I would like to thank

Andrew Walker for initiating a new passion.

Jim Sterne, Marshall Sponder and David Meerman-Scott for

feeding my growing interest and being so accessible.

Michael Barnes for accompanying me during this project.

Last but not least, this project would not have been possible without

the support of my family, my friends, and my dear Anatole.

iii

One in every 13 people on earth uses Facebook (Facebook, August 2011). Last year in

the US, $716 million have been spent by businesses in social media and it should

reach $3.1 billion in 2014 (Forester, 2011). However, 84% of companies do not

measure the return of their investment (ROI) in social media (Mzinga and Babson,

2009). In this context, the question is: why do they keep spending more and more, if

they can’t measure?

This project aims at determining if social media performance can be measured. For this

purpose, it explores the relationship between social media and ROI, the irrationality

surrounding the field and provides a set of metrics and best practices to optimize

measurement. It is based on the hypothesis that performance can be measured,

involving a combination of qualitative and quantitative metrics, and keeping a degree of

apparent irrationality.

The literature review sets the framework of this research, clarifying the social media

environment, especially the craze around it and detailing the various motives that may

explain the corporate engagement. It describes the relationship between ROI and

social media, emphasizing on the clash of cultures and the issues that derive from

such incompatibility. It finally focuses on the technological aspect, web analytics, the

lack of widely accepted standards, the immaturity of most organizations and the new

metrics and practices that the business world is not ready to accept and integrate yet.

The research has been lead following the post-positivist philosophy accompanied with

a deductive approach. Mostly qualitative methods have been used, but a triangulation

with other methods has also been necessary to moderate biases and optimize

reliability. Worldwide figures of the sector have accepted to bring their viewpoints,

providing sound and advanced insights. They have opened areas and directions for

further research.

Major findings include the necessity to discriminate existing key terms, an animated

discussion around a potential social media bubble, main challenges to social media

performance measurement and strong advice on best practices.

The conclusion confirms that social media performance can be measured. This implies

a redefinition of key concepts and to introduce new practices. Irrationality has been

clarified and the existence of an economic bubble has been rejected for now.

Eventually this project proposes a new framework to optimize social media

performance measurement, including good practice rules and a new set of metrics.

Table of contents

I. Research question, hypotheses and objectives ................................................ 2 i. Research question ............................................................................................... 2 ii. Hypotheses ......................................................................................................... 3 iii. Research objectives ........................................................................................... 3

II. Clarifying the key terms....................................................................................... 3 i. Social Media ........................................................................................................ 4 ii. ROI...................................................................................................................... 4 iii. Web Analytics..................................................................................................... 5

1. Critical literature review............................................................................6

1.1. Social media....................................................................................................... 6 1.1.1. Definitions and current context ..................................................................... 6 1.1.2. Infatuation or necessity?............................................................................... 8 1.1.3. Irrationality: is there a social media bubble? ................................................ 9

1.2. Return on Investment (ROI) .............................................................................. 9 1.2.1. ROI and marketing: a clash of cultures? .................................................... 11 1.2.2. No ROI without measure – what to measure and how? ............................. 11 1.2.3. The qualitative element: lack of accountability or simple incompatibility? .. 13

1.3. Web Analytics .................................................................................................. 13 1.3.1. Definition and current context ..................................................................... 13 1.3.2. Aligning offline and online activities ............................................................ 14 1.3.3. Social media ROI, a new framework? ........................................................ 15

1.4. Research gap ................................................................................................... 16

2. Methodology ............................................................................................17

2.1. Research philosophy ...................................................................................... 17 2.1.1. The positivist approach: suitability and limitations. ..................................... 17 2.1.2. From positivism to post-positivism.............................................................. 19

2.2. Research approach ......................................................................................... 20 2.3. Research strategy ........................................................................................... 21 2.4. Research choice: data collection and analysis ............................................ 22

2.4.1. Multiple-methods ........................................................................................ 22 2.4.2. Secondary data........................................................................................... 23 2.4.3. Secondary data contribution to qualitative primary data gathering............. 23

3. Findings/results.......................................................................................26

3.1. Measuring social media performance ........................................................... 26 3.1.1. Discriminating key terms............................................................................. 26 3.1.2. Managing data ............................................................................................ 27

3.2. Social media and ROI...................................................................................... 28 3.2.1. The nature of the relationship ..................................................................... 28 3.2.2. The main issues.......................................................................................... 29

3.3. Level of irrationality ........................................................................................ 31

4. Analysis and discussion of findings .....................................................33

4.1. Context of social media and ROI ................................................................... 33 4.1.1. Cultural aspect......................................................................................... 33 4.1.2. Data ......................................................................................................... 34 4.1.3. Tools and technology ................................................................................. 36 4.1.4. Motives and objectives ............................................................................... 36

4.2. The issue of irrationality ................................................................................. 37 4.2.1. Lack of accountability ................................................................................. 37

v

4.2.2. Craze ...................................................................................................... 37 4.2.3. Bubble......................................................................................................... 37

4.3. Metrics and measurement .............................................................................. 38 4.3.1. Use of metrics............................................................................................. 38 4.3.2. Lack of standards ....................................................................................... 39 4.3.3. Frameworks ................................................................................................ 40 4.3.4. Best practices ............................................................................................. 41

Recommendations and conclusion...........................................................42

Appendices ..................................................................................................50

Bibliography ................................................................................................51

1

MEASURING SOCIAL MEDIA PERFORMANCE

___________________

Social Media are increasing their audience rapidly and consume nearly 25% of

people’s time online. The average visitor spends 66% more time on these sites

or platforms than they did a year ago. (Nielsen, June 2010). This is a worldwide

phenomenon.

As soon as this progress has been confirmed, the business world has dived into

social media: from a modest presence to sometimes a major investment

reaching millions of pounds, the corporate engagement and budgets allocated

to social media have been constantly increasing.

Forrester Research (2011) estimates that “in the US, $716 million was spent on

social media marketing in 2010, and that it will reach $3.1 billion by 2014”. At

that point, social media will be a greater channel than email or mobile, but still

quite smaller than search or display advertising.

The growth is rapid and widespread. 84% of companies and marketing

agencies have announced an increase of their social media investments in

2011 (E-consultancy, November 2010), no matter what economic sector they

operate in.

On average, social media investment represents 10% of the total marketing

budget of companies (9.9% in February 2010, Duke University, 2010) and is

expected to increase to 17.7 % within four years.

This new investment is funded either by increasing the overall marketing budget

(44 % of businesses expanded their marketing budgets in 2011 to fund their

social media strategy – Nielsen, 2011), but more often by reallocating existing

marketing lines to social media. As a result of the trade-off, telemarketing and

direct mail are experiencing the biggest declines in spending.

2

Human resources account for nearly 60% of social marketing expenditures,

including staff salaries for blogging, content development, social monitoring…

and 20% of the budget are fees for agencies, consultancies and other social

marketing service providers (Marketing Sherpa, 2010).

Marketers have been captivated by the new range of social media tools, and

their essential concern has been to identify which latest social media platform

they could add to the marketing mix. Still today, intuition seems to be the

ground for many decisions rather than achievable objectives. In May 2010, 52%

of marketers had no plan for social media (Digital Brand Expressions, 2010).

In such a context it is not surprising, as research has highlighted, that 84% of

organizations do not measure the return of their social media actions and

investment (Mzinga and Babson Executive Education, 2009). Now, given all the

above, why do organizations keep on substantially increasing their social media

investment? Behind this question lies the main issue of social media

performance evaluation, which is the topic of this research project.

The first step of the reflexion will consist in looking at the genuine natures of

both social media and ROI. On the one hand, social media are defined by

users’ interactions that intrinsically cannot be numerically quantified, or only

partially. On the other hand, ROI rests on quantifiable values. This inherent

incompatibility impacts on social media legitimacy, acceptability and credibility

within the business world.

I. Research question, hypotheses and objectives

i. Research question

This research aims at providing a theoretical and empirical contribution to the

issue of evaluation of social media performance, focusing eventually on the

return on investment in social media.

3

More specifically, it explores one main research question: “Is it currently

possible to measure a company’s social media performance. If yes, to what

extent?”

ii. Hypotheses

For the purpose of this project, two hypotheses are formulated.

First, that it is possible to measure social media performance, using a

combination of quantitative and qualitative indicators.

Second, that it involves a significant level of irrationality (possibly related to the

dot com bubble), which tends towards impossibility to accurately measure it.

iii. Research objectives

The research objective is to investigate the context of social media investment

and ROI calculation within companies, assessing the current context and

challenges.

Therefore the research will first clarify the current and complex relationship

between social media investment and ROI culture.

It will then identify the extent of irrationality around increasing corporate

investment in social media.

Last but not least, it aims at providing a set of metrics and good practice rules to

optimize social media ROI calculation.

II. Clarifying the key terms

Before exploring the subject in further details and critically reviewing the

literature, key research terms must clarified.

For each of these key concepts, social media (SM), Return on Investment (ROI)

and Web analytics will be presented and further discussed in the critical

literature review.

4

i. Social Media

In the introduction of his book “Social Media Metrics”, Sterne (2011) suggests

that social media “allow anybody to communicate with everybody. In other

words, consumer-generated content distributed through easy-to-access online

tools.”

The term social media basically refers to web-based and interactive technology

and communication tools. Kaplan and Haenlein (2010) define it as “a group of

internet-based applications that build on the ideological and technological

foundations of web 2.0. and that allow the creation and exchange of user-

generated content”. A key element is social interaction.

Social media can take different forms and are used for many different purposes.

Hence, many categorisations have been developed. For the purpose of this

research, the Interactive Advertising Bureau (2009) classification has been

selected. It breaks down social media into three main categories: (1) social

media sites, (2) blogs, and widgets and (3) social media applications. This

classification will favour a reliable analysis of observations.

Among all social media platforms available, this research project will focus on

social media sites, such as Facebook, Myspace or LinkedIn, as this is where

companies are investing the most, and therefore where the ROI challenge

stands.

ii. ROI

Organizations aim at being profitable. Logically, any spending needs to be

justified, in terms of return (ROI) whether it is contribution to profit, market share

or strategic positioning among others.

Investment is commonly linked to profit. Profit can be defined as “the return that

a company makes on the resources it has invested. Investors expect to see a

profit return in line with the amount of money invested. The main measures of

profits used therefore are return on capital or return on investment” (Lomax and

Raman, 2008).

5

In finance, the return is the measure of the result of any investment. The ROI is

a ratio that correlates the money gained or lost on an investment and the

amount of money invested.

There are several methods to measure ROI, but the most common calculation

(which will be used in this research) is the following:

ROI = (Gain of investment – Cost of investment)

Cost of investment

In the context of social media, only half of the equation seems to be

measurable: the amount of the investment. The other half is hardly measurable.

How to measure the presence or quantify the interactivity and relate it to the

money invested?

Lise Brende, director of marketing analytics at Bing, explains in The Wall Street

Journal her difficulty to convince executives “to cut big checks” for social media

without traditional analytics to prove its value (2011). Brende suggests a “lack of

a ratings system” as one reason for this challenge.

A source of inspiration for successful performance measurement would be the

new analytical and monitoring tools introduced by web analytics.

iii. Web Analytics

The Web Analysis Association (WAA) (2011) proposes a concise definition:

“web analytics is the measurement, collection, analysis and reporting of Internet

data for purposes of understanding and optimizing web usage”.

Chaffey (2008) defines it more specifically as “the techniques used to assess

and improve the contribution of e-marketing to a business including reviewing

traffic volume, referrals, clickstreams, online reach data, customer satisfaction

surveys, leads and sales”.

Web analytics are used by a minority of organizations. However, it is gradually

democratizing along with the increase of the maturity level of the companies.

However, as it will develop, the limitations of web analytics come from the fact

that they deal with figures and quantities only.

6

The various aspects of these three central concepts will now be further

investigated in the available literature.

1. Critical literature review

As announced above, this literature review will cover the areas of social media

(SM), Return on Investment (ROI) and Web analytics. It will emphasize on

previous research insights and arguments, which can contribute to a better

understanding of the subjects addressed. It includes previous academic

research, academic journals, books and industry and marketing reports. We will

follow Saunders et al. (2009) when he defines literature review as a “detailed

and justified analysis and commentary of the merits and faults of the literature

within a chosen area, which demonstrates familiarity with what is already known

about [the] research topic”.

As a first step, conceptual clarification on the above-mentioned key terms will

be discussed, and definitions will be put into perspective.

1.1. Social media

1.1.1. Definitions and current context

One of the most comprehensive descriptions of the social media phenomenon

has been provided by Joshi (2007). “The past few years have seen the advent

of Web based social media systems such as blogs, wikis, media-sharing sites

and message forums. Such Web 2.0. systems have a significant amount of user

generated content, and have become an important new way to publish

information, engage in discussions and form communities around the Internet.

Their reach and impact is significant with tens of millions of people providing

content on a regular basis around the world.” (Joshi et al., 2007).

Indeed, the phenomenon is considerable. With over 750 million users,

Facebook is now used by 1 in every 13 people on earth, with over 50% who log

in every day» (Facebook, 2011). On a more local scale, an eMarketer survey

has highlighted that as of February 2011, “88% of Brits used social networking

sites” (eMarketer, February 2011).

7

The phenomenon is new, diverse, somewhat controversial, and the existing

literature does not provide a uniform definition.

Kaplan and Haenlein’s definition has been presented in the introduction (2010).

On the other hand, Scott and Jacka (2011) define it as “the set of Web-based

broadcast technologies that enable the democratization of content, giving

people the ability to emerge from consumers of content to publishers.”

This lack of uniformity mentioned above also occurs when it comes to

suggesting a classification.

Kaplan and Haenlein (2010) identify six types of social media: collaborative

projects (e.g. Wikipedia), blogs (e.g. Blogger) and microblogs (e.g. Twitter),

content communities (e.g. Flickr, Youtube), social networking sites (e.g.

Facebook), virtual game worlds (e.g. World of Warcraft) and virtual social

worlds (e.g. Second Life).

Sterne (2011) breaks down the social media catalogue into six categories:

forums and message boards, review and opinion sites, social networks,

blogging, microblogging, bookmarking and media sharing.

The IAB (2009) divides social media into three main categories: social media

sites, blogs, and widgets and social media applications.

The above classifications tend to describe the different platforms and tools

available. However, the IAB also focuses on the uses and benefits. For the

purpose of this research the IAB classification will be used, with a focus on

social media sites:

“Social media Sites are characterized by the inherent functionality that

facilitates the sharing of information between users within a defined network.

The nature of Social media allows for the initiation of conversation by either

party, a key differentiator from established broadcast channels” (IAB, 2009). “It

has added a participatory element where an individual not only receives

information but has the ability to take part in the creation and distribution of

content” (IAB, 2009).

8

1.1.2. Infatuation or necessity?

The growth of social media is comparable to a revolution (Smith, 2009) and the

case of Facebook is revealing. In 2010, the company generated revenues of

about $2,000 billion, a growth of 158% (Bloomberg, 2010). The attraction to

social media is like “an abiding love affair of consumers with social networks”

says Weide, an analyst at IDC (2010).

Logically, the business world has been attracted by this enormous population of

consumers, and quickly decided to engage in this new world. In 2010, Weide

could say “all the big brands are there” (Bloomberg, 2010). However, this initial

move from the companies was intuition-based (on the volume i.e. number of

users). It then gradually evolved.

In 2009, Tia Fisher identified two major reasons for companies to enter the

social media arena. First, Social media is where actual and potential customers

are interacting, and it shapes how they think. Then, because competitors are

already doing it, and “a company’s absence is only highlighted by such

accessibility” (Alston, 2009).

In 2011, Blanchard added that “social media is a communication tool, like the

telephone and email, that serves the purposes of critical business functions,

including public relations, marketing, lead generation, customer service, and

market research. Some of the best uses for social media activities are in

support of these particular business functions, and here is the best part: each

one of these business functions already has its very own objectives and targets,

which you can plug your social media into” (Blanchard, 2011).

Most of businesses, either small or large, record at least a presence on social

media, but above all allocate it with an increasingly significant budget. However,

surprisingly enough, 84% of companies do not measure ROI of social media

activities (Mzinga and Babson Executive Education, 2009) and 80%

acknowledged the difficulty in tracking ROI in the medium (e-consultancy,

2010).

How can this investment be constantly growing if its efficiency ie the ROI is not

measured? Or do they measure differently? How can they justify this spending?

9

1.1.3. Irrationality: is there a social media bubble?

With a lack of ability to measure performance and return, one could argue that

the gradual growth of the investment is irrational, defined as « not logical or

reasonable » (Oxford, 2011).

Some people may even consider that in the absence of a reliable evaluation of

its efficiency, this growing investment is comparable to the formation of a

bubble. An economic bubble is defined as "trade in high volumes at prices that

are considerably at variance with intrinsic values" (King et al., 1993). Because

they are groundless, bubbles are always ephemeral. Bubbles cycles involve a

boom (prices reaching ‘absurd’ levels), followed by a crash (sudden drop of

prices).

The recent level of acquisitions of social media companies such as LinkedIn or

Skype have unveiled figures way above the traditional acquisition ratios.

Thompson (2011), senior editor at The Atlantic said that “LinkedIn has the

highest price-revenue ratio of any stock anywhere”. Answering Thompson,

Robak (2011), president of Pluris Valuation Advisors states that « from a

fundamental valuation standpoint, this looks like an incredibly rich – unusually

rich – valuation, even by tech company standards. It really reminds me a lot of

the late 1990s ».

This illustrates the irrationality that surrounds social media and arouse the idea

of a potential investment bubble. Some argue that bubbles cause misallocation

of resources into non-optimal uses, then having a negative impact on the

economy. Well-known bubbles include the tulip mania (recorded in 1637!) or

more recently real estate or dot com bubbles among others. Are we going

through a bubble with social media? Existing reliable and credible literature

does not provide answers to this question yet.

1.2. Return on Investment (ROI)

As said above, the traditional evaluation of the return of an investment is a ratio

that measures the correlation between the quantities invested, usually valued in

monetary terms, and the quantities gained. Applied to social media, the

10

investment is easy to measure but when it comes to measuring the return,

businesses face difficulties. The main reasons are the lack of adapted tools, but

also a poor definition of the initial objectives by the companies. One evaluates a

return by setting objectives and comparing them with the results obtained. In

the reality, when it comes to social media, most companies define global targets

such as “exposure”, reputation”, branding, “traffic” (not sales), search ranking

improvement… hardly measurable.

It is essential to measure success or failure of actions, in order to be able to

build a strategy or make some decision about future investments or simply

marketing budget allocations. Since ROI is currently difficult or impossible to

measure in social media, success or failure cannot be assessed. Moreover, “the

truth is that you cannot succeed in anything if success is never defined” (Solis,

2011).

Due to the fact that often executives are digital immigrants (Prensky, 2001), and

therefore not mature and confident as digital natives regarding digital activities,

they interpret the current situation as uncertain and demanding caution and

prudence.

Solis’s foreword to Blanchard’s “Social Media ROI” gives a good description of

the challenge: “as in anything in business, the ability to tie activity to the

business values is critical. If we are to commit time, resources, and budget to

social networks, our investments must be justified. Indeed, social media

strategies must prove long-term value and contribution to the bottom line in

order to evolve into a pillar of business success. But how do you measure

something when best practices, case studies, and answers in general are

elusive? We are struggling to prove the merit of an important ingredient in the

future success of business because precedents have yet to be written or tested”

(Blanchard, 2011).

As seen, the ROI may not be easily measurable in traditional and quantitative

terms but this difficulty is not new and it is worth exploring the possibility of

using measures in qualitative terms as it has been previously experimented in

other fields.

11

1.2.1. ROI and marketing: a clash of cultures?

Since its early days, the relationship between ROI and marketing has been

challenging. Marketing teams have “not been held accountable for showing how

marketing expenditures contribute to increasing shareholder value. As time has

gone by, this lack of accountability has undermined marketer’s credibility,

threatened the standing of the marketing function within the firm, and even

threatened marketing’s existence as a distinct capability within the firm” (Rust et

al., 2004).

While the marketing team will focus on measuring brand awareness, which is

naturally hard to quantify, the financial team will look for an accurate numerical

value to fill in a financial statement or the balance sheet. Effectiveness of

marketing activities such as PR or advertising is also delicate to measure. How

can you accurately measure the ROI of an outdoor advertising campaign?

This issue has been latent since the early days of advertising. Back in 1966,

Dean pointed out “the problem of how much to invest in advertising”, wondering

if it is “formal and objective, rather than intuitive” (Dean, 1966). The incapacity

to quantify the return of advertising triggers immediate concerns: is it an

investment or a current expense? Is it geared by objectivity or intuition?

This lack of accountability applies to digital activities and particularly social

media, as data about offsite actions is harder to capture, and must be combined

with other indicators, which are often qualitative.

1.2.2. No ROI without measure – what to measure and how?

1.2.2.1. The necessity to set a direction and objectives

“If a man does not know what port he is steering for, no

wind is favourable to him.”

Seneca, mid-first Century BC

The same applies to social media. If you invest without clear objectives, you will

never know if you have reached your destination. More specifically, Blanchard

(2011) highlights the confusion between strategy, tactics and objectives in the

12

context of social media: “trying to come up with a social media strategy to

somehow justify the need for a social media program in the first place was

pointless. But using social media as a vehicle to support existing business

objectives, now that made a lot more sense”. He concludes by stating “a social

media program, in order to deliver results, must have a purpose rooted in

business objectives” (Blanchard, 2011). On the other hand, “when we need

benchmarks to evaluate return on investment (ROI) effectiveness, marketing

professionals should first determine the duration of a campaign and what

milestones or standards they want to reach” (Sponder, 2011).

1.2.2.2. The Key Performance Indicator (KPI)

It then appears essential to align social media with SMART (Doran, 1981)

business objectives and key performance indicators (KPIs) in order to measure

ROI.

In the case of an objective of brand awareness or customer engagement, what

is exactly meant? Would there be KPIs to measure brand awareness? KPIs

would probably be complex and uncertain. Can increased product or brand

attractiveness be measured as a ROI?

KPIs are especially hard to set for qualitative objectives such as brand

awareness or reputation, which appear to be common objectives of social

media presence.

Within social media, objectives can be of different natures. Social media and

specifically social networking sites aim at connecting people. These people

usually use them for various reasons: “it can be for altruistic reasons, it can be

for brand building, it can be for ego boosting, it can simply be a creative outlet.

For businesses it can be a way of getting closer to their customers, getting

direct feedback, creating a unique brand identity, being hip and cool – or some

may do it just because CEO wants it” said Kaushik (2007). This multitude of

motives and the lack of accountability (e.g. how would you accurately measure

a company’s closeness to customers?) make ROI measurement a real

challenge. However, the necessity to measure remains.

13

1.2.3. The qualitative element: lack of accountability or simple

incompatibility?

The lack of access to quantitative data captured offsite and often the lack of

specific objectives for social media marketing among other factors, have

increased the necessity to consider qualitative data, trying to turn them into a

key measure of social media performance.

A common watchword in the business world says “if you can’t measure it, you

can’t manage it”. There are things that cannot be measured but still exist (think

about a company’s culture). This summarizes the dilemma between social

media benefits and ROI measurement. Can they both be conciliated?

Blanchard writes, “setting targets creates accountability” (Blanchard, 2011).

This is one of the major challenges of this research project.

1.3. Web Analytics

1.3.1. Definition and current context

Web analytics are the technology used to monitor online activities (including

social media), capture the coveted data, and make the analysis possible.

Chaffey (2008) defines web analytics as the “techniques used to assess and

improve the contribution of e-marketing to a business including reviewing traffic

volume, referrals, clickstreams, online reach data, customer satisfaction

surveys, leads and sales”.

The use of web analytics is currently democratizing and only used by a minority

of organizations, often at a low maturity level. This may partly explain the 84%

of companies not measuring ROI of social media (Mzinga and Babson, 2009).

However, as outlined by Sponder (2011), “the average business user, agency

owner, or stakeholder lacked (and still lacks) the understanding, patience, or

sophistication to utilize these listening platforms effectively. As a result, they

hire analysts to access the platforms for them and to provide reporting data and

dashboards. Dashboards are reports that give users a high-level snapshot of

how their campaigns and business initiatives are performing online”.

Then, it seems that although web analytics reports may be easy to share, they

are not clearly understandable by those who do not have the specific skills and

market knowledge, and thus do not give the same level of insight to everyone.

Social media has moved web analytics another steps forward, introducing a

14

new level of analysis, a range of new actionable metrics and challenges. An

important one relies in the merger of web and social media analytics data.

Marketwire Sysomos, a social media listening platform has created a program

called Sysomos Audience, attempting to merge Web and social media data to

produce superior ROI metrics. « Audience fills the significant gap between Web

analytics services, which provide information about what is happening on your

Web site, and social media monitoring services that track who is talking about

your company or brand» (Sysomos, 2011).

As Benson, a social media expert, outlines, “Web Analytics provide traditional

attributes of page views, unique site visitors, etc. This alone isn’t adequate

anymore because customers are talking to each other (and much of it isn’t on

your site)” (Fischer, 2009).

This suggests that “returns from social media investments will not always be

measured in dollars, but also in customer behaviours (consumer investments)

tied to particular social media applications”. (MIT, 2010)

Yet, this qualitative element is theoretically not an acceptable ROI metric for the

financial department. “Without the ability to accurately measure social media,

however, it’s hard to see how ROI calculations can be done well” (Sponder,

2011).

1.3.2. Aligning offline and online activities

Thus, to measure the social media ROI, other sources of data must be used. It

can include traditional ROI techniques such as marketing research. However, in

order to be effective, a social media strategy must be integrated within a digital

strategy, in line with the offline strategy. Successful integration of the various

channels can lead to cross-channel synergies. How can those synergies be

measured? To which channel should the added value be attributed? When

there is synergy between different tools, how would the ROI be measured?

Would the tools be grouped or would the measure be individualized?

In order to integrate both offline and online aspects, the REAN framework

(Blanc, 2006, Jackson, 2009), standing for Reach, Engage, Activate and

Nurture, seems to be the most appropriate tool as it addresses cross-channel

15

strategies. In the context of this project, it will enable the analysis of the

synergies between social media and other online activities, and offline or other

‘traditional’ marketing actions.

1.3.3. Social media ROI, a new framework?

A first review of the literature confirms that Social media ROI cannot be

measured like traditional marketing ROI. As outlined by MIT research

“marketers are struggling with social media measurement partly because the

frameworks are still largely driven by reach and frequency and are ill-suited to

the interactive media environment” (MIT, 2010).

1.3.3.1. Owyang’s framework

In an attempt to address the issues around ROI of social media, Owyang (2010)

has developed the social media ROI pyramid, a framework serving as a guide

to measure social media performance. Owyang provides a list of diverse

attributes to be measured, including activity, tone, velocity, attention,

participation and other qualitative items.

However, this framework does not solve all the issues associated to

measurement, as it is only qualitative and can tend to verse into subjectivity. It

poses the problem of credibility of measurement examined in this project.

1.3.3.2. The IAB framework

The Social Media Council of the above mentioned IAB has published “a new

framework for measuring social media activity” (Pentin, 2011) addressing these

issues.

Pentin (2011) identifies six main reasons why a framework is required:

accountability, efficacy, interpretation, optimisation, benchmarking, and

standardisation.

The framework consists of three fields:

• I: Intent

• A: Awareness, appreciation, action, advocacy

• B: Benchmark

This framework integrates most of the common issues of social media

performance measurement, providing KPIs sets and other good practice rules.

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In order to assess its usability for this research, its pertinence and adaptability

will be analysed further.

In summary, these frameworks are first step towards optimization of social

media performance measurement, but present some limitations that will be

addressed in primary research.

How to measure social media ROI in a way that would make it accountable?

Which metrics must be used as a pattern for social media measurement, and

which ones must be set case by case?

Considering the lack of information and access to data regarding Social media

ROI, why are the allocated budgets increasing? Would this be a form of trust, or

on the contrary, fear for exclusion?

1.4. Research gap

Extensive research has already been conducted on the issues surrounding

social media, including influence on branding or sales contribution among

others. In parallel, research around ROI of social media has also been

conducted, but remains incomplete. The research findings mostly converge

towards the combination of both qualitative and quantitative metrics.

A few points, such as the broad implementation and integration of a digital-

specific strategy (including web analytics) within the company are usually

considered as secondary, and not as primary influencing factors.

The discrepancy between ROI and social media, one being quantitative and the

other devoted not to be quantified, is usually not central in the existing literature.

The immaturity of the sector appears to be a key element that could partially

explain the issues faced by businesses when it comes to measuring digital and

more specifically social media performance. This point will be further discussed

with the respondents.

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2. Methodology

This section intends to demonstrate how knowledge will be developed and data

produced, in line with the research questions and objectives. For this purpose,

four fields will be covered: research philosophy, approach, strategy and choice.

2.1. Research philosophy

The research philosophy relates to the development of knowledge and the

nature of that knowledge in relation to research (Saunders, 2009). Even if the

importance of the philosophy is less prominent in business-related projects than

in social science or scientific projects, its importance and overall influence on

the whole project remains high. The philosophy helps to refine the research

methods to be used, to identify the evidence gathered, to interpret and hence to

contribute to the research questions. It also helps to recognize the limitations of

each approach.

The most common research philosophies in business projects are positivism,

realism, interpretivism and pragmatism (Saunders, 2009). Among these

research philosophies available, positivism and post positivism will be explored

in relation to research methodology.

2.1.1. The positivist approach: suitability and limitations.

“Positivism asserts that the real knowledge is based

on experience and positive verification”

Auguste Comte, 1865

Saunders defines positivism as “the epistemological position that advocates

working with an observable social reality. The emphasis is on highly structured

methodology to favour replication, and the end product can be law-like

generalisations, similar to those produced by the physical and natural scientists”

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(Saunders, 2009). Smith (1998) also proposes a useful descriptive analysis:

“Positivist approaches to the social science assume things can be studied as

hard facts and the relationship between these facts can be established as

scientific laws. For positivists, such laws have the status of truth”.

This stresses the importance given to induction and verification and the

establishment of laws, which, in principle, aims to avoid subjective viewpoints.

The inductive approach has direct implications for a research based on it.

At first, the idea of law-like principles behind positivism seems to suit this

project, as research objectives include the development of good practice rules

and a set of metrics to be used as a “template” for performance measurement.

However, considering the specificities of the topic (such as the fast pace and

evolution of the sector, importance of the qualitative aspect…), positivism

presents a number of limitations.

Research needs to be quantitative to become the basis of fundamental laws.

This approach is not adapted to provide an in-depth understanding human

beings and their behaviours.

Moreover, when it comes to measuring performance, numerous factors

intervene, which contribute to make each case specific and unique. Law-like

generalisations may thus not prove relevant to this subject, because of their

inflexibility and potential lack of adaptability among others.

Then, the structured approach of positivist reasoning may prove to be too rigid

for such an empirical project, in which new thinking fields and research areas

need be constantly considered in order to extend the reflection perimeter.

The variety of behaviours, perceptions, feelings and attitudes towards social

media are beyond the scope of positivism.

In order to address these limitations to suit this research project, further

research has been conducted, leading to post-positivism.

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2.1.2. From positivism to post-positivism

“Post-positivism provides an alternative to the

traditions and foundations of positivism for conducting

disciplined inquiry. For the post-positivist researcher

reality is not a rigid thing, instead it is a creation of

those individuals involved in the research.”

Hughes, 1994

The recognition of the limitations of positivism gave way to the emergence of a

new philosophy, post-positivism, which claims that “reality does not exist within

a vacuum, its composition is influenced by its context, and many constructions

of reality are therefore possible” (Hughes 1994). Proctor (1998) suggests that

“among the various factors that influence reality construction, culture, gender,

and cultural beliefs are the most significant”. Post positivism enables recognition

of the elaborate relationship between individual behaviour, attitudes, socio-

cultural context and external environment.

Post-positivism can be used to analyse the various factors that influence reality

and “the intricate relationship between individual behaviour, attitudes, external

structures, and socio-cultural issues such as cultural beliefs” (Proctor, 1998).

This applies to the analysis of social media use and related behaviours.

In conclusion, “positivism adopts a clear quantitative approach to investigating

phenomena as opposed to post-positivist approaches, which aim to describe

and explore in depth phenomena from a qualitative perspective” (Crossan,

2003).

The qualitative approach, as opposed to the quantitative approach of positivism,

is quite adapted to explore and understand the wide variety of behaviours and

attitudes around social media.

The limitations of post-positivism generally relate to qualitative methods, which

imply active participation of investigators, interactivity and proximity of the

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researcher to the investigation. This human intimacy can introduce a certain

level of bias.

Considering those definitions and critics, post-positivism has been chosen to

develop this research project. This decision also involves important implications,

in the research approach, strategy and choice.

2.2. Research approach

Two main research approaches are available. One, the deductive approach,

starts from the need to test theory with observation and the other one, the

inductive approach, starts from the need to develop theory from observation.

As further explained by Saunders (2009), the deductive approach involves “the

testing of a theoretical proposition by the employment of a research strategy

specifically designed for the purpose of its testing” (Saunders, 2009).

This approach is informally called the “top down” approach. It is concerned with

testing and confirming hypotheses.

This research being concerned with an initial question, or initial hypothesis, the

deductive reasoning has been considered as the most appropriate.

However, the conditions for sound deductive reasoning are the necessary

quality of the initial assumption and the empirical evidence to confirm it. As

Hume argues, “our everyday functioning depends on drawing uncertain

conclusions from our relatively limited experiences rather than on deductively

valid arguments”. The observations need to be unbiased and representative,

otherwise the whole process will be deteriorated.

For these reasons, triangulation with some inductive reasoning has been used

to maximize data reliability. “Triangulation is a powerful technique that facilitates

validation of data through cross verification from more than two sources. In

particular, it refers to the application and combination of several research

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methodologies in the study of the same phenomenon” (Bogdan and Biklen,

2006).

2.3. Research strategy

The research strategy determines the overall plan of how the researcher will go

about answering the research questions.

In business research projects, several strategies are available (e.g. experiment,

survey, case study, action research, grounded theory or archival research

among others).

On the one hand, Yin (2003) states that “each strategy can be used for

exploratory, descriptive and explanatory research. On the other hand, Saunders

(2009) argues that “some of these clearly belong to the deductive approach,

others to the inductive approach”.

Given the form of the research question and the focus on a contemporary event

(vs historical), the case study has clearly appeared to be the most appropriate.

The case study is “a strategy for doing research which involves an empirical

investigation of a particular contemporary phenomenon within its real-life

context using multiple sources of evidence” (Robson, 2002). Yin et al. (1984)

add that it is “an empirical inquiry which investigates a contemporary

phenomenon within its real-life context when the boundaries between

phenomenon and context are not clearly evident and in which multiple sources

of evidence are used.

Flexibility allowed by the case study is essential. As highlighted by Schell

(1992), “the case study is the most flexible of all research designs, allowing the

researcher to retain the holistic characteristics of real-life events while

investigating empirical events”. It enables the integration of reality, flexibility, a

holistic approach and therefore to understand this rather complex phenomenon.

Schell (1992) adds that “as a form of research, the case study is unparalleled

for its ability to consider a single or complex research question within an

environment rich with contextual variables”.

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The case study strategy has efficiently contributed to resolve both the research

questions and objectives of this project. It has lead to a combination of

exploratory and explanatory studies, as its main goal aimed at providing a deep

understanding of the context, enabling to develop theory about the relationship

between the different variables involved.

Observation, experimentation, interviews and literature have been used to

support this project.

2.4. Research choice: data collection and analysis

Although quantitative data has been essential to assess the relevance of this

project, this research project is a multi-method qualitative study, as the nature of

the topic is somewhat diverse, and biases need to be moderated. This choice

has been made to maximize reliability and generalization. The essence of this

project requires a combination of methods to describe and analyse the

complexity of the phenomenon studied.

2.4.1. Multiple-methods

Multiple-methods consist in blending qualitative and quantitative research as

well as designing, collecting and analysing data from different methodologies.

The social media and the business world are a multi-faceted and multi-layered

reality. The use of a single method would only partially reveal this reality. Hence

the need to combine methods and provide better opportunities to answer the

research questions, and allow a better evaluation of the extent to which the

research findings can be trusted and inferences made from them, as suggested

by Tashakkori and Teddlie (2003). The investment in social media appeals to

both quantitative and qualitative motives.

Qualitative data, defined as “non-numerical data or data that have not been

quantified” (Saunders, 2009) appeared to be more relevant to this project than

quantitative data. First it better suits the chosen research philosophy. Then, it

enables a tailored data collection and a deeper understanding of the context

and the various variables involved. The use of the above-mentioned

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triangulation technique has facilitated the validation of these data, combining

both secondary and primary sources.

2.4.2. Secondary data

Secondary data has strongly supported the development of this research, and

has contributed to the assessment of its relevance.

Academic sources

Academic literature on ROI is numerous and diverse, and the challenge

consists in identifying the most relevant pieces of work available. A critical

review of each selected source of information has been necessary due to the

fast development of social media and pace of technology changes.

Technological changes, shifts and upcoming issues may not be considered in

some articles written even a few months ago.

Reliability issues

Due to the nature of social media, belonging to the digital world and the fast

evolution of the sector, most of the information has been found online or in

public literature. Many of these sources are less reliable and credible than

academic journals or industry reports: this has justified further caution in their

use within this project.

Questionnaire design

To design the semi structured questionnaire used to interview opinion leaders

or prominent experts of social media, all the information gathered from

secondary sources (mentioned above) has been used. It has also involved a

critical literature review, involving further personal thinking and analysis on the

existing literature and schools of thought.

2.4.3. Secondary data contribution to qualitative primary data gathering

Secondary data has been used as a basis to design of the questionnaire used

to guide the semi-structured interviews, and minimize the gathering of already

existing information.

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The critical literature review, involving further personal thinking and analysis on

the existing literature and schools of thought, has supported the development of

the questionnaires, in order to optimize both the creation of a real contribution

to the topic, and the gathering of “tailor-made” data, directly related to the

specific research topic.

2.4.4. Semi-structured interviews

In order to investigate the current practices of an evolving phenomenon, it was

necessary to hear the opinion of the leaders of the sector who are decisive

actors and direct contributors to the shaping of the sector’s future. Despite their

numerous interviews in many publications in the world, a direct interview

appeared to be essential to cover the exact scope of the research and hopefully

gather new or different thoughts and opinions.

Process

A list of potential respondent profiles had been developed. This target included

various stakeholders of the sector, which would enable to reassemble ideas

from different point of views. The variety of selected profiles was a prerequisite,

in order to ensure that relative global objectivity through a balance of different

and sometimes contrasted opinions and findings.

A semi-structured interview guide has been designed and tested ensuring that

the questions asked would lead to the aspects needed to contribute to this

project. The tests have emphasized the necessity to broaden the questions to

make sure that each interview would stimulate and give space to all the

important thoughts and opinions had to share.

As each respondent has a specific role in the social media sector, the guide

seemed to be too broad and not-specific enough to clearly bring results and

contribute to the project.

The final questionnaire has been sent and answered via email, and then

completed by either a phone call or a meeting when physically possible.

Addressing interviews issues and limitations

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As a qualitative approach, the selection of respondents has been established to

ensure that all the different opinions about the research would emerge. Hence

the variety of backgrounds, positions, philosophies and points of view.

Respondents are not representative of the sector and needed not to be. As one

of the objectives of this research is to propose new practice rules and a set of

metrics to optimise social media performance measurement, they have proved

to be the appropriate people to bring new insights and ideas, set standards and

put this topic into perspective, through a more holistic approach.

The selection of the respondents could bear some bias as:

• They are the most savvy and knowledgeable in this topic, therefore more

than any other stakeholder.

• They have all been involved and established in the field for a long time,

and therefore demonstrate high maturity levels and approaches which

may not be representative of the other stakeholders.

• The respondents are not the people who have directly invested in social

media, such as companies who spend drastically.

In order to limit the bias, which could arise from the proximity of the interviewer

with the investigation, all questionnaire answers have been received by mail.

Only after this phase was there a contact (oral or direct) with the respondent

used as an opportunity to explain or complement the written answers.

Overall suitability of this method

The initial doubt on the feasibility to obtain a direct contact with key experts and

leaders has been quickly dispelled. Most of the top experts and leaders related

to this research subject have been keen to participate. As an indicative

example, Jim Sterne, author of “Social Media Metrics: How to measure and

optimize your marketing investment” and founder of the world-respected Web

Analytics Association, accepted to both respond to my questions via email and

discuss them further over the phone. This high quality of contributors has highly

contributed to the purpose of this project, understand a growing and somewhat

unexplained phenomenon.

This qualitative approach to research through interviews has proved relevant

and efficient, bringing new ideas and areas of thinking and leading to findings.

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Statistics have been used to identify or confirm trends, complement some

qualitative findings and validate some generalizations.

However, it needs to be said that the fast pace of the evolution of the sector

creates the necessity to monitor the trends and possibly amend some aspects

of the generalizations made.

The questions discussed during the semi-structured interviews have been

included in the appendix one.

3. Findings/results

Below are the main findings of the research.

3.1. Measuring social media performance

3.1.1. Discriminating key terms

There are different opinions regarding social media performance measurement.

In their publications, professionals such as Jim Sterne and Olivier Blanchard

assert that it is possible to measure social media performance. However, as

mentioned by Jim Sterne, “it’s never black or white”.

Andrew Walker, consultant and Web analytics and Digital marketing professor,

outlines that “it is zeros and ones, data is recorded automatically: something

can be measured”. However, along with Sterne who warns that “it’s never black

or white”, he points out the immaturity of this very new sector, requiring new

approaches and therefore bringing new issues. Marshall Sponder, web

analytics specialist and author of “Social Media Analytics”, joins Walker on the

fact that something can be measured. However, when it comes to the possibility

of measuring social media performance, he says:

“I have taken both points of view at different times -

constantly mentioning the lack of standards and

accountability while also stating we could measure

anything, to our heart's content, if we really wanted

to.”

Sponder, 2011

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Regarding the lack of standards affecting accountability, Walker adds that

“standards will start to emerge, just as the Web Analytics Association (WAA) did

for basic web analytics”.

Jim Sterne, founder and president of the WAA, in his first interview for this

research, questions the terms used:

“Accuracy, accountability and usefulness are all very different. I cannot

accurately measure the population of a country but I can be accountable for the

usefulness of the census. We have to admit that there is no true accuracy in

measuring anything - even your height. You are taller in the morning than at

night and your height is affected by temperature and humidity. Not to mention

how you stand and whether you washed your hair that day.” Jim Sterne, Author

of Social Media Metrics (September 1st, 2011).

Sterne suggests to take accuracy “off the table”, and emphasizes the

importance of measuring social interactions consistently, in order to “be able to

compare and contrast and come up with an analysis that is useful to the

business”.

A review of current measurement practices may prove to be necessary.

Following this idea of discriminating the basics, Walker insists on the

importance of differentiating data, information and knowledge.

3.1.2. Managing data

As far as data is concerned, Walker suggests that “many problems will be

resolved as data will become increasingly accurate”. To illustrate this point, he

uses an example of the traditional web, where companies have captured large

amounts of accurate data about their visitors. This has led their online

advertising to be micro-targeted. “The level of targeting is becoming

increasingly accurate, none of this would have never happened in the nineties.

Data standards have never been better, and industry specific standards are

currently being developed”.

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Regarding accountability, although financial people are mainly dealing with

numbers and quantitative indicators, some companies start to quantify

qualitative data.

Walker mentions the emergence intellectual capital (IC) accounts, aiming at

producing some sort of P&L of knowledge in the organisations.

“The term intellectual capital conventionally refers to the difference in value

between tangible assets (physical and financial) and market value” (Magrassi,

2002).

More specifically, a new component has emerged in this IC account: relational

capital.

“It consists of more identifiable items such as trademarks, licences, franchises,

but also the less definable, such as customer interactions and relationships”

(Skyrme, 1998).

Walker expects social media to start appearing on this IC report soon.

3.2. Social media and ROI

3.2.1. The nature of the relationship

How to relate social media and ROI that are, as we have seen, inherently

different?

Sponder insists on the fact that “Social Media ROI has to be defined in-terms of

social media, not brick and mortar stores, as it has been. Much of social media

doesn't not fit into (yet) a financial transaction and any value you put on a

"friend" or "fan" is subjective, and probably biased to some outcome.”

Walker indicates a “non existing relationship between social media and ROI”,

insisting on the fact that “we are not there, the relationship is not established

yet. It is currently impossible to prove ROI.” As pointed out by Sponder, Walker

asks: “Pepsi has 22 million fans on Facebook, what is their value?”.

To support the idea of a “non-relationship” Walker also uses Skype’s example.

Microsoft bought Skype for $8.5 billion, valuing the company almost 10 times its

$ 860 million turnover in 2010.

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Walker gives a final conclusion by saying that “ROI and social media are not

currently connected”.

These points confirm the main challenges about measuring the efficiency of

social media investment.

3.2.2. The main issues

3.2.2.1. Redefinition of values

Sponder introduces the idea that “the real issue […] is not ROI, but the lack of

any currency value around social media, itself. People keep wanting to treat it

as if it has financial value (which it does) but you can't trade your friends in for

dinner (or can you - I suppose with Influence, you could - but it's not something

you can bank on). So at the end of the day, we need to call ROI something else

for Social Media, or just redefine it so that it makes sense with Social Media, as

it doesn't, now.”

Sponder then warns:

“The main challenges are [that] we have no way to universally agree on the

value of friends, fans, followers, likes, votes, and so on, at any point in time.

Also, we don't have the right formulas to calculate the values, they need to be

re-written for social media - perhaps not all formulas can be universally applied

everywhere.”

This difficulty to find common evaluation and measure is exacerbated by “Fear”

and its consequences, a concept brought up by David Meerman-Scott,

marketing strategist and author of “The New Rules of Marketing and PR”.

He argues that « the insistence on old school ROI measures is fear based» and

says:

« Every day, I run across FEAR of marketing on the Web.

- Fear comes from bosses who insist on calculating the ROI of the new

rules of marketing & PR based on sales leads and press clippings.

- Fear comes from offline advertising and PR practitioners cautiously

making the transition to Web platforms to generate attention.

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- Fear comes from those who insist on copying the competition. »

Sterne also joins Meerman-Scott’s point of view, outlining that “people consider

the Internet in terms of TV and eyeballs”.

3.2.2.2. Consistency

Consistency appears to be a crucial element, and Sterne seems to be the first

advocate when he says:

“The main challenge to measuring the ROI of a social media campaign is to

make it consistent with other campaigns you are measuring and consistent over

time”. He points out the necessity for companies to standardize internally and

not change tools in order to remain consistent. He also recommends the use of

tools such as Hitwise and Comscore as generic measurements or external

benchmarks.

3.2.2.3. Defining standards

Most agree that the definition of both internal and industry standards appears to

be one of the main challenges for successful social media performance

measurement. Walker says that “defining standards will prove very hard”, using

the “bounce rate” example: several definitions are available for this single

simple metric.

3.2.2.5. Resources

Another issue is the recruitment of the right people in the role: it can currently

prove hard to find people who understand the different tools available and are

able to analyze, and it is especially harder for social media.

Human resources do not have any job spec yet to recruit the right people, and

usually do not have the skills to identify the challenges related to the position.

In terms of skills, Walker asks: “how many people could realistically consider

the range of tools and decide which one is the best?”

Walker also quotes Kaushik, using the Pareto principle, stating that 20% of the

budget should be allocated to the software, and 80% to the people. Finding the

right people then appears to be a major challenge.

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3.3. Level of irrationality

Is there a social media bubble that would evidence a certain level of

irrationality?

Respondents’ opinions diverged. While Sterne affirms that “we are definitely in

a social media investment bubble”, David Meerman-Scott is more radical,

stating that “the idea of ‘bubble’ is ridiculous”.

Sterne argues that “investors are still enthralled by the large numbers of users

[that] any given social platform claims, without enough consideration of the

business model”. About the bubble effect on social media performance

measurement, he mentions that it is “a very different subject from a company's

ability to measure the marketing and customer service value of using social

media. If the bubble bursts, will it impact the credibility of social media as a

business tool? Sure... but only for a while. Then, those who are doing it well will

continue to prove its value. Just as online advertising did after the Dot Bomb in

March, 2000.”

Sponder also thinks “there is a "bubble" around Social Media. But [...]

as business starts incorporating social media measurement into business

practice, [he feels that] the bubble will eventually dissolve - but [he doesn't] think

it will !pop"! “.

In the same way, Sterne suggests that the social media investment is based on

uncertain figures which makes it hazardous for businesses.

On the other hand, David Meerman-Scott has a quite opposite opinion when he

states that “the idea of a ‘bubble’ is ridiculous. We are in the middle of a

communications revolution. Was there a telephone bubble? A radio bubble? A

television bubble? No.”

Walker is more moderate regarding the idea of a bubble. “Social media is too

small to be a bubble: it is not a bubble from an economic perspective”.

In the 1990s, e-commerce was a bubble, there has been massive investment

and then it crashed. Unlike this Dot Com bubble, Walker says in his own words,

“social media is easy to screw up and cheap! How can you loose money in

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something that doesn’t cost anything?” He finally summarizes mentioning “a lot

of hype and nonsense talk around social media”.

Instead of a bubble burst, Walker forecasts that “there will definitely be a

backlash, but it might help to measure ROI, as it may force people to find

metrics to measure performance and justify the investment”.

Sponder moderates and reconciles the diversity of opinions when he says that

“as platforms mature and evolve and as people become more articulate about

what they want out of Social Media, the practice of it will become more precise

and standards will evolve (in the next 5 years or so).”

Would this bubble have a direct impact on measurement?

Sponder also thinks that the bubble affects measurement, but “again, as

people/users of this information and the platforms, become more sophisticated,

they will demand more and more accuracy from the SMM platforms and this will

lead to more creditable Social Media Analytics and measurement.”

Good practice rules and metrics have also been discussed with the

respondents, but will be contextualised and put into perspective in the next

section, in order to increase their relevance in the context of this research.

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4. Analysis and discussion of findings

Secondary research and primary research as well, have shown a widely shared

conviction that it is a necessity to measure social media performance and ROI.

Unanimity can also be observed on the current difficulty of achieving the

measurement and the fact that no realistic and exploitable solution is available

for the corporate world yet. As previously pointed out, without the ability to

measure ROI, success or failure cannot be assessed, strategic planning

becomes hazardous and the budget allocation to the most efficient and

profitable actions is not possible.

4.1. Context of social media and ROI

In order to analyse the relationship between social media and ROI, it is

necessary to explore the characteristics of each universe and their exigencies.

4.1.1. Cultural aspect

4.1.1.1. Clash of cultures

Within the organisations, two cultures coexist. One, led by finance people, relies

on figures and monetary evaluations or indicators. The other one, led by

marketing is concerned with human behaviour, reputation or awareness, which

figures cannot satisfactorily measure. As brought up in the literature review:

how would you measure the performance of a PR event?

This incapacity to quantify return in absolute numbers has turned the marketing

budget into an expense rather than an investment.

This uncovers the issue of quantification of intangible and qualitative data that

has been addressed in both the secondary and primary research.

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More specifically, intellectual capital, which has come out from primary

research, is a tentative to measure knowledge within organisations. The

concept was refined by the introduction of the concept of “relational capital”,

which values items such as customer interactions and relationships.

According to the findings of this project, we can expect social media activity to

become part of the intellectual capital, and soon be integrated in corporate

reports.

In order for this to happen soon, some issues described below will need to be

overcome.

4.1.1.2. Organizational culture

The organizational culture is an important barrier to social media measurement.

First, it is still difficult and slow to introduce a digital (and even social) culture

within companies, because of the current traditional managerial behaviours and

visions. This may change owing to the increasing number of digital natives, who

will integrate it more easily than digital immigrants (Prensky, 2001). As an

example, the companies (especially HR) may face difficulties while recruiting

web analyst profiles, because of the technical specificities of the position and

the risk of not being able to optimize their competencies and keep them

motivated.

Language, as discussed during primary research is another barrier to social

media analytics acceptance, as the jargon related to the field may not be

understood by the different employees.

In conclusion, most companies have a low maturity in terms of traditional web

analytics, which can obstruct social media performance measurement.

4.1.2. Data

4.1.2.1. Lack of uniformity

All data gathered on social media platforms is quite heterogeneous. The

source, the data collection method and the diversity of tools used give a

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disparate and incomplete description. This affects the capability to identify

consumer behaviour towards brands or products, adding to the complexity of

social media performance measurement.

As said before, some analysts like Walker point out the necessity to differentiate

data, information and knowledge.

4.1.2.2. Lack of accuracy

Visitors’ data captured on social media platforms is neither comprehensive nor

accurate.

As evidenced in the findings, traditional web has reached an acceptable level of

accuracy, which now enables to achieve personalised micro targeting (e.g.

display campaigns) and measure both its pertinence and efficiency. Social

media needs to follow the same path by gradually increasing both accuracy and

pertinence of the data that needs to be analysed.

The respondents interviewed show a real concern about accuracy, which does

not appear in the literature review, which rather undermines it.

4.1.2.3. Lack of consistency

As discussed, the attempts to measure social media performance to date are

not satisfactory, and this incapacity pushes the business executives to

permanently try new alternatives. The profusion of technological offers and

platforms adds to this phenomenon, creating inconsistency.

Aggregation of data can also sit under consistency. In order to be consistent

and meaningful, the disparate tools and data collection methods require

aggregation.

This inconsistency is exacerbated by the lack of widely accepted standards in

the industry.

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4.1.3. Tools and technology

Technology and marketing are becoming inseparable and the prediction of

Marshall McLuhan “the medium is the message” (McLuhan, 1964) has become

a reality: the medium has a determining effect on our perception of the

message.

Technology evolves at a fast pace, especially in social media. The main issue in

this field is that the coveted data cannot be captured yet.

First, because the complexity and subtleties of social interactions need to be

interpreted.

Then, because the actual impact on sales or other traditional KPIs cannot be

proven yet.

The existing monitoring tools demonstrate limitations, as qualitative content still

requires human analysis and appreciation in order to be useable. This

increases the importance of the role of the analyst, which as said before, is one

of the biggest challenges for successful social media performance

measurement. This also prevents generalization of both reporting and

monitoring.

Last but not least, the disparity of the tools usually makes aggregation more

difficult. The data gathered cannot be used and transferred from one tool to

another, and thus does not interoperate on different platforms or allow any

synthesis. The emergence of industry standards would help to solve this issue.

4.1.4. Motives and objectives

As discussed in the literature review and findings, corporate executives invest in

social media for the following reasons: increase brand value, increase sales,

increase customer satisfaction or reduce marketing expenses.

The existing literature insists on the fact that this incapacity prevents from any

ROI calculation.

37

Motives then result relatively clear in the executives’ mind. However, when it

comes to setting specific objectives, the impossibility to measure affect their

capability to set objectives following the SMART.

4.2. The issue of irrationality

4.2.1. Lack of accountability

Data accountability is another major issue. What do figures mean? Are they the

only way to express reliability? “Accounting is not a business analysis tool” says

Sterne, “and all figures contain a certain degree of assumption”. This is even

truer when it comes to social media, where figures are either absent or apply to

marginal features.

As a consequence, there is a risk: this lack of accountability can be a source of

interpretation, or irrational behaviours.

4.2.2. Craze

In spite of this lack of rationality, companies significantly increase their presence

and investment in social media, and this phenomenon keeps on growing. As

discussed in the literature review, most executives (84%) cannot justify this

investment by measuring return and seem to be more motivated by responding

to competitor’s presence. This lack of objective reasons confirms the high

degree of irrationality in social media.

4.2.3. Bubble

This trend for irrationality has struck the evaluation of the businesses involved

in social media.

The current overstated evaluations have brought the idea of a potential social

media bubble. As the existing literature remains rather silent on this idea, the

subject has been raised with the respondents during the primary research

interviews. They have expressed contrasted opinions about the idea of a

bubble. In summary, there are two main schools of thought.

38

One, led by David Meerman-Scott categorically denies the existence of a

bubble. He prefers to rationalize, and identifies fear as the main cause of

irrationality.

Arguments refer to the small size of social media, the investments are not as

massive as they were in the e-commerce in the nineties. While recognizing

certain irrationality around social media, Walker asks “how can you loose

money in something that doesn’t cost anything?”

Those who advocate the existence of a bubble explain it by a fascination

towards the phenomenon and the large number of users, which leads to a

biased correlation between the real economics of the company and the

investment.

A more moderated approach brings up the idea that the bubble will not pop, as

the introduction of measurements will eventually dissolve it.

Both opinions ultimately converge in saying that whether it bursts or not, metrics

will be found to measure and justify the investment.

However, an irrational phenomenon is occurring: it is currently impossible to

justify recent sales prices of Skype or LinkedIn, which are valued ten times their

annual turnover. From these two examples, Walker sees a confirmation that

ROI and social media are not currently connected.

Isn’t this disjunction between ROI and value an evidence of the existence a

bubble?

4.3. Metrics and measurement

4.3.1. Use of metrics

Social media metrics are currently facing the same challenges as web analytics

in their early days.

Actionable metrics have emerged with web analytics, enabling to measure

online activity and analyse behaviour on websites. Although most companies

39

still remain relatively immature in the field, the acceptance of their importance is

increasing, and they start to be integrated in performance measurement.

As an example, these metrics have become essential to assess the

effectiveness and ROI of any digital campaign, and are now reckoned as a very

useful tool.

However, in social media, the metrics are currently not performing or acceptable

for most businesses. The nature of social media is not compatible and does not

comply with existing data capture techniques and processes, and “values”

constituting the actionable metrics are not clearly defined and standardized yet.

The current technological and monitoring tools do not provide complete analysis

and reports because the qualitative aspect of social interactions that cannot be

addressed by algorithms.

In order to get round those difficulties, ROI metrics need to be redefined for

social media. As brought up during primary research, “the real issue is the lack

of any currency value around social media. ROI needs to be called something

else for social media” suggests Sponder.

4.3.2. Lack of standards

As a consequence of the impossibility to measure social media performance,

the industry has not been able to establish widely accepted standards that

could become the norm yet. The same applies at the company level: internally.

This lack of industry wide standards directly impacts on performance

measurement.

In the technological field, platforms are quite diverse but no one is universally

appreciated and accepted.

Due to this absence of shared standards, external benchmarking is not feasible

since a company cannot compare its social media performance against its

competitors

As an example, how would you define the value of a fan? As suggested in

primary research, the value of a fan is currently not determined, and cannot be

universally fixed, as it depends on objectives of the company’s presence on

social media.

40

Internal standards definition would enable companies to remain consistent, and

thus optimize social media performance measurement over time as well as from

one campaign to another.

Last but not least, metrics cannot be determined in isolation from specific

objectives: businesses first have to define what they want to achieve in order to

decide on what they should be measuring.

4.3.3. Frameworks

In order to structure and optimize ROI calculation, frameworks have been

designed. For social media, as shown in the literature review, focus has been

placed on two major frameworks: Owyang’s and Pentin’s.

One of the purposes of this project is to propose a framework that addresses

the current challenges of the industry and responds to the limitations of existing

frameworks.

Owyang’s framework appears to be theoretically advanced and very sound. It

provides good practice rules and qualitative. However, in the context of this

project, it shows a lack of hard financials, which affects its relevance. Moreover,

it may be quite complex and technical for most companies who have a low

maturity.

The IAB framework also presented in the literature review provides good

practice rules and successfully combines hard financials with soft metrics,

making it appropriate to most companies.

It also adapts the metrics to each social media platform. However, it does not

solve the issue of consistency of data from one platform to another, and thus

prevents integration of results.

These limitations of existing frameworks justify the development of a new

framework that integrates the findings of this research. Recommendations will

be presented in the next chapter.

41

4.3.4. Best practices

Walker and Sponder expect the field to mature, leading to further interest for

traditional web analytics. Sponder outlines that further accountability and

acceptance is “only a matter of time - hopefully, sooner than later. Standards

would help (…) I would say, as we nail down social media Analytics, best

practices and eventually, standards, will come into being - with them, will come

accountability and the ability to meaningfully compare platforms and

perhaps, inter-operate with the data. Today, that's not yet the case.”

On the other hand, Sterne argues that the qualitative element can be

quantitatively assessed. “Qualitative metrics can be quantitatively used to make

business decisions about anything. In government, it's called Polling. In

customer service, it's Customer Satisfaction. In social media, it's a combination

of attention, engagement and polarity.” However, as developed above, these

qualitative measures will still be controversial in terms of pure accountability,

and should be balanced with hard financials as well as perspective on existing

practices (e.g. accounting): “there is no such thing as hard numbers (…) It’s

never black or white” (Sterne, 2011)

I believe that the existing lack of accountability will remain for a few more years,

but in the end, data capture will enable the collection of the necessary data,

standards will emerge, organizational culture will partially shift to digital and

social media metrics will be better accepted, despite their qualitative aspect.

The qualitative data will be further quantified, and better accepted, it is only a

matter of time. (Sponder, 2011)

I believe the emergence of standards will optimize consistency, and

comparability, which are key elements of successful performance

measurement.

42

Recommendations and conclusion During this project, it has been highlighted that the current tools available

cannot overcome all the obstacles to social media performance measurement

and ROI calculation. Until consistent and universally accepted standards

emerge and adequate technological solutions are designed, the best immediate

orientation is probably to optimize the use of available tools and practices.

This process involves the selection of the most efficient and appropriate ones,

in order to ensure that their mutual role will enable consistency and that their

association will create synergies.

This process could start by adapting the organizational culture towards further

digital maturity, reviewing the current processes, improving the quality and

reliability of the data captured, selecting the adequate the metrics and the best

technological tool available to each structure.

Ten good practice rules have been derived from the combination of secondary

and primary research findings. These good practice rules would enable

optimization of measurement, providing a solid structure that ensures

consistency and increases data and analysis reliability.

From all the above good practices to be used by the companies are

recommended below:

1. Invest in social media competence in marketing and analysis

Competence and expertise should represent 80% of the social media

investment versus 20% for the software.

2. Define specific goals for Social Media

Develop SMART goals specific for social media activities and actions.

3. Define accurate and consistent data to be collected

Data collected needs to be appropriate, accurate and consistent over time and

campaigns.

43

4. Do not measure social media in isolation

Basis for selecting items to be measure needs to encompass the holistic

context of social media

5. Define simple KPIs

Develop KPIs using the REAN framework and its four dimensions using a

combination of soft metrics and hard financials

6. Be consistent

Ensure consistency in time and tools when measuring performance of actions.

7. Avoid relying on simple metrics

Performance measurement should not rely on one simple metrics such as

“Number of Likes”, “Number of Fans” which give a biased image of a complex

situation.

8. Benchmark internally and externally

Benchmark social media performance should be benchmarked either

against:

- another time period – historical data

- other platforms which share similar objective (e.g. blogs, content sharing,

networking…°.

- other channels which share similar objectives (e.g. CRM, e-marketing,

sales promotions, public relations, advertising…)

- other competitors which share the similar objective on the same platform.

9. Use the “So What technique”

When designing or testing new measurement use the ‘so what’ technique to

determine if it actually brings value and pertinence to the evaluation process

10. Review business processes and organization

As you introduce new tools, new measurements, review existing business

processes and organization.

44

As suggested, it is necessary not to measure social media in isolation, to

combine experience from other fields and to identify complementarities. As an

example, a merge of web and social media data would support the production

of superior ROI metrics.

This systemic approach and cross-channel analysis would stimulate the

identification of new and unknown patterns and synergies. This illustrates why

the approach to social media metrics needs to be holistic. The interactions are

not restricted to simple relationships between users, they are also related to

one’s behaviour and relieving the message, possibly making its spreading viral.

Users are now enabled to have a maximized strike force. How to monitor and

measure this amplified word-of-mouth and its actual influences and

consequences?

Only a limited set of diverse indicators would provide an understanding of such

a complex reality. However, these metrics should be aligned with more

traditional ones such as the data captured by the POS or sCRM systems in

order to provide insights that would be more acceptable to executives.

These challenges have been closely addressed while developing the set of

metrics presented in the Table 1 (page 48). This framework has ensured that

hard financials (acceptable by the executives) are included and closely aligned

with soft metrics, initially more relevant to social media. The set of metrics is

based on Sterne’s main objectives for social media (2011), the REAN

framework (Jackson, 2009) and derived from Pentin’s framework (IAB, 2011).

The corresponding tools and their limitations have also been included in order

to ensure critical analysis.

Technology will keep on evolving, and so are the web analytics tools. Some

listening platforms already come up with ROI calculations. However, with

‘inaccurate’ data, social media performance measurement results inaccurate,

making the ROI calculation impossible. Technology will keep on being

dependent on the quality of data collected, and this is also true for social media

measurement.

45

With social media, users have been able to generate content. However, the

analysis of this content is still mostly keyword-based and so is the basis for

collecting social media data. Tools available (e.g. SM2 Alterian) will soon

improve the understanding of the user through further use of advance

intelligence and semantic analysis, but subtleties cannot be interpreted and

analysed by technology.

The absence of digital and social media culture is a decisive obstacle and the

degree of maturity of the organization is currently considered as the most

prominent factor to a successful measure of social media. This justifies a

rigorous and competent recruitment process and the permanent involvement of

the senior management.

Today, it can be stated that we are able to measure (only partially though) the

“what” (ie data that give a quantitative description of actions and behaviours).

The challenge for tomorrow is to explain the “why”. Why does a customer act

this way? Why is he a fan? Without this knowledge, social media will still remain

an unexplored field.

Considering the timeframe of this project, the three research objectives have

been met. Elements of answers have been provided through the collection of

unpublished information that has given further insight to this topic.

As this project only focuses on social media sites, it gets round of certain

issues, which may need to be considered while developing a larger framework.

The two hypotheses have been confirmed to some extent.

It is possible to measure social media performance, as pointed out by Walker,

“something can be measured” (2011). Many barriers intervene in social media

performance measurement, but combination of both soft metrics and hard

financials enable to provide a holistic approach and consistent monitoring that

enable performance measurement over time.

Considering the above, there is definitely irrationality around social media

investment. It is not related to the Dot Com bubble, but rather to a social media

46

bubble, considering that there is one. It seems to be early to mention an

investment bubble in financial terms, but there is surely a genuine social and

engagement bubble that is worth investigating further. Investments should keep

increasing over the next years, and may reach unexpected levels, but it is

premature to talk about an economic bubble, with all the characteristics

involved. The ‘bubble’ may not ‘pop’ but rather dissolve with further maturity,

leading to the emergence of new metrics and new performance measurement

approaches, practices and tools.

In summary, it is possible to measure social media performance, and the

framework recommended can be a support to new metrics acceptance and

recognition. An investment in social media may never produce figures that are

identical to those of a financial statement, and this will probably remain so in the

next years.

The obsolescence or inefficiency of the current metrics does not mean that

there is no return. The challenge is the corporate capacity to accept other types

of measurements abandoning the traditional opposition between quantity and

quality.

As for all the deep shifts required, they will take a few years to be widely

accepted and implemented.

Social media’s role within companies may also evolve and revolutionize internal

functions such as internal communications and social relationships.

Today, the relationship between ROI and social media still needs to be defined

and built. As suggested by Sponder (2011), “at the end of the day, we need to

call ROI something else for Social Media, or just redefine it so that it makes

sense with Social Media, as it doesn't, now.”

Social media do represent a considerable progress for the exchange, the

information and communications around the world. Obviously, the business

world will be increasingly present and involved in this universe. However, the

difference in culture and values will force the companies to adapt their

47

organizations, tools and practices in order to optimize their presence and design

new criteria for performance evaluation. This project has the objective to be a

contribution to this challenge.

48

Table 1: Set of metrics * GATC: Google Analytics Tracking Code

Objective KPI (derived from REAN) Soft metrics Hard financials Benchmark Tool used Limitation

Increase the number of fans

- Number of monthly active users - Proportion of active users / total users

- Cost per unique fan/visitor - Cost per impression

- Other channels - Other platforms - Competitors’ pages

-Facebook Insights - Hitwise, Comscore

- Lack of hard financials - Comparability of data from different sources - Datacapture methods limitations (e.g. reliability)

Expand the audience

geographically

- Users’ provenance

- Visitors coming from any social media platform provenance

- Sales records by country - Historical data

- External benchmark (competitors)

- Facebook

insights - GATC* - Sales records - Hitwise, Comscore, Alexa

- Lack of hard financials

- Comparability of data from different sources - Datacapture methods limitations (e.g. reliability)

Increase the number of positive mentions

- Number of positive mentions - Proportion of positive mentions/all mentions

- Cost per engagement - Cost per referral

- Historical data - Facebook insights - SM2, Audience

- Comparability of data from different sources - Datacapture methods

limitations (e.g. reliability) Engage

Increase the engagement

Post feedback - Patronage - Historical - Other platforms

- Facebook insights

- Datacapture methods limitations (e.g. reliability)

- Content shared - Size of final audience reached

- Cost per lead (share) - Cost per impression

- Historical data - Facebook insights - GATC*

- Datacapture methods limitations (e.g. reliability)

- Incremental traffic generated on site

- Cost per lead - Historical data - Other platforms

- GATC* - Datacapture methods limitations (e.g. reliability)

- Incremental number of fans generated

- Cost per acquisition - Other platforms - Other channels - Historical data

- Facebook insights

- Datacapture methods limitations (e.g. reliability)

- Total audience reached - Cost per impression (final audience)

- Facebook insights

- Datacapture methods limitations (e.g. reliability)

Activate Content shared

- Incremental audience reached

- Cost per impression (final incremental audience)

- Historical data - Other platforms/channels - External

- Facebook insights - SM2 - Competitive intelligence tools

- Comparability of data from different sources - Datacapture methods limitations (e.g. reliability)

Purchase intentions - Number of unique visits on store

locator - Action: add to basket

- Conversion rate to sales

of these intentions - Historical data - GATC*

- Lack of trackability

Revenue

generation Engage

Coupons /Promotional codes

- Coupons downloads - Coupons downloads/promotional code uses vs number of - Impressions of coupons/promotional codes

- Incremental sales value generated by coupons/promotional codes - Cost per incremental sale

- Other platforms/channels - Other sales promotion campaigns

- Sales records - Facebook insights - GATC*

- Comparability of data from different sources - Datacapture methods limitations (e.g. reliability)

48

Table 1: Set of metrics (part 2) * GATC: Google Analytics Tracking Code

Objective KPI (derived from REAN) Soft metrics Hard financials Benchmark Tool Limitation

Incremental sales value generated

- Income - Expenses - Customer satisfaction

- Incremental sales value - Cost per incremental sale

- Historical - External (industry, competitors) - Other channels campaigns

- Sales records - Hitwise, Comscore, Alexa - GATC*

- Comparability of data from different sources - Datacapture methods limitations (e.g. reliability)

Convert Facebook fans into new customers

Conversion rate to sales of Facebook fans

Frequency of purchase - Historical - Other channels

- GATC* - Sales records

- Datacapture methods limitations (e.g. reliability)

Revenue generation

Activate

Conversion rate to sales of new visitors coming from any social media platform

- Conversion rate to sales of new visitors coming from any social media platform

- Average basket value - Incremental sales value generated

Other campaigns Historical

- GATC* - Market research (eg coupon)

- Datacapture methods limitations (e.g. reliability)

Reach Incremental audience - Number of impressions - Incremental audience reached through content sharing

- Cost per acquisition - Cost per impression

- Other campaigns - Other channels - Historical - External (industry)

- Facebook insights - Sales records

- Datacapture methods limitations (e.g. reliability)

Activate/Nurture Customer service standards

- Number of calls from social media users (use a differentiator) vs other channels

- Requests solved on social media platforms - Requests solved on social media platforms through other users

- Cost reduction in customer centres

- Historical data - Other platforms - Other channels

- P&L - Call records

- Trackability - Lack of hard financials

Conversion rate to repeated sales of new visitors coming from

any social media platform

- Repeated sales - Repeated visits

- Retention rates - Repeated sales of new visitors coming to social

media

- Historical data - Other channels/other

platforms

- GATC* - Sales records

- Datacapture methods limitations (e.g. reliability)

ActivateNurture

Repeated sales (loyal customers)

- Repeated sales of social media customers

- Incremental sales - Cost per incremental sale

- Historical data - Other channels/other platforms - External (industry)

- GATC* - Sales records

- Thresholds for loyalty to be clearly defined - Comparability of data from different sources - Datacapture methods limitations (e.g. reliability)

Cost reduction

Nurture Customer satisfaction - Positive vs negative mentions - Content shared

- Cost per referral

- Historical data - Other channels/other platforms

- GATC* - SM2, Audience - Facebook insights

- Comparability of data from different sources - Datacapture methods limitations (e.g. reliability)

50

Appendices

Appendix A: Semi-structured questionnaire

1. In your books and articles, you assert that it is possible to measure social media

performance. Others point out the current lack of accountability or accuracy of

data and metrics available. How do you react to their point of view?

2. Some argue that ROI and social media are opposed: ROI being mostly

quantitative and involving exact figures, whereas the qualitative aspect is key to

social media. In the current context, how would you describe the relationship

between social media and ROI?

According to you, what would be the main difficulties/challenges when it comes

to Social Media ROI calculation?

3. To some extent, there is certain irrationality around social media investment,

what do you think about the idea of a “social media bubble”?

Do you think it can affect the accuracy/credibility of ROI measurement?

4. Putting aside the different goals related to social media, which key good

practice rules would you recommend to use systematically, in order to optimise

social media ROI calculation?

5. If a company with a low level of maturity in social media and web analytics had

to choose 3 metrics to measure the incremental sales value generated, which

ones would you recommend?

6. Some monitoring tools already exist (Alterian SM2, Sysomos Audience), do you

think that they can be considered as an added value for social media

performance measurement?

7. Social Media are developing fast and with it, the necessity to measure

performance and return. Do you think this development will lead to further

accountability of social media engagement and investment, or on the other

hand, will qualitative metrics be better accepted, despite their certain lack of

financial accountability?

51

Bibliography

Blanchard, O., 2011, Social media ROI: Managing and Measuring Social media Efforts

in Your Organization, 2011, Pearson Education

Bloom, P. N., Novelli, W. D., 1981, Problems and Challenges in Social Marketing,

Spring 1981, The Journal of Marketing, American Marketing Association

Bogdan, R. C., Biklen, S. K., 2006, Qualitative research in education: An introduction to

theory and method, 2006, Allyn & Bacon

Bullas, J., 2011, How effective are your Facebook ads?, Jeff Bullas. Weblog, February

25, 2011. Available from: http://www.jeffbullas.com/2011/02/25/how-effective-are-

facebook-ads/ accessed 01/10/2011]

Chaffey, D., 2011, What is social media optimisation (SMO)?, Smart Insights. Weblog,

June 5, 2011, Available from: http://www.smartinsights.com/social-media-marketing-

alerts/what-is-social-media-optimisation-smo/ accessed 01/10/2011]

Chaffey, D., Smith, PR., 2008, eMarketing excellence, 2008, Third edition, Butterworth-

Heinemann

Cheng, L., 2005, Changing language teaching through language testing: a washback

study, 2005, Cambridge University Press. p. 72.

Connoy, J., 2011, How to calculate ROI for Social Meida Investment, Macho Arts.

Weblog, April 17, 2011, Available from: http://machoarts.com/how-to-calculate-roi-for-

social-media-investment accessed 01/10/2011]

Crossan, F., 2003, Research philosophy: towards an understanding, 2003, Indiana

University Bloomington, USA

Denzin, N., Lincoln, Y., 2003, The landscape of qualitative research, 2003, Sage

52

Doran, G. T., 1981, There's a S.M.A.R.T. Way to Write Management's Goals and

Objectives, 1981 , Volume 70, Issue 11, Management Review

Dos Santos, S., 2011, Why Social Media is Disruptive, 8 February, 2011, Online

accessed at http://thesocialcustomer.com/shawn-santos/34382/why-social-media-

disruptive

Duke University, 2010, The CMO Survey, February 11, 2010, American Marketing

Association (AMA)

Dyer, P., 2010, Social media: Everybody’s doing it, but for different reasons,

Pamorama. Weblog, March 7, 2010. Available from:

http://www.pamorama.net/2010/03/07/social-media-everybodys-doing-it-but-for-

different-reasons-charts/ accessed 01/10/2011]

eMarketer, 2011, Social media marketing brings new revenues, Customers, eMarketer

Blog. Weblog, June 24, 2011, Available from:

http://www.emarketer.com/Article.aspx?R=1008459 accessed 01/10/2011]

eMarketer, 2009, Marketers Moving to Digital Media, eMarketer Blog. Weblog, April,

30, 2009, Available from: http://www.emarketer.com/Article.aspx?R=1007064

accessed 01/10/2011]

Evans, D., McKee, J., 2010, Social media Marketing, 2010, Sybex

ExactTarget, 2010, Marketers plan to boost spending in email, social media and other

non-traditional outreach channels this year, February 2010, Econsultancy

Haque, U., 2010, The social media bubble, Harvard Business Review. Weblog, March

23, 2010. Available from:

http://blogs.hbr.org/haque/2010/03/the_social_media_bubble.html accessed

01/10/2011]

Fisher, T., 2009, Return on Investment in Social media: A Look at the Arguments,

September 2009, Journal of Database Marketing & Customer Strategy Management

Volume 16, London, UK

53

Hoffman, D. L., Fodor, M., 2010, Can You Measure the ROI of Your Social media

Marketing?, September 23, 2010.

Hughes, J., 1994, The Philosophy of Social Research, 1994, Longman

Interactive Advertising Bureau, 2011, IAB Internet Advertising Revenue Report, April

2011. Available from:

http://www.iab.net/media/file/IAB_Full_year_2010_0413_Final.pdf accessed

01/10/2011]

Interactive Advertising Bureau, 2009, Social Media Ad Metrics Definitions, May 2009,

IAB [online]

Available from: http://www.iab.net/ugc_metrics_definitions [accessed on 01/06/2011]

Jackson, S., 2009, The Cult Of Analytics : Driving online marketing strategies using

web analytics, 2009, Elsevier/Butterworth & Heinemann

Joshi, A., et al., 2007, Web (2.0) Mining: Analyzing Social media, October, 2007,

University of Maryland, Baltimore, USA

Kaplan, A., Haenlein, M., 2010, Users of the world, unite! The challenges and

opportunities of Social media, Business Horizons 53, 2010

Kallas, P., 2011, What to expect from social media management system, Dreamgrow.

Weblog, 2011. Available from: http://www.dreamgrow.com/what-to-expect-from-social-

media-management-system/ accessed 01/10/2011]

Kallas, P., 2011, 11 free social media and marketing ebooks and reports, Dreamgrow.

Weblog, 2011. Available from: http://www.dreamgrow.com/11-free-social-media-and-

marketing-ebooks-and-reports/ accessed 01/10/2011]

Kallas, P., 2011, How to add Facebook Insights for your website, Dreamgrow. Weblog,

2011. Available from: http://www.dreamgrow.com/how-to-add-facebook-insights-for-

your-website/ accessed 01/10/2011]

Kaushik, A., 2007, Web Analytics: An Hour A Day, 2008, Sybex

54

Kincaid, D., Wong, K., Pone, A., Munro, K., Wise, B., 2010, Forget Social media: Think

Social Profitability, May 2010, Brand Perspective Series, Level 5

Lomax, W., Raman, A., 2008, Analysis and Evaluation, First edition 2008, Butterworth

Heinemann

Melville, P., Sindhwani, S., Lawrence, R. D., 2010, Social Media Analytics: Channeling

the Power of the Blogosphere for Marketing Insight, 2010, IBM

Morris, T., Wood, S., 1991, Testing the Survey method: Continuity and Change in the

British Industrial Relations, 1991, Work Employment and Society, Vol 5, No. 2

Murdough, C., 2009, Social media Measurement: It’s not impossible, Fall 2009, Vol 10

No 1, Journal of Interactive Advertising

Muzio, E., 2011, Is social media the next bubble?, The Huffington Post. Weblog,

August 26, 2011. Available from: http://www.huffingtonpost.com/edward-muzio/is-

social-media-the-next-bubble_b_936805.html accessed 01/10/2011]

Mzinga and Babson Executive Education, 2009, Social Softaware in Business, 8

September 2009, Mzinga and Babson Executive Education

Nielsen, 2011 Social Media Business Benchmarking Study, 2011, Nielsen-Community

Engine

Nielsen, 2010, Social Networks/Blogs Now Account for One in Every Four and a Half

Minutes Online, June 15, 2010 Nielsen

Owyang, J., 2010, Social Marleting Analytics, Altimeter Groupd and Web Analytics

Demystified, 2010, Available from: http://www.web-

strategist.com/blog/2010/04/22/altimeter-report-social-marketing-analytics-with-web-

analytics-demystified/ [accessed 01/10/2011]

Owyang, J., 2010, Framework: The Social Media ROI Pyramid, 2010, Available from:

http://www.web-strategist.com/blog/2010/12/13/framework-the-social-media-roi-

pyramid/ accessed 01/10/2011]

55

Philips, D., Burbules, N., 2000, Postpositivism and educational research, 2000,

Rowman & Littlefield

Pisello, T., 2011, Social Media ROI is Greatest Challenge to Marketing Success in

2011?, The ROI guy. Weblog, April 7, 2011. Available from:

http://blog.alinean.com/2011/04/social-media-roi-is-greatest-challenge.html accessed

01/10/2011]

Powell, G., Groves, S., Dimos, J., 2011, ROI of Social media: How to Improve the

Return on Your Social Marketing Investment, 2011, Wiley and Sons

Prensky, M., 2001, Digital Natives, Digital Immigrants, October 2011, MCB University

Press

Proctor, S., 1998, Linking philosophy and method in the research process: the case for

realism, 1998, Nurse Researcher

Qualman, E., 2011, Socialnomics: How Social media Transforms the Way We Live and

Do Business, 2011, Wiley and Sons

Raice, S., 2011, New Metrics Gauge Heft of Facebook Ads, Wednesday, August 3,

2011, The Wall Street Journal, USA

Remenyi, D., Williams, B., Money, A., Swartz, E., 1998, Doing Research in Business

and Management: An Introduction to Process and Method, 1998, Sage

Rust, R., et al., 2004, Measuring Marketing Productivity: Current Knowledge and

Future Directions, October 2004, Journal of Marketing, Vol. 68, Issue 4.

Salmani, D., Akbari, M., 2008, Fuzzy and research paradigms relationship: a mutual

contribution, May 12 2008, The Academic Leadership Journal

Sanders, S., 2011, The UK Social Media Census 2011, Lawsons conversations.

Weblog, June 8, 2011.

Available from: http://www.lansonsconversations.com/financial-services/infographic-

the-uk-social-media-census-2011/ accessed 01/10/2011]

56

Saunders, M., Lewis, P., Thornhill, A., 2009, Research Methods for Business Students,

2009, Fifth edition, Pearson Education

Scott, R., Jacka, M., 2011, Auditing Social media: A Governance and Risk Guide,

2011, Hoboken, Wiley and Sons

Social Media ROI: Socialnomics, 2009, Video, Eytan, T. Available from:

http://www.tedeytan.com/2009/11/29/4243 [accessed 01/10/2011]

Solis, B., 2011, In Social Media, Your Return Represents Your Investment, Fast

Company. Weblog, Jun 1, 2011. Available from:

http://www.fastcompany.com/1756759/in-social-media-your-return-represents-your-

investment accessed 01/10/2011]

Solis, B., 2010, ROI: How to Measure Return on Investment in Social Media, Brian

Solis. Weblog, February 22, 2010, Available from:

http://www.briansolis.com/2010/02/roi-how-to-measure-return-on-investment-in-social-

media/ accessed 01/10/2011]

Skyrme, D.J., 2007, Valuing Knowledge: Is it Worth it?, 2007, Asian Quality

Smith, T., 2009, The social media revolution, 2009, International Journal of Market

Research, Volume: 51, Issue: 4, World Advertising Research Center Limited

Solis, B., 2011, Social Media ROI: ROI Doesn’t Stand for Return on Ignorance, April

2011, online accessed at http://www.briansolis.com/2011/04/social-media-roi-roi-

doesn%E2%80%99t-stand-for-return-on-ignorance/

Sponder, M., 2011, Social Mediq Analytics: Effective tools for building, interpreting, and

using metrics, 2012, McGraw-Hill

Sterne, J., 2010, Social media Metrics: How to Measure and Optimize your Marketing

Investment, 2010, Wiley and Sons

57

Sysomos, 2010, Sysomos Unveils Audience for Social Media ROI: Connecting Visits to

Websites with Social Media Mentions, 2011, Sysomos online accessed at

http://www.sysomos.com/audience/

Tashakkori, A., Teddlie, C., 2003, Handbook of Mixed Methods in Social and

Behavioural Research, 2003, Sage

Tapscott, D., 1997, Strategy and the New Economy, November/December 1997,

Strategy and Leadership, Vol 25, Issue 6

Thompson, D., 2011, LinkedIn has the highest Price-Revenue ratio of any stock

anywhere, May 20, 2011, The Atlantic

Toffler, A., 1980, The Third Wave, 1980, Morrow

Vickers, J., 2010, The Problem of Induction, June 21st, 2010, Stanford Encyclopedia of

Philosophy

Wasserman, T., 2011, Most marketiers plan to increase social media spend this year,

Mashable. Weblog, April 19, 2011. Available from:

http://mashable.com/2011/04/19/marketers-social-media-spend/ accessed 01/10/2011]

WebsTeam, 2011, Small Business Stats: Social Media Will Grow in 2011 (infographic),

Webs. Weblog, May 26, 2011 Available from: http://blog.webs.com/2011/05/26/small-

business-stats-social-media-will-grow-in-2011-infographic/?display=wide accessed

01/10/2011]

Womack, B., 2010, Facebook 2010 Sales Said Likely to Reach $2 Billion, More Than

Estimated, December 16, 2010, Bloomberg, New York, USA

Yin, R., 1984, Case Study Research: Design and Methods, 1984, Newbury Park, Sage

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