study, social networking communities

closed to the public (referred to as Internal Social Networking (ISNs)

websites) have not been considered. The social networking sites considered

in this category, due to the restriction set on the minimum share of visits, are

larger, more generic sites featuring a multitude of users from all walks of

life. This category does not feature niche market social networking sites for

users sharing one or few specific interests only.

In recent years various social networking websites have sprung up, and the

popularity of such services are increasing by the day. A number of

businesses (particularly larger businesses) are also using social networking

websites as a means to raise brand awareness within online communities,

and for recruitment purposes.

None of the social networking sites identified in this category require a

subscription fee. Most social networking websites generate the vast majority

of their revenues through advertising.

Table 5 shows the share of visits to social networking websites in the first

quarter of 2009. Facebook is clearly the market leader with a substantial

73% market share.

70

Table 5: Social Networks Visits Share - 2009 Quarter 1

Rank DomainVisits

1 Facebook www.facebook.com 72.71%2 Bebo www.bebo.com 14.83%3 MySpace www.myspace.com 8.23%4 Twitter www.twitter.com 1.28%5 Tagged www.tagged.com 1.24%6 Windows Live Home home.live.com 1.21%

Most popular websites in Social Networking – 2009, 1st quarter

The values for the concentration ratio of the top four websites, ( CR4

=97% ), and the Herfindahl measure ( HHI = 0.56 ) in this sector, based

on their share of visits, indicate high levels of concentration.

Figure 5.10 shows the share of visits among the top four companies

(Facebook, Bebo, MySpace, and Twitter) in this category, over the period of

a year (2008/2009). This figure shows a steady trend in the rate of growth

of Facebook's share of visits (20% increase in online market share). Over

the same period, a gradual decrease in the number of visits to Bebo (by

15%) and MySpace (by 10%) can be observed, while Twitter has enjoyed a

slight increase (by 3%) in its share of visits.

Figure 5.11 shows the power curve for successively-ranked unique visit

shares in the social networking market, over a period of three months in

early 2009. The PDF for the curve is described by the following equation:

f x =107.21 x−2.49 with R2=0.95

This power curve shows Facebook receiving a much larger portion of visits

71

Figure 5.10: Social Networks - top 4 market structure dynamics - 2008/2009

June, 2009April, 2009

February, 2009December, 2008

October, 2008August, 2008

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

Social Networks - Top 4 Visits Share2008/2009

Facebook Bebo MySpace Twitter

shar

e of

vis

its

than other websites. The value of R2 , suggests the data is being described

by the power law to a suitable degree of accuracy. The exponent takes

the value of 2.49, highlighting a particularly steep power curve with high

levels of inequality between the top ranking website and the lower ranks.

72

Figure 5.11: Social Networks Power Curve - 2009, 1st Quarter

0 1 2 3 4 5 6 7 80

20

40

60

80

100

120f(x) = 107.21 x̂ -2.49R² = 0.95

Social Networking - 2009, 1st quarterindex: most popular website = 100

Rank by Popularity

6. Discussion of the Results

The results of the data analysis presented in the previous chapter indicate

the different nature of the chosen vertical markets. A number of factors

responsible for influencing online market structures have been identified by

the literature survey. In this chapter, by drawing on those influences, and the

observed patterns in online market structures, a comparison of the results

with the literature is provided. The main influential aspects in each observed

market structure are presented separately to begin with. This discussion is

carried forward by a cross-industry analysis, where all the online markets

are compared to each other and a number of conclusions are drawn based on

their market characteristics.

6.1. Case-by-case Analysis

6.1.1. Commercial Airlines

The (online) market structure which commercial airlines operate within, has

been identified as one with moderate levels of concentration, and high

perceived risk. The impact of electronic markets on the commercial flights

industry has caused higher levels of competition amongst airlines. This has

benefit consumers more than the airlines. These high levels of competition

particularly affect low-cost carriers, which use price as their unique selling

point. Such airlines, which by nature suffer from a lack of service

differentiation are forced to cut costs and generate revenues from sources

other than ticket sales (ancillary revenues) in order to remain profitable.

Partnering with other organisations enables low-cost carriers to provide (and

73

generate revenues from) a number of services outside their core

competency area (flights). This serves as a crucial profit maximisation

strategy. Easyjet, and RyanAir, the market leaders in this category, have both

gradually expanded their services over the past few years to include hotel

bookings, car rentals, and travel insurance, by partnering with other

organisations. See [46] and [47]. By using the internet as a medium for

sales, airlines have been able to incorporate the various services they offer

on one site, allowing for such partnerships to become feasible, while

consumers are able to access the various functions offered in a more

convenient manner.

In order to offer low prices and remain competitive, budget airlines need to

cut their own spending as well. Utilising the internet as a medium for sales,

whilst in effect reducing labour costs, has proven to be a successful cost-

cutting strategy. The large number of promotions and incentives targeted at

online consumers serve as a testament to this. Suitable utilisation of the

internet and electronic systems also allow for more efficient dynamic

pricing mechanisms (e.g. first x % of tickets available on each flight are

sold at minimum price) to be put in place. Prices can automatically be

updated round-the-clock with minimum human interference.

Lowering prices implies higher volumes of sales are required to reach

break-even points. For this reason, airlines need to focus on customer

retention, while trying to attract new customers. Making use of well-

designed and easy-to-navigate websites is one way of reducing customers'

effort expectancies and encouraging them to revisit their site. This may also

promote customer loyalty in the long-run, leading to higher multi-homing

74

costs.

The multi-homing costs associated with this online market relatively are

low. This is mainly caused by the one-off nature of the transactions made by

most users and the fact that they can choose to purchase their tickets on any

of the given websites, depending on the offered price or promotion.

However, participation in frequent flyer programmes are likely to increase

these costs. Frequent flyer points can be earned in a multitude of ways

which do not involve the purchase of tickets, such as credit card purchases,

mortgages, long-distance calls, etc. This highlights the importance of

partnerships in promoting websites in this market.

There are no indications of strong network effects in this market. Customers

appear to mainly care about getting the cheapest deals, and are not

concerned with other passengers wishing to fly with the same airline.

However, price-savvy users may wish to compare prices and have access to

a multitude of airline fares before purchasing tickets. This can allow a

flight-comparison website to grab some market-share. It seems implausible

however, for any of the established websites belonging to airlines to offer

such a service, as it would be counter-intuitive. Logistical problems

stemming from certain airlines operating in certain routes may also dampen

the impact of any such comparison websites.

The majority of the websites offering similar features leaves very little room

for platform differentiation.

Considering the factors stated above, Winner-Take-All effects are not likely

to prevail in this online market.

75

Based on the analysis carried out so far, and the power curve figures, the

structure of this B2C-focused market is likely to remain the same for the

foreseeable future with no dominant players emerging.

Despite the seemingly stable nature of this market structure, major

technological advances may bring about the emergence of a dominant

player. SITA [48], an IT company specialising in Aviation IT solutions, has

published a report highlighting technological advances which may change

the nature of air travel over the next 5 years. Web 2.0 and Software Oriented

Architecture (SOA) are among the technologies discussed. These

technologies may enable sites to differentiate their platform from others and

gain a competitive advantage. A discussion of the specific nature and

benefits of such features is beyond the scope of this document. Interested

readers can consult [49].

6.1.2. Consultancies

A close inspection of the websites in this category shows these sites are

mainly used to raise brand awareness and provide information about the

services their respective companies offer. This, to some extent differs from

the other (B2C-focused) markets analysed during the course of this study.

Therefore it is important to acknowledge that visits to sites in this category

do not necessarily have a direct correlation with their respective companies'

revenues. The informative nature of such websites is also partly responsible

for the relatively “flat” power curve observed in this “intangible-rich”

online market.

The leading websites in this sector, with the exception of CRM Metrix,

76

belong to large (three of the top four companies are part of the big four

auditors), established consultancy firms, with many years experience in the

field. This highlights the importance of company size, and established brand

names in the level of use amongst users of these websites.

CRM Metrix are a niche market consultancy service focusing on internet

marketing solutions. The fact that CRM Metrix, given its small size relative

to the “consultancy giants” in this category, and brief history of existence

(established in 2001), has been able to capture such a considerable share of

the online market is of interest. After carrying out a careful study of this

website (www.crmmetrix.fr) and the upstream websites visited before, i.e.

websites which the user has been re-directed from to reach the current

location website, it became evident that a large number of visitors (on

average 65% of the visits since September 2008) had been re-directed there

from Coca-Cola's “Coke Zone” website (http://www.cokezone.co.uk). The

use of interactive marketing methods (a simple example of this is a well-

targeted link with a message such as “This survey has been powered by

CRM Metrix”) have allowed CRM Metrix to generate sufficient interest

among users visiting Coke Zone, and encouraging them to visit their own

website. By placing their own link on their managed brands' websites

(which can potentially see a higher volume of traffic dependant on their size

and nature), this small company has been able to steal a considerable share

of this rather competitive online market. This goes to show the significance

of in-house expertise and appropriate utilisation of internet marketing

techniques, as well as emphasising the importance of partnering skills to

gain a competitive advantage. See [50].

77

There are no strong signs of factors contributing to Winner-Take-All

outcomes in this market. Despite the gradual increase in market share levels

by the top two websites, the competitive nature of this market make it

difficult for one single website to outperform others and gain a dominant

position in the foreseeable future.

The factors determining the success of various websites belonging to

consultancy firms seem to be riding mainly on their size, reputation, and

partnering skills. However, the adept use of internet marketing techniques

can allow much smaller companies advance in such markets as well. CRM

Metrix's successful market share grab is testament to this.

6.1.3. Insurance

The online insurance industry has been identified as one which is mainly

focused on B2C services. The websites in this category, as well as serving as

an information point for users, offer a range of e-services. This implies the

number of visits to these websites can have an impact on their sales figures.

Insurance companies encourage consumers to purchase insurance policies

online by offering them better deals and discounts. The large overheads

normally seen in this industry (especially for certain types of insurance

policies, such as travel insurance) can be reduced significantly, and

successful websites are able to take advantage of economies of scale as the

unit cost for each insurance policy sold is reduced.

The leading websites in this category provide a range of services, which go

beyond plain insurance services. The AA's website provides a range of

financial (loans, credit cards, online banking, savings account, etc.), and

78

travel services (hotels, flight bookings and other modes of transportation,

route maps etc.), as well as car breakdown covers, new/second-hand car

sales, and driving lessons. The AA's route planner service

(http://www.theaa.com/route-planner/index.jsp) launched in 2004 [51],

makes use of Google Maps, creating a web mash-up to offer users travel

advice, as well as advertising hotels and other businesses/services with

which they are affiliated. Users can book holiday breaks on this website, and

take advantage of a range of services outside the company's core

competency area (e.g. flights), which has been made possible with

successful cross-industry partnerships.

Tesco Personal Finance, and Virgin Money UK have adopted a similar

approach, in that they provide a range of financial services such as loans,

credit cards, and savings accounts, in addition to their insurance services on

their website. One of the main contributing factors to the profitable

provision of such services are cross-industry partnerships (Tesco Personal

Finance, until 2008, was half owned by the Royal Bank of Scotland until

2008 [52], Virgin Money UK is partnered with the Co-operative Bank [53]

The One Account [54], and other organisations).

The fact that the services offered by many of these websites (the market

leaders in particular) go beyond that of plain insurance services should be

taken into consideration when drawing conclusions regarding the structure

of the online “insurance” industry. For example, it would be incorrect to

assume the large number of visitors to The AA website are all seeking

insurance services. However, the significance of a gradual expansion of

services into other areas, and effective partnering skills should be noted.

79

Most websites encountered in this sector appear to enjoy the financial

backing of larger organisations. This confirms the high-risk nature of the

market. Smaller organisations looking to provide insurance services on the

internet may need to seek the financial backing of a larger organisation

and/or consider forming alliances with other successful insurance

companies and financial institutions.

The rise of comparison websites such as Money Supermarket

(www.moneysupermarket.com), Gocompare (www.gocompare.com), and

Confused (www.confused.com), which have not been included in this

category (with market share levels comparable to the leading websites in

this category), signals the importance of price differentiation and the

relatively high level of trust among (normally cautious) online shoppers, as

they search for the cheapest insurance policies on the web. The existence of

legal and contractual obligations and policies by the regulating bodies (as

stated in the “terms & conditions” sections on these websites) appear to

have contributed to this.

At present, there are subtle indications of Winner-Take-All effects in this

market, however this may not be the case in the future.

Multi-homing costs, particularly in a monetary sense, are low, as searching

for various insurance policies and receiving online quotes are costless.

However, loyalty schemes such as ones adopted by Tesco Personal Finance

(earning Tesco Club Card points with the purchase of financial products)

can increase this.

The recent increase in the popularity of comparison websites signals

consumers' desire to have access to, and compare different insurance

80

policies on various websites. Some market leaders such as Tesco, have

realised this and tried to capitalise on it. See Tesco Compare [55]. However,

this type of service can only be provided by those insurance websites (not to

be confused with comparison websites which don't offer any insurance

services of their own) with the ability to offer consistently low prices. These

companies are likely to be larger and more powerful than other competitors.

A more detailed discussion of comparison websites and their effects is

beyond the scope of this document.

There is not much difference from a functionality point of view amongst the

websites studied, however, The AA appears to be the only website taking

advantage of Web 2.0 technologies (with its route planner service).

The flat power curve associated with this “tangible”, B2C-focused online

market meets expectations.

6.1.4. Search Engines

This market is clearly dominated by one major player: Google. Google is

not only the top ranking website amongst search engines, but the most

visited website out of all websites visited from within the UK (based on data

from Hitwise).

The search engines networked market has three sides: the “users” who

search for information and content on the web, the “content providers”

(website owners/managers) who provide the content searched for, and

another group of content providers which will be referred to as the

“advertisers”. The latter also consists of websites/website owners, but with

the difference that they pay to have their content made available on

81

“sponsored links” lists or in favourable positions on the organic results list.

Search engine users value the ability to have access to a large number of

relevant websites (content providers) when they carry out a search.

Advertisers enjoy having access to as many users as possible, due to larger

amounts of revenues being generated from more visits. Content providers

too, value the ability to interact with large numbers of users, as they gain

more exposure to provide their information, generate revenues, and/or sell

their products/services. However, it is important to note that content

providers have very little say in their websites appearing on the list of

results returned by a search engine. These factors have made the search

engines market one with strong network effects.

The Google platforms (google.com, google.co.uk, etc.) have been able to

differentiate themselves from other search engines based on their

functionality and the quality of service that they provide. This is mainly due

to their sophisticated search algorithms, and their unexampled distributed

computing platforms.

Multi-homing costs associated with using search engines are not particularly

high, as internet searches are typically free and speedy. However, Google's

ability to consistently provide relevant and informative results leaves little

room for users who experience their service to go elsewhere.

A combination of strong network effects and differentiated platform

functionality have created a Winner-Take-All platform structure in this

market, with Google capturing 91% of the market share.

The search engines market structure in the twelve months prior to this study

82

appears highly stable, with Google maintaining its high share of the market.

For this reason, it is difficult to predict any major changes to this market

structure in the foreseeable future.

The gradient of the power curve associated with this market indicates the

huge amount of inequality between the top rank and lower ranks. This meets

expectations due to strength of network effects and the intangible nature of

this market. The structure of this online market is likely to remain the same

in the foreseeable future.

6.1.5. Social Networking Services

This highly concentrated market is dominated by Facebook. Facebook's

average share of visits over the past twelve months, across all categories of

websites (accessed from within the UK), are second only to that of Google.

Over the same period, the share of visits for Facebook in the social

networking websites category have increased by 20%, while its main rivals

which provide a similar service have suffered considerable market share

losses.

First, and early mover advantages are mainly discussed from a strategic

management standpoint, and in relation to traditional markets. In recent

years, however, the concept has been borrowed and extended to explain

electronic market outcomes as well. For example, PayPal, founded in 1999,

was the first peer-to-peer (P2P) payment market of its kind. By capitalising

on its first-mover advantages and network effects (as buyers and merchants

started adopting it), it was able to quell eBay's Billpoint, resulting in eBay's

decision to close Billpoint and acquire PayPal in 2002 [56].

83

Considering the nature of social networking services, it can be seen why the

users of such services might highly value the ability to access other potential

users of such services. However, in the context of social networks, network

effects are said to take place once a website reaches critical mass. See [57].

“Critical mass in social network research is a sociodynamic term used to

describe the scale of a social system at which the system becomes self-

sustaining and fuels further growth.” [58] Thus, early movers in this

market, do not necessarily gain a competitive advantage until they reach

critical mass. This is seen as a contributing factor to Facebook's ability to

catch up with older rivals such as MySpace.

The websites considered in this category offer various features, in an

attempt to differentiate their platforms from others, however, the services

offered by Facebook are considered to be of similar nature to that of rivals

Bebo, and MySpace. In contrast, Twitter , by providing micro-messaging

services, offers slightly different functionality to other websites. This can

explain why it has been enjoying a growth in online market share (by 3%),

despite some of its older and better-know rivals losing market share.

The multi-homing costs associated with this networked market are not high

in a monetary sense as none of the websites in this category require

membership fees. However, the time spent on these networks and the

inconveniences associated with managing multiple profiles on multiple

websites can deter users from multi-homing.

A combination of the factors stated, and the sharp increase in concentration

levels in this market, indicate the possibility of WTA outcomes prevailing

in this market, with Facebook emerging as the dominant player.

84

The intangible nature of the services offered in this category (most

technology-related industries are considered to be intangible, due to high

reliance on the talent of individuals involved) justify its relatively steep

power curve, signalling high levels of inequality amongst firms.

6.2. Cross-industry Analysis

Five vertical markets within three major industries - aviation, business and

finance, and computers & the internet - have been considered in this study.

The discussion of results above, indicates the different nature of these

vertical markets. A number of models (such as HHI, WTA, and power

curves) have been employed to interpret their respective structures.

The two vertical markets in the “computers and internet” industry exhibit

strong WTA effects and high levels of concentration, i.e. dominated by a

small number of sites. These are search engines and social networking

services. These markets consist of “click-only” companies which have no

presence in the physical market and conduct all transactions online. Thus,

their share of visits can be interpreted as a measure of their performance.

They both enjoy strong network effects and are intangible-rich.

The majority of companies in the remaining three industries are “click-and-

mortar” companies, i.e. traditional “brick-and-mortar” companies, now

extending their operations by adding e-marketing. Note that a small number

of websites within these categories, only exist online (e.g.

eCarinsurance.co.uk). They display weaker WTA effects, with lower levels

of concentration, signalling higher levels of perceived risk and competition.

One of the main driving factors behind the popularity of sites in these

85

markets (with lower concentration levels) is their ability to maintain

successful partnerships with other (online and offline) organisations. This

allows them to advance the scope of their services, and gain a competitive

advantage. This is particularly noticeable in the airline and insurance

markets.

The consultancy market is intangible-rich by nature, due to its reliance on

the talent and skills of individuals. The sites in this category are mainly used

to promote brand awareness, as opposed to focusing on sales or e-services.

Thus, the share of visits for websites in this category, do not necessarily

have a direct correlation with their performance in the physical market.

Nevertheless, the top ranking websites in this category, with the exception

of CRM Metrix, belong to larger consultancy firms. These websites enjoy a

lower level of overall visits. Furthermore, this category is the only B2B-

focused category considered in this study. These factors can explain the low

levels of industry concentration observed in this intangible-rich market.

Price-based competition in the online aviation industry, and to a lesser

extent, online insurance (partly due to high levels of user trust), has driven

companies within these sectors to take advantage of e-commerce in order to

remain competitive.

Larger, better-established websites, with good reputations, appear to enjoy a

higher number of visits than other sites within the click-and-mortar group of

companies .

A number of state-of-the-art web technologies which have been (or are

about to be) utilised in each market have been identified. Web 2.0

technologies and mash-ups are the most cited among these.

86

Table 6 provides a summary of the characteristics and measures of industry

concentration in each identified online market.

87

Table 6: Cross-industry Analysis - Summary: Characteristics, Results, and Measures of Industry Concentration

Vertical Market Market Characteristics CR HHI

Commercial Airlines 1.1 0.94 57% 0.11

Consultancies 0.97 0.93 53% 0.12

Insurance 1.27 0.99 70% 0.18

Search Engines 2.65 0.84 100% 0.84

Social Networks 2.49 0.95 97% 0.56

Low-Moderate concentration, click-and-mortar, B2C-focused, site visitors interested in purchasing tickets and researching prices, competition mainly on price, e-commerce crucial to remain competitive (particularly with low-cost carriers), partnering skills important, weak WTA effects, tangible assets, state-of-the-art: Web 2.0, SOA

Low-Moderate concentration, click-and-mortar, B2B-focused, site visitors interested in researching and using as information source, size, experience, and reputation important, weak WTA effects, intangible assets, state-of-the-art: interactive marketing techniques Moderately concentrated, click-and-mortar, B2C-focused, site visitors interested in purchasing services and comparing prices, high user trust so competing mainly on price, larger sites providing wider range of services are more popular, financial backing and partnering skills important due to high risks, subtle WTA effects, e-commerce important cost-cutting tool, tangible assets, state-of-the-art: Web 2.0Highly concentrated, click-only, general (B2C and B2B),site visitors interested in getting the most informative search results, strong WTA effects mainly due to differentiated platform functionality and network effects, intangible assets, state-of-the art: advanced search algorithms, distributed computing platformsHighly concentrated, click-only, general (B2C and B2B), site visitors interested in social networking, WTA outcome likely due to strong network effects (passed critical mass), intangible assets, state-of-the-art: Web 2.0, Micro-messaging

R2

7. Conclusions

Carrying out a systematic analysis of the chosen vertical markets has

enabled the author to draw conclusions about online markets of similar

nature. The implications of which go beyond the studied markets.

The market concentration levels in all industries demonstrating strong WTA

effects have proven to be very high, with the dominant website being a

number of times larger than the next popular website. The use of Winner-

Take-All effects for online markets has provided remarkably accurate

portrayals of all the studied sectors. This gives particular importance to the

use of such models in assessing online market structures. Moreover,

throughout this project, the figures generated from HHI, power curves

(gradients), and to a lesser extent CR, have proven to be consistent in

relation to the observed structure of the online markets.

An empirical regularity in the share of visits enjoyed by various websites

was observed. These observed patterns (of unique visits to websites) are

accurately represented by the power law. Thus, power curves can provide a

novel approach for assessing online industry structures. This argument has

been substantiated both by the gathered data, and by the industry

concentration levels seen in various online markets. The derived power

curves in each online market have been re-plotted using logarithmic scales.

Recall the standard test for determining whether a statistical distribution

follows the power law (by re-plotting it using logarithmic scales) discussed

in the Research Methodology chapter. These log-log plots are available in

the appendix section. Note that high variations from the straight line, i.e.

88

lower R2 values, are mainly caused by the small sample size.

Power curves can also be utilised as an important tool in order to benchmark

an online industry's market structure. This can help determine a website's

current standing in relation to other websites and to measure deviations from

the expected norm.

The existence of appropriate rules and policies set by regulating bodies and

industry observers can contribute to higher levels of trust among internet

users. Aided by the free flow and availability of information on the web,

these consumers are able to focus on finding the lowest prices. This process

can drive competition in online markets suffering from low levels of

differentiation, based on the nature of their core products.

Competing on price in moderately concentrated online markets requires the

appropriate usage of e-commerce and digital systems to reduce overheads

and increase productivity levels. Companies with better internal capabilities

and higher levels of IT competency are likely to be more efficient at

conducting business over the internet. Furthermore, for a website to gain a

competitive advantage in such an industry, it needs to develop profitable

relationships with online/offline cross-industry organisations. This would

allow a gradual expansion of its services (typically accessible from the same

location/site), which leads to generating a larger customer (visitor) base.

Appropriate partnering skills are also seen as a requirement to enter high-

risk markets such as (online) insurance.

Customer retention is particularly important for websites competing in

moderately concentrated industries suffering from low multi-homing costs

89

(usually brought about by the one-off nature of transactions). These

websites can encourage users to re-visit their sites, and increase the costs

associated with multi-homing, by providing easy-to-navigate websites and

introducing loyalty schemes.

Online markets consisting of websites whose main functionality is to

promote brand awareness and serve as an information point for interested

businesses and individuals (e.g. consultancies), are likely to be dominated

by reputable sites belonging to larger organisations which are high in

experience levels. However, the use of state-of-the-art online marketing

techniques in these seemingly competitive markets (with weaker WTA

effects) may allow sites belonging to much smaller organisations to

advance and gain considerable market shares.

The previous chapter highlighted how early mover advantages in social

networks on the internet can be realised once a site has become self-

sustaining (reached critical mass). The author argues this will be the case in

many online markets which exhibit strong WTA effects, particularly in ones

showing strong network effects and/or highly differentiated platform

functionality. In other words, early mover advantages can only be realised

once a website gains a dominant position by taking advantage of its strong

network effects, superior functionality or design, and to a lesser extent with

high multi-homing costs. The Google search engine's dominance in its

respective category provides a suitable example supporting this argument.

Businesses in all studied sectors are likely to benefit from incorporating

state-of-the-art technologies on their sites. Most notably Web 2.0

technologies. Pioneering websites which take advantage of these

90

technologies are likely to gain a competitive advantage and increase their

online market shares.

The following recommendations for further study have been made:

1. One main criticism of the ISP method is that it cannot capture the

full range of user activities online, particularly after the user has

accessed a website and has started using the various features

provided on the site. For instance, conducting transactions in secure

mode (https) cannot be captured using this method. Researchers

using internet research companies which use the ISP method can't be

sure whether a user is merely browsing a website or making a

purchase (given the website has a product/service for sale). This

calls for making use of ISPs & Panel data together in an attempt to

both capture the whole range of online user activities, and have a

dataset that is representative and diverse in nature. However,

researchers need to be aware of, and address the complications

arising from the possibility of two (or more) derived datasets not

representing the same sets of users.

2. The significance of comparison websites were highlighted in this

paper. These services are especially important in markets which

compete mainly on price, such as insurance and aviation.

Comparison websites can have powerful impacts on these market

structures and can further increase competition levels. These

websites are enjoying a growth in visits by the day, and are

competing with leading insurance websites. Thus, further studies on

the nature and implications of these services are required to analyse

91

this growing online trend.

3. The significance and impact of the internet and e-commerce can

truly be realised when it is considered globally. The various shares of

visits for websites were limited to the UK region. While the

frameworks discussed are particularly relevant for click-and-mortar

companies which have offices in the UK and conduct business there.

This may not be the case for click-only companies which have no

physical presence and conduct business on a more global scale. This

calls for an analysis of global online trends and shares of unique

visits to confirm the ideas discussed in this paper, and perhaps

provide a more comprehensive framework in internet marketing.

4. In the instances where click-stream figures have been used, they

have provided meaningful explanations regarding online user

behaviour, and the source of websites' visits. Due to constraints set

by time and the scope of this study, click-stream data was not fully

explored. This calls for further studies to be carried out in this area.

5. Finally, in order to fully analyse click-and-mortar websites, data

capturing the performance and size of companies in the physical

world need be utilised. This will allow more comprehensive

analyses, as well as a comparison of online markets with their

traditional counterparts to be carried out.

92

Appendix A: Industry Power Curves – Logarithmic Scale

93

Figure A1: Commercial Airlines Power Curve – Logarithmic Scale

1 10 1001

10

100

1000

f(x) = 156.3 x̂ -1.1R² = 0.94

Commercial Airlines - 2009, 1st quarterLogarithmic Scale

Rank by Popularity

Figure A2: Consultancies Power Curve - Logarithmic Scale

1 10 1001

10

100

f(x) = 101 x̂ -0.97R² = 0.93

Consultancies - 2009, 1st quarterLogarithmic Scale

Rank by Popularity

94

Figure A3: Insurance Power Curve - Logarithmic Scale

1 10 1001

10

100

f(x) = 116.52 x̂ -1.27R² = 0.99

Insurance - 2009, 1st quarterLogarithmic Scale

Rank by Popularity

Figure A4: Search Engines Power Curve - Logarithmic Scale

1 101

10

100

f(x) = 60.81 x̂ -2.65R² = 0.84

Search Engines - 2009, 1st quarterLogarithmic Scale

Rank by Popularity

95

Figure A5: Social Networking Power Curve - Logarithmic Scale

1 101

10

100

f(x) = 107.21 x̂ -2.49R² = 0.95

Social Networking - 2009, 1st quarterLogarithmic Scale

Rank by Popularity

Appendix B: Consumer Behaviour Models

Consumer Behaviour

“Consumer behavior is defined as activities people undertake when

obtaining, consuming, and disposing of products and services.”[59]

Consumer behaviour is influenced by a number of cultural, social, and

personal factors.

“Culture is the fundamental determinant of a person's wants and behavior.

The growing child acquires a set of values, perceptions, preferences, and

behaviors through his or her family and other key institutions.”[60] Cultural

factors have been split into smaller subcultural elements in order to produce

a more precise definition of cultural influences on the consumer. These

subcultures help study the buying behaviour associated with race,

nationality, religions, and geographical areas. Classification based on social

class is another way of determining consumer behaviour. This is because the

assumption may be made that members of a certain social class hold similar

values, behaviour, and interests.

Social factors like family, reference groups, and social roles and statuses

also affect customer behaviour. Reference groups have direct or indirect

influence on the buyer's behaviour. These groups include the buyer's family,

friends, co-workers, neighbours, etc. People resume various roles in society

and sometimes a buyer will make a purchase based on or influenced by their

social status. For example a manager who buys a sports car may have made

that purchase based on the influence of social status.

Personal factors include occupation and economic circumstances, as well as

96

age, personality, self-concept, lifestyle, and value. Marketers need to pay

close attention to age and gender, in order to differentiate between different

preferences in goods and services among these groups. Consumption

patterns are also influenced by occupation and economic circumstances,

lifestyle, and personality.

Psychological factors are another major influence in the buying decision

process. These include beliefs, learning, motivation, and perception.

The Buying Decision Process

“The Internet has changed the process of information search. Today's

marketplace is made up of traditional consumers (who do not shop online),

cyber-consumers (who mostly shop online), and hybrid consumers (who do

both). Most consumers are hybrid.”[61] There are five stages consumers go

through when making a purchase decision. However, depending on the

circumstances, customers may skip a few steps or backtrack. (e.g. a

Manchester United Football Club fan may skip stages 2, 3, and 4 and jump

straight to the purchase stage after becoming aware that tickets for a certain

match have gone on sale.) This is the five-stage model developed by

marketing scholars to describe the buying decision process:

1. Problem Recognition: An event or thought process triggers the need

for a product or service. By studying consumers, marketers try to

identify circumstances that trigger a particular need/desire.

2. Information search: Now aware of the particular need, the

costumer is in a sense of “heightened attention” and has become

97

more receptive to information about different types of the product or

service of interest. The person may now conduct an information

search, which may be done through a variety of ways such as asking

a friend, performing an online search, or looking at catalogues in

order to gain some information about the desired product. Marketers

are interested in the main sources of information the consumer turns

to. “These information sources fall into four groups: Personal

(friends and family), Commercial (Web sites, sales-persons,

Advertising, packaging, etc.), Public (consumer-rating organisations,

mass-media), and Experimental (by using the product). Most

information is generally received via commercial sources (this

estimate varies across industries), however the most effective

sources of information are personal or public ones.

3. Evaluation of alternatives: In this stage, consumers generally form

their decision on a rational basis and are likely to try and evaluate

and select from various products. Beliefs and attitudes play a major

role in influencing people's buying behaviour as well.

4. Purchase decisions: The consumer has now gone through an

evaluation stage and by now may have decided on a brand to

purchase. Consumers can make five sub-decisions when deciding

on a purchase: brand, dealer, quantity, timing, and payment method.

5. Postpurchase behaviour: “The next stage of consumer decision

making is post-consumption evaluation, in which consumers

experience a sense of either satisfaction or dissatisfaction.

Satisfaction occurs when consumers' expectations are matched by

98

perceived performance. When experiences and performances fall

short of expectations, dissatisfaction occurs. These outcomes are

significant because consumers store their evaluation in memory and

refer to them in future decisions.”[62]

99

References[1] Paul Rincon, BBC News Website, "What happened to the Big Bang machine?", 2008, Available from: http://news.bbc.co.uk/2/hi/science/nature/7627631.stm [Accessed April 10 2009]

[2] The Economist, "What's the score?; Twenty years of the world wide web", 2009, Available from: http://www.economist.com/sciencetechnology/displaystory.cfm?story_id=13277389 [Accessed April 21 2009]

[3] D. Quinn Mills, Harvard Business Publishing, "Who's to Blame for the Bubble?", 2001, Available from: http://harvardbusiness.org/product/who-s-to-blame-for-the-bubble/an/F0105A-PDF-ENG [Accessed April 21 2009]

[4] Nicholas Carr, Harvard Business Publishing, "IT Doesn't Matter", 2003, Available from: http://harvardbusiness.org/product/it-doesn-t-matter/an/R0305B-PDF-ENG [Accessed April 21 2009]

[5] The Economist, "The end of the free lunch--again; Internet companies", 2009, Available from: http://www.economist.com/opinion/displayStory.cfm?story_id=13326158 [Accessed April 25 2009]

[6] Aaron Smith, Pew Internet and American Life Project, "The Internet's Role in Campaign 2008", 2009, Available from: http://www.pewinternet.org/Reports/2009/6--The-Internets-Role-in-Campaign-2008.aspx [Accessed April 15 2009]

[7] "World Internet Usage Statistics", 2009, Available from: http://www.internetworldstats.com/stats.htm [Accessed 19 August 2009]

[8] Office for National Statistics, "National Statistics Online", 2009, Available from: http://www.statistics.gov.uk/CCI/nugget.asp?ID=8 [Accessed 25 August 2009]

[9] Arik Hesseldahl, BusinessWeek, "Obama's Broadband Plan", 2009, Available from: http://www.businessweek.com/magazine/content/09_03/b4116027365196.htm [Accessed April 13 2009]

[10] BBC News Website, "Government backs 2Mbps broadband", 2009, Available from: http://news.bbc.co.uk/1/hi/technology/8012848.stm [Accessed April 21 2009]

[11] , McKinsey Quarterly, "How companies are marketing online: A McKinsey Global Survey", 2007, Available from: http://www.mckinseyquarterly.com/How_companies_are_marketing_online_A_McKinsey_Global_Survey_2048 [Accessed April 22 2009]

100

[12] Sarah Monroe, David Sinclair, and Tobias A. Wachinger, McKinsey Quarterly, "Understanding online shoppers in Europe", 2009, Available from: http://www.mckinseyquarterly.com/Understanding_online_shoppers_in_Europe_2353 [Accessed 12 August 2009]

[13] Gary Armstrong and Philip Kotler, "Marketing: An Introduction", 7th

edition, Prentice Hall, 2005

[14] CIO.com, "E-Commerce Definitions and Solutions", 2007, Available from: http://www.cio.com/article/40298/ABC_An_Introduction_to_E_Commerce [Accessed 23 April 2009]

[15] Sucharita Mulpuru, Forrester, "eBusiness and the Economy", 2008, Available from: http://www.forrester.com/Research/Document/Excerpt/0,7211,47583,00.html#heading3 [Accessed 20 August 2009]

[16] Gary Armstrong and Philip Kotler, "Marketing: An Introduction", 7th

edition, Prentice Hall, 2005, 487-492

[17] T. W. Malone, R. I. Benjamin and J. Yates, "Electronic Markets and Electronic Hierarchies", Communications of the ACM, vol. 30 (6), pp. 484-497

[18] Ibid. , p. 497

[19] Shan Wang & Shi Zheng & Lida Xu & Dezheng Li &Huan Meng, "A literature review of electronic marketplace research: Themes, theories an", Information Systems Frontiers, vol. 10 (5), pp. 555-571

[20] Markus Lenz, Hans-Dieter Zimmermann and Mark Heitmann, "Strategic Partnerships and Competitiveness of Business-to-Business E-Market", Electronic Markets, vol. 12 (2), pp. 100-111

[21] Shan Wang & Shi Zheng & Lida Xu & Dezheng Li &Huan Meng, "A literature review of electronic marketplace research: Themes, theories an", Information Systems Frontiers, vol. 10(5), pp. 555-571 (p. 567)

[22] Thomas R. Eisenmann, Harvard Business Publishing, "Winner-Take-All in Networked Markets", 2006, Available from: http://harvardbusiness.org/product/winner-take-all-in-networked-markets/an/806131-PDF-ENG [Accessed 18 August 2009]

[23] Ibid. , p. 2

[24] Ibid. , p. 3

[25] Ibid. , p. 4

101

[26] Michele Zanini, McKinsey Quarterly, "Using ‘power curves’ to assess industry dynamics", 2008, Available from: http://www.mckinseyquarterly.com/Using_power_curves_to_assess_industry_dynamics_2222 [Accessed 15 June 2009]

[27] Hitwise Competitive intelligence, Available from: http://www.hitwise.co.uk [Accessed 20 May 2009]

[28] comScore Inc., Available from: http://www.comscore.com/ [Accessed 20 May 2009]

[29] WhatIs.com, "Definitions, computer terms, and tech glossaries", Available from: http://whatis.techtarget.com/ [Accessed 9 April 2009]

[30] comScore, Inc, Visibility, Available from: http://www.comscore.com/About_comScore/Methodology [Accessed 21 April 2009]

[31] "Google Analytics", Available from: http://www.google.com/analytics/ [Accessed 25 August 2009]

[32] Hitwise Competitive Intelligence, “About Us”, Available from: http://www.hitwise.com/uk/about-us/ [Accessed 12 September 2009]

[33] comScore Inc. , Methodology - Available from: http://www.comscore.com/About_comScore/Methodology [Accessed 25 August 2009]

[34] Hitwise, “How we do it”, Available from: http://www.hitwise.com/ca/about-us/how-we-do-it [Accessed 21 August 2009]

[35] Jason Burby, Angie Brown & WAA Standards Committee, Web Analytics Association, "Web Analytics Definitions", 2007, p. 9, Available from: http://www.webanalyticsassociation.org/attachments/committees/5/WAA-Standards-Analytics-Definitions-Volume-I-20070816.pdf attachments [Accessed 18 August 2009]

[36] Brian Everritt, "Cambridge Dictionary of Statistics", 2nd edition, Cambridge UK, 2002, p. 298

[37] Stat Trek, "Statistics Tutorial: Discrete and Continuous Probability Distributions", Available from: http://stattrek.com/Lesson2/DiscreteContinuous.aspx [Accessed 28 August 2009]

[38] M.E.J. Newman, "Power laws, Pareto distributions and Zipf’s law", Contemporary Physics, vol. 46 (5), pp. 323-351

102

[39] Michael Mitzenmacher, "A Brief History of Generative Models for Power Law and Lognormal Distributions", Internet Mathematics, vol. 1(2), pp. 226-251

[40] , OECD - Glossary of Statistical Terms, "Concentration Definition", ,, Available from: http://stats.oecd.org/glossary/detail.asp?ID=3165 [Accessed 14 August 2009]

[41] R. S. Khemani, OECD, "Glossary of Industrial Organisation Economics and Competition Law", Available from: http://www.oecd.org/dataoecd/8/61/2376087.pdf [Accessed 14 August 2009]

[42] U.S. Federal Trades Commission, "HorizontalMergerGuidelines", 1997, Available from: http://www.usdoj.gov/atr/public/guidelines/horiz_book/hmg1.html [Accessed 16 August 2009]

[43] IdeaWorksCompany.com, "Buckle Your Seat Belts - Airline Executives Predict More Fees and Plan to Sell More Services via Their Web Sites", 2007, Available from: http://www.ideaworkscompany.com/press/AnalysisAlaCarteTrends2007.pdf [Accessed 1 September 2009]

[44] Howstuffworks, "How Frequent Flyer Programs Work", Available from: http://communication.howstuffworks.com/ff-programs.htm/printable [Accessed 2 September 2009]

[45] Google, "Webmaster Guidelines", Available from: http://www.google.com/support/webmasters/bin/answer.py?answer=35769 [Accessed 3 September 2009]

[46] easyJet.com, "Key Events in our History", Available from: http://www.easyjet.com/EN/About/Information/infopack_keyevents.html [Accessed 3 September 2009]

[47] Ryanair, "About Us", Available from: http://www.ryanair.com/site/EN/about.php [Accessed 3 September 2009]

[48] SITA, "Home page", Available from: http://www.sita.aero/ [Accessed 4 September 2009]

[49] SITA, "Ten Technology Advances that will Change Air Travel", 2009, Available from: http://www.airport-technology.com/downloads/whitepapers/consultancy/file431/ [Accessed 4 September 2009]

[50] InternetRetailer.com, "CRM Metrix Experiences Record Growth During 2009 First Half", 2009, Available from: http://www.internetretailer.com/pressReleaseDetail.asp?id=31481

103

[Accessed 4 September 2009]

[51] The AA, "About Us", Available from: http://www.theaa.com/aboutaa/history.html [Accessed 5 September 2009]

[52] BBC News Website, "Tesco Planning big financial push", 2008, Available from: http://news.bbc.co.uk/1/low/business/7529544.stm [Accessed 5 September 2009]

[53] Virgin Money, "Loans", Available from: http://uk.virginmoney.com/virgin/splash/loans.jsp [Accessed 6 September 2009]

[54] Virgin Money, "Motgages", Available from: http://uk.virginmoney.com/virgin/mortgage/index.jsp [Accessed 6 September 2009]

[55] Tesco, "Tesco Compare", Available from: www.tescocompare.com [Accessed 6 September 2009]

[56] HowStuffWorks, "PayPal History", Available from: http://money.howstuffworks.com/paypal3.htm [Accessed 8 September 2009]

[57] J. Christopher Westland, "Critical mass and willingness to pay for social networks", 2008, Available from: http://www.sciencedirect.com/[Accessed 8 September 2009]

[58] Phillip Ball, "Critical Mass: How One Thing Leads to Another" , Farrar, Straus & Giroux, 2004

[59] Blackwell, Miniard, and Engel,"Consumer Behaviour", 10th edition, Thomson South-Western, 2006

[60] Philip Kotler & Kevin Lane Keller,"Marketing Management", 12th

edition, Prentice Hall, 2006

[61] Philip Kotler & Kevin Lane Keller, "Marketing Management", 12th

edition, Prentice Hall, 2006

[62] Blackwell, Miniard, and Engel, "Consumer Behaviour", 10th edition, Thomson South-Western, 2006

104