Social media metrics for event sponsorships: a new performance measurement system

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© Matteo Balzarini – 14200328 – Applied Digital Project
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Table&of&Contents&
TABLE&OF&CONTENTS& 4!
EXECUTIVE SUMMARY& 6!
1.! SITUATION ANALYSIS& 8!
1.1&PROJECT&BACKGROUND& 8!
1.2&SPONSORSHIP&AT&LIVE&EVENTS& 8!
1.3&DESCRIPTION&OF&CROWDSIGHT’S&MOBILE&CONTEST&PLATFORM& 8!
1.4&BUYER&PERSONAS& 9!
1.5&SWOT&&&TOWS&ANALYSIS& 10!
1.6&COMPETITOR&ANALYSIS& 11!
1.7&BACKGROUND&TO&MARKETING&PROBLEM& 12!
1.8&AIM&OF&THE&PROJECT& 12!
2.! RESEARCH & ANALYSIS METHODOLOGY& 13!
2.1&STATEMENT&OF&RESEARCH&OBJECTIVES& 13!
2.2&MAIN&RESEARCH&QUESTION&&&HYPOTHESES& 13!
3.! INDUSTRY & LITERATURE REVIEW& 15!
3.1&INDUSTRY&REVIEW& 15!
3.2&LITERATURE&REVIEW& 17!
4.! RESEARCH DESIGN& 20!
4.1&GENERAL&APPROACH& 20!
4.2&RESEARCH&METHOD& 20!
4.3&METHOD&OF&DATA&COLLECTION& 21!
4.4&TARGET&POPULATION&AND&SAMPLING& 22!
4.5&FIELDWORK&AND&DATA&COLLECTION& 23!
5.! QUESTIONNAIRE RESULTS & INSIGHTS& 25!
5.1&DATA&ANALYSIS& 25!
6.! IMPLEMENTATION & RECOMMENDATIONS& 36!
6.1&OVERALL&SPONSORSHIP&PERFORMANCE&METRIC& 36!
6.2&SPONSORSHIP&ECONOMIC&VALUE&IDENTIFICATION& 39!
6.3&CROWDSIGHT&DASHBOARD&RECOMMENDATIONS& 40!
7.! CONCLUSION& 41!
7.1&FINAL&THOUGHTS&&&CHALLENGES&FACED& 41!
7.2&WORK&PROCESS& 41!
7.3&PROJECT&CONSTRAINTS& 42!
8.! REFERENCES& 43!
9.! APPENDICES& 46!
APPENDIX&I&–&COMPETITORS’&ANALYTICS&DASHBOARD& 46!
APPENDIX&II&–&SURVEY& 51!
APPENDIX&III&–&FIRST&LEVEL&METRICS& 56!

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APPENDIX&IV&–&EMAIL&FOR&SURVEY&DISTRIBUTION& 57!
APPENDIX&V&–&SURVEY&RESULTS&MATRIX& 58!
APPENDIX&VI&–&SURVEY&DATA&ANALYSIS& 59!
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EXECUTIVE SUMMARY
The aim of this project is to investigate the correct service solution for Crowdsight, a mobile contest
platform targeting large-scale live events, which allows sponsor companies to engage fans, generate
viral promotion and reward the best content creators.
Crowdsight is currently developing a beta of the aforementioned platform and needs to effectively
understand what online analytics data to display to its customers (the sponsors) on the platform’s
customer dashboard, in order to best inform sponsors about contest performance, social engagement
produced -both internally at the app level and externally on Social Media- and possibly the
economic value associated to this data.
The research presented the exciting opportunity to understand customers’ needs in terms of online
analytic metrics and build new, more representative aggregated metrics, to better understand and
track user engagement and its economic value for companies.
The project starts by defining background information on the company, its competitive environment
and marketing problem, in order to better understand the scenario of investigation.
Subsequently, the market research problem is defined, accompanied by main research objectives
and hypotheses.
A separate chapter presents an overview of the secondary research currently available on the subject
and an overview of the sponsorship market, across all industries, mainly in Europe.
The third chapter illustrates the research design in all its parts: starting from the overall general
approach, moving towards the method of data collection, population target and sampling, research
instruments utilized, an outline of the field work and data collection which will be performed, to
conclude with data analysis and anticipated findings.
The fifth chapter analyzes the results of market research underlining potentially interesting findings.

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The sixth chapter proposes the adoption of two new metrics, calculated through a combination of
research insights and mathematical applications: Overall Sponsorship Performance metric (OSP)
and a Sponsorship Economic Value Identification metric (SEVI), which, if implemented, would
fundamentally improve current Crowdsight’s sponsorship analytics service offering, giving it a
considerable competitive advantage in terms of service value for the ideal target customers.
Finally, Chapter 7 presents project conclusions with an analysis of the work process conducted and
final thoughts on learnings and challenges faced during the project.

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1. SITUATION ANALYSIS
1.1 Project Background
Crowdsight is a spin-off from sponsorship sales operations completed via the sports marketing
agency Sway Sports. The sponsorship sales work unveiled a significant demand from sponsors to
digitally activate at live events - for which they lacked a viable technology platform.
The company aims to provide such platform - implementable as a stand-alone app, as well as
embeddable within any event app.
Crowdsight was set up in March 2014 and took part in the New Frontiers program, which helped
further define the business model. The company’s aim is to service a mobile platform that allows
sponsors to create sponsorship value easily.
1.2 Sponsorship at Live Events
Sponsorship takes place when a company pays to get associated with an event.
Companies sponsor various types of live events, from sport matches to music festivals. When it
comes to sports events companies might directly sponsor teams, tournaments or fairs, with the aim
of exposing their brands to fans.
When deciding to sponsor a particular event companies look at the event relevancy to their
products, their brand fit in the event in terms of target market, the mission alignment between
company and event and the business results, in terms of tangible return on investment from the
sponsorship. This is generally considered in terms of profit.
1.3 Description of Crowdsight’s Mobile Contest Platform
During live events, sponsored rewards are offered to fans in return for structured co-created
interactions with brands. The platform measures the impact of this digital activation and fans-to-
brand engagement through a gamification experience of live events, to gain actionable insights on
the active user base and effectively identify the real sponsorship value. Fans at live events will be
able to participate to contests organized by the sponsor and win prizes.
Crowdsight plans to start by offering a photo contest solution where fans will be asked to take
pictures during the event and share them on Social Media, in order to win prizes.

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Additionally, photos taken and shared through Crowdsight mobile app will include the sponsor’s
brand in order to boost sponsorship value.
Finally, all the content co-created by fans through Crowdsight app will be available to be used by
the sponsor.
1.4 Buyer Personas
John Mobile
Freshly made sponsorship manager with a passion for Impressionist art
Age: 32
Marital status: in a relationship
Wealth level: Medium-income (€12k/mo)
Occupation: Marketing Manager at Vodafone
Interests: Impressionist art, baseball, eastern philosophies, charities
Digital channels reach: SEO, SEA, Display Ads, Facebook, Email, LinkedIn
Time spent online: 15hrs a week (including work-related browsing)
Digital knowledge: early immigrant
Goals: become CMO in his firm, travel around Asia, create his art collection.
He frequently organizes sponsorships at live rugby matches in Ireland
Challenges: John needs to correctly assess the performance of sponsorships he manages, generate
engagement and gather useful insights about Vodafone brand recognition
How Crowdsight can help: Crowdsight provides an immediate feedback on sponsorships’ engagement
performance thanks to its state of the art analytic dashboard
Quote: “If it’s shared, works!”
Common objections: “Dear Crowdsight, I don’t want the usual analytic metrics, I need answers!”
Marketing messaging: “Try Crowdsight and discover how easy can be to understand your
sponsorship’s performance through real-time analytics”
Scotty Sporty
Dedicated marketer, wannabe marathoner
Age: 30
Marital status: married, no kids
Wealth level: Medium-income (€6k/mo)
Occupation: Marketing Manager at PaddyPower
Interests: Running, French movies, design, yoga
Digital channels reach: SEO, SEA, Display Ads, Facebook, Email, LinkedIn, YouTube
Time spent online: 25hrs a week (including work-related browsing)
Digital knowledge: Early immigrant
Goals: Make an impact in his new role by organizing a first successful sponsorship in a football match,
win the Dublin marathon, go for a month of yoga and meditation in Tibet.
Challenges: Scotty needs to generate re-usable content that can be leverage in the already successful
PaddyPower social channels to create additional buzz and attract eve more potential customers
How Crowdsight can help: Crowdsight provides native engagement in live events through mobile
contests that generate re-usable co-created content ready to be integrated into marketing campaigns
Quote: “If you are losing faith in human nature, go out and watch a marathon”
Common objections: “I need high quality content from fans, not the usual “posing” pictures!”
Marketing messaging: “With Crowdsight you can create totally customized contests that speak at your
audience and generate untapped engagement opportunities in live events”

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1.5 SWOT & TOWS Analysis
Strengths Weaknesses
1.#Strong#relationship#with#sponsors 1.#New#entrant#to#the#event#management#
industry
2.#Deep#knowledge#of#live<events#environment 2.##Limited#funds#and#resources
3.#Proprietary#contest#platform 3.##Small#operational#and#technical#team
Opportunities (SO2Strategies)242Attack (WO2Strategies)242Feeding2Attack
1.#Comprehensive#Analytics 1.#Build#a#solid#understanding#of#Sponsors'#needs#
in#terms#of#Analytics#and#performance#and#ROMI#
expectations
1.#Build#a#flexible#Analytics#and#reporting#
system#focused#mainly#on#Sponsors,#
avoiding#direct#competition#with#the#event#
management#industry#whose#focus#is#
mainly#on#event#organizers
2.#Untapped#market#niche 2.#Establish#as#leading#innovator#by#developing#
proprietary#engagement<based#performance#
metrics,#securing#a#rising#niche#in#the#live<events#
sponsorship#industry
2.#Secure#additional#funding#by#proving#
proof#of#niche#potential
3.#Event#sponsorship#expenditure#growth 3.#Leverage#relationships#with#sponsors#and#
industry#knowledge#to#obtain#more#sponsorship#
expenditure
3.#Grow#the#team#organically#focusing#on#
hiring#technical#sales#reps,#able#to#explain#
the#platform#value#to#Sponsors
Threats (ST2Streategies)242Defense (WT2Strategies)242Feeding2Defense
1.#Bigger#players#adaptation 1.#Keep#innovating#by#integrating#new#
technologies#such#as#wearables#,to#keep#the#
company#ahead#of#competitors
1.#Build#solid#partnerships#with#few#critical#
Sponsors#at#an#early#stage
2.#New#entrants#with#better#engagement#
delivery#technologies
2.#Create#higher#barriers#of#entry#by#developing#a#
comprehensive#analytics#system#based#on#industry#
insights#and#hard#to#replicate
2.#Build#a#personalized#and#dedicated#
communication#channel#with#sponsors
3.#Increasing#privacy#regulations 3.#Develop#clear#privacy#policy#and#give#fans#
adequate#tools#to#manage#their#communication
3.##Involve#the#whole#team#in#focus#groups#
around#privacy#to#develop#new#and#
unobtrusive#data#collection#strategies

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1.6 Competitor Analysis
The majority of event-engagement solutions currently present on the market focuses o event
organizers, offering them event management tools and basic analytics, while driving engagement
through attendees profile, guided polls, internal messaging systems, maps and exhibitors lists.
(See APPENDIX I for snapshots of the Competitors’ Analytics Dashboard)
Competitor Description Target Service Analytics5Features
Livecube
Livecube(serves(event(apps,(fusing(game(
mechanics(to(audience(participation(
Event(organizers
Social(interactions(
management(and(
gamification,(agenda,(surveys
Basic(analytics(about(devices(used(to(
access(the(platform,(social(sharing(
metrics,(most(reported(content(and(
top(contributors
Quickmobile
QuickMobile(develops(mobile(event(apps(
to(compile(and(manage(information(
about(meetings,(conferences,(and(
exhibitions
Event(organizers
Custom(event(app(with(
schedule,(locations,(speaker(
profiles,(presnetations,(
exhibitor(details,(internal(
news(feed,(chat,(photos
Basic(analytics(dashboard(displaying((
attendees(activity,(survey(results,(
social(sharing(metrics
Crowdcompass
CrowdCompass(develops(event,(
conferencing(and(tradeshow(mobile(
apps
Event(organizers
Custom(event(app(with(
schedule(integration,(
attendees(profiles,(interactive(
maps,(internal(chat,(
interaction(gamification,(
polling
Only(analytics(about(app(downloads(
and(usage
EventMobi
Mobile(event(apps(with(gaming(layer(for(
exhibitions(and(live(events
Event(Organizers
Event(app(offering(internal(
chat,(maps,(attendees(profiles,(
polls,(files(repository,(
interaction(gamificatiom,(
exhibitors(profile(and(alerts.(
The(app(also(offers(internal(
advertising(options(for(
sponsors
and(usage
Fish
Platform(for(data(collection(and(fans(
engagement(through(a(mix(between(
online(and(offline(guided(experiences
Event(organizers(/(
Sponsors
Kiosks,(line(control(system,(
photo(contests,(custom(
messages(to(fans,(usage(and(
sharing(analytics
Geographic(data,(asset(engagement(
metrics,(KPI(benchmarks,(lead(data,(
brand(sentiment(analysis,((social(
media(tracking
DoubleDutch Event(experience(management(app Event(organizers
Attendee(networking,(polls,(
lead(scanning,(content(
management
and(usage
Bizzabo
Platform(for(event(and(attendees(
management
Event(organizers(/(
Sponsors
Ticketing(and(registration,(
agenda,(polls,(reports,(alerts,(
contacts(management,(
Sponsors(splash(screen(and(
dedicated(profile
Only(analytics(about(tickets(sold(and(
app(usage

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1.7 Background to Marketing Problem
Crowdsight is planning a beta release of its platform for September 2015, to perform user testing
and gather crucial information that will be used to deliver the first official release.
The beta release is going to be a perfect occasion to test the platform’s core value, which consists
in both a user-end app (which will be used by fans at live events) and a contest administration
dashboard (accessible for sponsors to deploy and evaluate analytics data regarding contests’
performance).
The value of this platform for sponsors relies both on the ability to serve relevant contests to a
specific fan base during events, and on the possibility to effectively track and evaluate their
contest performance, being able to associate macro and micro economic values to every possible
metric identifiable.
Sponsors always strive to identify ROI connected to their marketing efforts, so proper analytics
data, together with Crowdsight proprietary analytics metrics (part of the company’s competitive
advantage), would be able to offer an indispensable asset for marketing and sponsorship
activities.
1.8 Aim of the Project
The project aims to devise and evaluate user engagement metrics and new analytical approaches
specific to the live events environment, with the goal of assembling a rich set of metrics that
would from the company’s core value. Furthermore, an appropriate economic value calculation
per each social channel will be devised, which will serve to effectively communicate economic
value of sponsorships to Crowdsight’s clients.
These analytical tools will have the fundamental role to provide timely information to sponsors
about their user base composition, demographic characteristics and online behavioral traits,
while associating an economic value to them.

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2. RESEARCH & ANALYSIS METHODOLOGY
2.1 Statement of Research Objectives
The project consists in a multi-level analysis of Social Media analytics through the following:
● Secondary research, to obtain information about suggested metrics and analytical
approaches.
Key deliverable: preliminary industry-ready analytical metrics.
● Survey deployment to sponsors, to gather data about sponsors’ data analytics needs and
preferences peculiar of the live entertainment environment and potential need for
proposed new metrics.
Key deliverable: minimum of 50 valid full survey completions from verified sponsors.
The data gathered from surveys is then broken down by sponsors segments and evaluated with
statistical techniques to identify potential matches with the proposed analytical metrics and
elaborate a consistent service pricing-model.
By understanding sponsors’ needs in terms of online analytic metrics the research could allow
the formulation of new, more representative aggregated metrics, to better understand and track
user engagement and its economic value for companies.
2.2 Main research question & hypotheses
The research aimed at answering the following questions, both through Primary and Secondary
research:
• What kind of online analytic metrics do sponsors need to effectively evaluate their
sponsorship efforts?
• Do they need metrics on Social Media echo from their sponsorship activity?
• Would they be interested in high-level aggregated metrics that can give more
comprehensive but less granular information on their sponsorship performance?
• Do these metrics need to have a connection with their sponsorship investments? If so,
how strong this connection has to be?
• Is there any difference in metrics preferences among sponsors with different
characteristics?

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The initial hypothesis started from the basis that sponsors actively promote events in order to
gain a return in terms of brand exposure and advertising, which in turns should translate in more
sales and additional revenue.
The research moved then from the assumption that sponsors want to effectively monitor and
analyze their sponsorship investments in order to understand their actual efficacy and usefulness
as part of their marketing strategy, for the company cause.
Secondary research gave an important contribution in understanding the current basis of theories
and empirical analysis about online analytic metrics, thus informing the creation of a
comprehensive set of metrics to be proposed to sponsors, through Primary research, by inviting
sponsors to complete an online survey.
The survey collected data about sponsors’ interest in different categories of metrics.
Additionally, the survey tried to assess the level of importance placed by sponsors on
understanding the economic value of proposed metrics, their interest in more aggregated metrics
(which would put in relation different online behavior aspects) and finally, if there is any
correlation between different sponsors’ characteristics and their analytic needs.
The general belief, which is here investigated and expanded, is that sponsors want to understand
the economic value of their sponsorship activities and are interested in obtaining relevant metrics
on any social activity resulting from their efforts, be it internally about the contest or externally,
from Social Media echo.
The research aimed at identifying actionable metrics, which will serve the marketing department
of sponsors and inform about the performance of contests carried through Crowdsight platform.
The research, through a structured survey, proposes specific metrics and suggests analytics areas
of interest to sponsor, collecting information about their preferences.
A data analysis crossing this information with the characteristics of the respondents within the
identified sample, served as fundamental basis for the development of a comprehensive and
structured analytics system, including all the relevant metrics identified, which has been
proposed to Crowdsight for implementation in their service dashboard.

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3. INDUSTRY & LITERATURE REVIEW
3.1 Industry review
The sponsorship industry is showing a steady growth in terms of spending. IEG’s 30th
year
industry review has forecasted that the global market growth should remain unchanged from last
year, bringing a 4% increase in spending, for a total of $57.5 billion for 2015, of which 15.3
Billion in Europe (with a 3.3% increase from 2014)
The main properties of this spending will be:
1. Sports – with 60% spending
2. Entertainment – with 10% spending
3. Causes
4. Arts
5. Festivals, Fairs and Annual Events
6. Associations and Membership Organizations
Marketers’ focus in this industry is on digital marketing, pursuing commercial partnership
opportunities in the digital landscape. The purpose is always the same, to “be the best at
delivering the audience corporate partners seek” (IEG, 2015).
Sponsors need to be able to measure, analyse and effectively use audience data to generate
pertinent insights.
Sponsorship facilitators like Crowdsight, exercise the crucial role of giving sponsors the tools to
generate value for fans and participants at live events, through the digital means (Social Media,
creative platforms and sharing capabilities).
The critical point for sponsorships facilitators keeps being their success in reaching, and
meaningfully engaging, the right audience. Technology nowadays is enabling these companies to
have a better understanding of their audience characteristics, needs and interests, to more
efficiently serve the right content at the right time.

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Online users have access to a whole new range of actions: downloading, manipulating and
sharing content with each other or with brands has opened new interaction scenarios between
companies and their consumers, like co-creation or viral trends generation.
Consumers want to be involved and express themselves (especially at live events), sharing their
experiences with their friends and acquaintances, they are tired of observe passively.
The digital data collected by platforms allows marketers to create custom promotions based on
specific behaviours and desires of their fan base. In this environment, audience data becomes
fundamental to create meaningful fan activation plans and deliver the needed ROI to brands.
This innovative new landscape is generating a more transparent communication-style between
consumers and organizations (potential threats to brands’ reputation), which need to be
effectively assessed and managed; consumers want their favourite brands to meet their
expectations, they are now able to publicly raise concerns and request quick solutions. Brands
and sponsorship facilitators need to be agile and solve potential disputes, keeping in mind that
consumers’ concerns or requests are often great opportunities to strengthen their brand image, by
exceeding their expectations and generate emotional attachment providing great solutions.
Additionally, with the right data from their audience companies have the opportunity to
anticipate their customers’ needs and generate attachment to the brand.
The overall consensus is that using digital activation and Social Media are two huge tools that
are slowly but surely moving from a luxury to a requirement in terms of brand activation.
Companies that are stuck in a traditional mindset are having a difficult time grasping these
concepts, but those large companies that understand the importance are excelling and growing
steadily.
Digital activation provides customers a way to interact with the brand and establish an easy and
modern way to communicate with brands as well.
A report of Cynopsis Media of 2014 shows how companies are using this data to see their brand
grow, and to analyze their target audiences and age groups and reach the Millennial generation
more easily. Using digital activation at live events gives consumers a way to really connect with
the energy of the vent, and also gives them a way to provide feedback, reinforce the brand's
positioning in the minds of the consumer, and helps to cut through the traditional advertising
clutter like print, web, and TV ads.

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3.2 Literature Review
The relevant literature for the proposed market research starts from secondary research in the
area of mobile user engagement, that constitutes pertinent and actionable ground for the
proposed primary research subject, and then moves towards a more specific and deeper analysis
on secondary research about currently available and industry-ready analytics metrics potentially
applicable to the nature of live events sponsorship contests, such as gamification analytics (for
internal contest analysis), Social Media analytics (for contest’s echo on external social
platforms) and a metrics fundamental structure analysis, to gather insights on how to create
potentially new metrics.
The following is a review of some of the relevant secondary research pieces available, which
have been used to inform the primary research effort, primary object of this project.
The literature review of previous researches on the topic has been carried out interrogating
existing academic literature databases such as ABI/INFORM, UCD OneSearch, Business Source
Elite, Science Direct.
Keyword such as: Social Media analytics, Social Media evaluation, measuring online social
activity, Social Media and economic value etc., have been used to research relevant secondary
data on the topic of mobile engagement in large-scale live events. The search returned articles,
thesis dissertations, journals, publications and book chapters. Approximately 60% of these
results were pertinent to the aforementioned analysis, while the rest was made of technical
research on mobile technology and large-scale technology application for live events.
Analytics&metrics&for&sponsorships’&mobile&engagement&&&coZcreation&
&
Posing the necessary basis for an evaluation of existing literature about possible analytics
approaches on mobile engagement in sponsorship, a study of mobile user engagement (MoEN):
Engagement motivations, perceived value, satisfaction, and continued engagement intention.
Decision Support Systems, by Kim Y. H., Kim. J. D., Watchter K., (2013), investigates and
proposes a mobile user engagement model which aims to explain the mobile engagement
intentions, motivations and perceived value. The results of this investigation indicate that
motivations influence the perceived value and engagement intention.
Moreover, a research carried by Jacucci, G., Oulasvirta, A., Salovaara, A., & Sarvas, R., (2005),
titled “Supporting the shared experience of spectators through mobile group media”, investigates

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the possibilities and boundaries of mobile in terms of group awareness and coordination,
meaningful construction of event experiences and active vs. passive spectatorship implications.
Jannucci G., Oulasvirta A., Ilmonen T., Evans J., Salovaara A., (2007), research titled
“CoMedia: Mobile Group Media for Active Spectatorship” keep analyzing the aspect of mobile
engagement focusing on CoMedia for large-scale events active spectatorship. The authors tested
their original assumptions with two field trials and found that CoMedia facilitated onsite
reporting to offsite members, coordination of group action and show that the integrated approach
better support the continuous interweaving of use with changing interests and occurrences proper
of large-scale events.
Moving on to review existing literature on the more specific subject of social analytics metrics,
keyword such as: Social Media analytics, Social Media evaluation, measuring online social
activity, Social Media and economic value etc., have been used to research relevant secondary
data on the previously mentioned databases. The search returned articles, thesis dissertations,
journals, publications and book chapters. Approximately the 80% of the results were relevant to
Social Media analytics, while the remaining 20% consisted of sector specific Social Media
analysis.
Donston-Miller (2012) suggests five metrics: quality of followers, demographics, top contents
(tweets/posts), page views and CTR, conversation metrics (Donston-Miller, 2012).
Sterne (2010) suggests comprehensive Social Media analytics measures include: reach,
influencers, sentiment analysis, mentions, conversation metrics.
Analytics&of&gamification&elements&
&
Subsequently, looking at existing secondary research on the subject of gamification analytics,
Heilbrunn, B., Herzig, P., & Schill, A., (2014), presented a research titled “Towards
Gamification Analytics-Requirements for Monitoring and Adapting Gamification Designs”. The
paper presents a model of 22 requirements that might be used to evaluate existing analytics
solutions or construct new methods for gamification analytics. The model is then validated based
on comprehensive expert interviews.
Social&Media&Analytics&&&economic&value&&
&

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Moving forward, an important element of effective analytics for sponsorship is the assessment of
an economic value to it, Calbreath (2010) describes how, despite companies’ higher expenditure
in social engagement, they are still unable to effectively quantify the economic value that these
platforms generate. Mcnamara (2011) seem to be of the same opinion, assessing the difficulty of
measuring the value of Social Media, in a report published by the Australian Centre for Public
Communication.
Hoffman and Foder (2010) describe how many professionals using Social Media applications
have a focus only for ROI, not analyzing qualitative objectives such as the value of a user
interaction (comment, tweet etc.) about brands.
Kaushik (2012) focuses on the fact that what is really important in Social Media are not the
“counters” (number of followers, likes, tweets etc.) but the actions generated by content
attention/virality, which create economic value for businesses. This is especially true given that
those “counters” metrics are today easily inflatable by buying likes or followers services.
Madison (2012) argues that the typical “vanity” metrics (page views, unique visitors, registered
members tec.) need to be combined with other (economic) metrics in order not to fail responding
to the usual “so what?” question.
Finally, Mauboussin, (2012) argues about a 4-steps process to outline the economic value of
social metrics: define governing goal, determine cause-effect for value drivers, identify activities
proper to employees to help pursue the governing goal, monitor and recurrently evaluate the
identified metrics so that they keep linking employee activities with the governing goal.

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4. RESEARCH DESIGN
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4.1 General Approach
This is a conclusive descriptive market research, which proposes a set of analytic metrics to the
sponsors’ sample in order to assess their preference and strategically identify the ones, which are
closer to their business needs.
The research is descriptive because the project didn’t aim at assigning a relation between
variables, focusing instead on describing the analytics needs of sponsors, who are the target
market of Crowdsight.
The research is also conclusive, since the project has the objective to generate specific
conclusions that could then be turned into business decisions by Crowdsight. The information
collected is in fact evaluated, crossing it with the sample internal characteristics, and used to
inform the development of specific Crowdsight service features.
I have been in charge of the whole research, performing research design, survey preparation and
taking care of the fieldwork. Crowdsight founder assisted me providing his industry know-how
and sponsorship market expertise, while the company CTO guaranteed on the potential
application of the metrics proposed by the survey.
The survey data analysis, together with recommendations, a proposed implementation plan and a
review of the existing Secondary research available on the subject, have been organized in this
final report, and handed to Crowdsight founder.
The information generated from this market research has been presented to UCD Smurfit and to
Crowdsight only and they haven’t been disclosed to any other party. On the other hand, the
survey results have been shared with those respondents who expressively asked to receive them.
4.2 Research Method
The market research followed a quantitative methodology, with the objective to quantify and
measure the data and derive pertinent results from the sample population, which have been
extended to the target customers’ population.

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This methodology allowed using structured data collection techniques, and applying rigorous
statistical data analysis to the sample data, providing precise results.
The research has been carried through an opinion based research method with 11 close-ended
questions for a total length of not more than 5 minutes. (See APPENDIX II for a copy of the
Survey).
In order to prepare the survey’s questions, common analytic metrics have been identified and
divided into two main groups: First Level and Second Level metrics.
First level metrics consist of online basic social signals (also referred in this research as
“interactions”): Facebook Like, Google +1, Twitter Favorite, Instagram Like, Facebook Share,
Google Share, Twitter Re-tweet, Facebook Comment, Google+ Comment, Twitter Comment,
Instagram Comment.
Second Level metrics consist of calculations made on top of First Level metrics. (See
APPENDIX III for First Level Metrics identified)
Below is a table describing the Second level metrics identified:
4.3 Method of Data Collection
An online anonymous survey has been used to collect the necessary data from marketing and
sponsorship managers, within the companies part of the sample. This way it has been possible to
test sponsors’ preferences and needs for specific analytic metrics. This method presented low
costs of implementation and gave an element of scale to customers’ preference, giving a
directional method of measuring intensity.
Qualtrics was used as survey preparation, data collection and storage system.
Metric Definition
Amplification0Rate
Rate%of%shares%per%social%media%post.%
Express%the%pace%at%which%your%audience%is%
growing.%
Conversation0Rate Rate%of%comments%per%social%media%post.
Applause0Rate
Rate%of%appreciation%signals%per%social%media%
post%(e.g.%likes,%+1s,%favorites)
Economic0Value Value%per%engagement%action%(interaction)

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The service immediately collects and stores respondents’ answer as they type them; for this
reason, the most important questions have been placed towards the beginning of the survey, thus
increasing the likelihood of having them completed.
The survey questions were built utilizing statistical measurement scales (i.e. nominal, ordinal,
interval, ratio).
A matrix row of data for each respondents has been generated contextually to the respondent
typing answers.
Additionally, by presenting questions on a screen, they have been presented in a uniform way
among respondents, read and understood the questions proposed autonomously, minimizing any
variability of fruition and bias, which would be have been present through an intermediary. The
questions style is formal, suited for B2B communication.
A potential disadvantage of this data collection method is posed by respondents’ identity.
Surveys have been sent by email to marketing and sponsorship managers, but there has been no
certainty that they were the actual person responding to the survey. By delegating to other
employees within the company, they could have potentially harmed the survey’s results
reliability. This issue is clearly out of my sphere of control, allowing me to minimize the
possibility of this outcome by accurately sending the survey to the right person within each
company.
Finally, this methodology could have create a sampling bias, since the demographic profile of
internet users does not represent the general population but, given the nature of the study, the
sample necessarily consisted of internet users, thus reducing the risk of such bias to occur.
4.4 Target Population and Sampling
The target population consisted of large companies (“sponsors”) that sponsor live events in
Ireland and UK.
The sample size consisted of about 1,255 large companies marketing and sponsorship managers
from the aforementioned database. The sample goal was identified as 50 respondents who fully
complete the proposed survey. This is a commonly accepted size, which should guarantee
sufficient data to generate conclusions and allow the formulation of recommendations.

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The sample has been chosen from an existing database of 3,221 large sponsor companies in
Ireland and the UK, which has been collected through multiple LinkedIn queries, indicating full
name, email, job description, company of employment, company size (in terms of employees),
company industry and regions of business (i.e. Irish, British or multinational) for each entry. The
database has been collected by Crowdsight founder and consists of potential clients of
Crowdsight, previously selected following Crowdsight target customer’s characteristics.
Given the time constraints for the production of this research, the sample has been extracted
following a simple random sampling technique. The entire database had equal chance of being
selected. The sample was probabilistic, with every population unit having the same probability
of becoming part of the sample, and representative of the whole population object of the study.
The simple random sampling has been carried out, by establishing a sampling ordering for
extraction.
The database entries have been numbered and extracted randomly -using Excel- to populate the
sample: each Excel row contained a company; a random number has been generated and the
corresponding row has been extracted. This procedure was repeated for 1,255 times, to obtain
1,255 sample entries.
Since from a first random extraction too many sample entries shared the same “company size”
characteristic, a new sampling round of extraction was performed in order to obtain a
representative sample of the population object of the study.
The choice of using an already existing database of sponsor companies of various sizes, had the
intent to favor a better response rate and an equally good geographical coverage of the sample,
following Crowdsight business expansion objectives. The rationale behind it is that the database
constitutes a close enough representation of Crowdsight target market, since it has been
generated internally, keeping in mind its business plan.
4.5 Fieldwork and Data Collection
Given the identified sample goal of 50 respondents, and estimating a response rate of about 4%, I
sent out 1,255 survey requests on the 30th of June, and collecting responses in the following 7
days.
The survey didn’t present a declared expire date, in order to not reduce response rate from late
respondents and avoiding to generate late deadlines for those willing to answer immediately.

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In case the number of full responses weren’t enough to reach the sample goal of 50, the sample
size would be then increased, by performing additional simple random samplings on the
remaining database entries.
The survey was sent through my personal student email address at UCD, explaining the nature
and reason of the survey request. (See APPENDIX IV for a copy of the Email for Survey
Distribution).

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5. QUESTIONNAIRE RESULTS & INSIGHTS
5.1 Data Analysis
The survey had a 5,26% response rate and produced a total of 70 answers, of which only 66 valid
responses, thus more than the minimum sample goal required.
The collected data was cleaned (through eye-balling, logic checking and spot-checking
techniques) of outliers that could cause analysis distortion, and then processed through SPSS.
As previously mentioned, the collected data has been organized into a data matrix and
subsequently utilized to perform different statistical analysis.
The following analysis have been performed:
Univariate analysis – for information on online analytic metrics:
• Identification of central values, highlighting mean, median and mode
• Analysis of ranges, quartiles and standard deviation
Bivariate analysis – for information on relations between metrics of interest and between metrics
preferences and sponsor characteristics:
• ANOVA
• Correlation
• Difference between means
The collected data is uniform and has allowed the creation of a data matrix, used to statistically
analyze the data. (See APPENDIX V for the Survey Results Matrix).
The following is a descriptive analysis of results for each survey question.

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Q1. When activating your sponsorship using Paid Channels, which Social Networks are most
important? Please rank the following by order of importance (1 is most important, 5 is least):
The results for this question clearly display the following ranking among Social Networks:
1) Facebook
2) Twitter
3) Google+
4) Instagram
5) Snapchat
There seems to be a dominance of Facebook (40/60 answers) and Snapchat appears clearly the
least important Social (41/60 answers place it as last).
Twitter holds a second place in terms of interest, while Instagram and Google+ position
themselves at a middle level, with means around 3 (3,43 and 3,48 respectively).
As a side note, some respondents replied by email claiming the absence of LinkedIn among the
socials to be ranked. The choice was intentional and was due to the nature of sponsored live
events served by Crowdsight, which don’t make LinkedIn a suitable Social Network used for
content sharing on those occasions.
# Answer 1 2 3 4 5 Total2
Responses
1 Facebook 40 17 1 1 1 60
2 Google+ 4 6 23 14 13 60
3 Instagram 0 6 21 31 2 60
4 Snapchat 0 1 4 14 41 60
5 Twitter 16 30 11 0 3 60
Total 60 60 60 60 60 @
Statistic Facebook Google+ Instagram Snapchat Twitter
Min$Value 1 1 2 2 1
Max$Value 5 5 5 5 5
Mean 1.43 3.43 3.48 4.58 2.07
Variance 0.59 1.30 0.53 0.48 0.91
Standard$
Deviation
0.77 1.14 0.72 0.70 0.95
Total$Responses 60 60 60 60 60

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Q2. With respect to sponsorship activation, which Social Networks are preferred to generate
Earned Media? Please rank the following by order of importance (1 is most important, 5 is least):
Results of Q2 are in line with expectations and appear very similar to the ones given for Q1.
Respondents seem to place the same importance to Social Networks for paid and earned content
generation.
Q3. As an estimate, how much would you spend to digitally activate your sponsorships per
event?
# Answer 1 2 3 4 5 Total2
Responses
1 Facebook 39 21 1 1 0 62
2 Google+ 6 8 28 11 9 62
3 Instagram 0 3 23 33 3 62
4 Snapchat 0 0 3 16 43 62
5 Twitter 17 30 7 1 7 62
Total 62 62 62 62 62 B
Statistic Facebook Google+ Instagram Snapchat Twitter
Min$Value 1 1 2 3 1
Max$Value 4 5 5 5 5
Mean 1.42 3.15 3.58 4.65 2.21
Variance 0.38 1.27 0.44 0.33 1.45
Standard$
Deviation
0.62 1.13 0.67 0.58 1.20
Total$Responses 62 62 62 62 62
# Answer Response %
1 €0$€10K 45 68%
2 €10K$€50K 16 24%
3 €50K$€100K 1 2%
4 €100K+ 4 6%
Total 66 100%
Statistic Value
Min$Value 1
Max$Value 4
Mean 1.45
Variance 0.65
Standard$Deviation 0.81
Total$Responses 66

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In terms of event sponsorship expenditure for digital activation, 45 (68%) of the 66 respondents
claimed that would spend between €0-10K, while 16 respondents would spend between €10K-
50K. Only one respondent would spend between €50K-100K and 4 respondents would spend
more than €100K.
There is a concentration of answers on the first class (€0-10K), the 92% is positioned up to
€49.5K, and only the 8% would spend about €50K or more.
Q4. Please answer the following questions related to sponsorships' fan generated content:
This set of questions was placed in the survey in order to better inform Crowdsight market
strategy but did not play an active role in the proposed analytics strategy.
1) How useful do you consider fan generated content for sponsorship activation efforts?
61 out of 66 respondents finds fan generated content useful to a certain extent, while a
minority (5/66) finds it completely useless.
# Question Not+at+all Somewhat Very Total+
Responses
Mean
1 How%useful%
do%you%
consider%fan%
generated%
content%for%
Sponsorship%
activation%
efforts
5 30 31 66 2.39
2 How%difficult%
is%it%to%find%
fan%
generated%
content%that%
you%can%use%
with%
permission
9 47 10 66 2.02
Statistic How*useful*do*you*consider*fan*
generated*content*for*Sponsorship*
activation*efforts
How*difficult*is*it*to*find*fan*
generated*content*that*you*can*
use*with*permission
Min$Value 1 1
Max$Value 3 3
Mean 2.39 2.02
Variance 0.40 0.29
Standard$Deviation 0.63 0.54
Total$Responses 66 66

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2) How difficult is it to find fan generated content that you can use with permission?
The great majority of respondents (57/66) finds it difficult to obtain fan-generated
content that can be re-used by brands.
This confirms Crowdsight expectations and underlines the value of its business offer of
post-sponsorship re-usable fan generated content for brands.
The questions Q5, Q6, Q7 and Q8 focus on understanding, for each Social Network analyzed,
the grade of interest in terms of comments, shares and likes.
Q5. Facebook Social Signals (for both Earned and Paid Media). Please rank the following by
order of interest (1 is least important, 5 is most):
There seems to be a medium-high importance placed to conversation and Applause Rate, while
Amplification Rate displays the highest importance.
# Question 1 2 3 4 5 Total2
Response
s
Mean
1 Conversat
ion,Rate,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
(#,
comment
s,per,
post)
4 4 26 22 10 66 3.45
2 Amplifica
tion,Rate,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
(#,Re>
tweets/S
hares,per,
post)
5 6 10 19 26 66 3.83
3 Applause,
Rate,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
(#,
favorites/
likes/+1,
per,post)
5 10 27 15 9 66 3.20
Statistic Conversation-Rate--------------------------------
(#-comments-per-post)
Amplification-Rate---------------------------------
(#-Re7tweets/Shares-per-
post)
Applause-Rate---------------------------------
(#-favorites/likes/+1-per-
post)
Min$Value 1 1 1
Max$Value 5 5 5
Mean 3.45 3.83 3.20
Variance 1.05 1.59 1.21
Standard$Deviation 1.03 1.26 1.10
Total$Responses 66 66 66

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This is understandable given that Amplification Rate measures that average number of shares per
content generated, which in turns defines the expected reach per content item. Shares appear to
be more important because they contribute in viralizing content.
Q6. Google+ Social Signals (for both Earned and Paid Media). Please rank the following by
Google+ displays lower means compared to Facebook (confirming a lower interest for this
Social Network). Amplification Rate is claimed to be the most important aspect for Google+ too.
Response
s
Mean
1 Conversat
ion,Rate,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
(#,
comment
s,per,
post)
15 7 25 15 3 65 2.75
2 Amplifica
tion,Rate,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
(#,Re=
tweets/S
hares,per,
post)
14 8 13 16 14 65 3.12
3 Applause,
Rate,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
(#,
favorites/
likes/+1,
per,post)
15 14 18 11 7 65 2.71
Statistic Conversation-Rate-------------------------------
post)
Applause-Rate---------------------------------
post)
Min$Value 1 1 1
Max$Value 5 5 5
Mean 2.75 3.12 2.71
Variance 1.41 2.11 1.68

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Q7. Twitter Social Signals (for both Earned and Paid Media). Please rank the following by order
of interest (1 is least important, 5 is most):
Results for Twitter are comparable to Facebook. Amplification Rate is preferred to other Second
Level Metrics. Means for Twitter answers seem to be very much in line with Facebook ones.
Response
s
Mean
1 Conversat
ion,Rate,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
(#,
comment
s,per,
post)
5 3 24 26 8 66 3.44
2 Amplifica
tion,Rate,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
(#,Re>
tweets/S
hares,per,
post)
6 5 11 15 29 66 3.85
3 Applause,
Rate,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
(#,
favorites/
likes/+1,
per,post)
10 5 22 22 7 66 3.17
post)
Applause-Rate---------------------------------
post)
Min$Value 1 1 1
Max$Value 5 5 5
Mean 3.44 3.85 3.17
Variance 1.05 1.73 1.43

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Q8. Instagram Social Signals (for both Earned and Paid Media). Please rank the following by
For Instagram, Amplification Rate confirms its leading importance. Means are in line with
Google+ ones.
Response
s
Mean
1 Conversat
ion,Rate,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
(#,
comment
s,per,
post)
13 12 21 13 4 63 2.73
2 Amplifica
tion,Rate,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
(#,Re=
tweets/S
hares,per,
post)
13 11 11 9 18 62 3.13
3 Applause,
Rate,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
(#,
favorites/
likes/+1,
per,post)
16 10 16 14 6 62 2.74
post)
Applause-Rate---------------------------------
post)
Min$Value 1 1 1
Max$Value 5 5 5
Mean 2.73 3.13 2.74
Variance 1.43 2.34 1.77

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Q9. Across the individual Social Networks, what is the expected amount of interactions from
fans per event? (Interactions: clicks, likes, +1, comments, shares, re-tweets, favourites):
Respondents expect an average of 357 interactions from Facebook, about 192 from Twitter,
approximately 57 from Instagram, 18 from Google+ and 8 from Snapchat.
It is interesting to note the great spread between minimum and maximum values. There seems to
be a great disparity of answers, probably due to the inherent differences in size of the companies
interviewed (which is confirmed by the average amount spent per digital activation per event as
in Q3).
Q10. What do you consider the average Euro value of a user interaction with your brand across
the following Social Networks?
Respondents give to Facebook the highest economic value per interaction (€23,93), followed by
Twitter (€19,08), while Google+ (€1,63), Instagram (€1,12) and Snapchat (€1,06) position
themselves at a way lower value per interaction.
Looking at minimum and maximum answers it is clear that also in this case respondents place a
very different value to interactions. This shows how subjective and open to interpretations can be
the economic value for Social Media interactions (sometimes valued at €0, as it was the case for
some respondents).
# Answer Min*Value Max*Value Average*Value Standard*
Deviation
1 Facebook 0.00 10,000.00 357.26 1,291.77
2 Google+ 0.00 600.00 18.04 81.77
3 Instagram 0.00 2,000.00 57.20 256.63
4 Snapchat 0.00 200.00 8.11 33.31
5 Twitter 0.00 5,000.00 191.75 682.39
# Answer Min*Value Max*Value Average*Value Standard*
Deviation
1 Facebook 0.00 1,600.00 23.93 181.48
2 Google+ 0.00 50.00 1.63 8.02
3 Instagram 0.00 50.00 1.12 5.91
4 Snapchat 0.00 50.00 1.06 5.84
5 Twitter 0.00 1,400.00 19.08 157.47

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Q11. Would you be interested in an overall metric, that aggregates and summarizes your
sponsorship performance online?
The 82% responded “yes”, confirming the expectations and signaling the strong need for
sponsorship and marketing managers for a better set of analytics tools to correctly assess
sponsorship performance in Social Media.
Additionally, a series of analysis has been conducted by crossing company characteristics (i.e.
size, industry, nationality) of respondents with survey results, in order to identify potential
significative relationship. (See APPENDIX VI for Survey Data Analysis).
Given the limited amount of respondents, companies have been divided in groups to facilitate the
analysis:
• Size
o 1-200 | Small companies
o 201-1000 | Medium companies
o 1001-10000+ | Big Companies
• Industry
o Products
o Services
• Nationality
o Ireland
o Ireland (Multinational)
# Answer Response %
1 Yes 51 82%
2 No 11 18%
Total 62 100%
Statistic Value
Min$Value 1
Max$Value 2
Mean 1.18
Variance 0.15
Standard$Deviation 0.39
Total$Responses 62

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An univariate ANOVA (Analysis of Variance) has bene performed, anwhich identified a
significative relationship (<0,05) between the variable “Nationality” and “Instagram”.
It seems that multinational companies (e.g. “Ireland (Multinational)”) give less importance to
Instagram if compared to Irish-only companies.
There are also few other relationships that happen to be very close to significativity:
• Multinational companies tend to give less importance to Facebook and they tend to give
more importance to Google+ than Irish-only companies.
• The level of interest in conversation rate for Twitter seems to be connected to nationality.
Irish-only companies seem to give more importance to Conversation Rate for Twitter, if
compared with multinational ones.
The above findings might bring a significative relationship if analyzed for a bigger sample than
the one investigated.
By crossing the remaining variables and by comparing means for independent samples, no other
significative relationship has been identified.

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6. IMPLEMENTATION & RECOMMENDATIONS
6.1 Overall Sponsorship Performance Metric
As previously mentioned, the research carried out through the questionnaire had the primary
objective of effectively assessing respondents’ grade of interest and preference in terms of Social
Media channels, when generating sponsorship efforts. Moreover, it sought to acquire specific
interest, as well as references, in order to build a useful aggregative performance metric.
At the end of the questionnaire respondents where asked to express their interest in a possible
aggregative metric which would rate their sponsorship performance on Social Media. As
previously mentioned, the great majority of respondents expressed their interest in such a metric.
Below I propose a way to construct the metric in question, along with an explanation of each
component that composes the metric.
Given the experimental and highly uncertain environment of research of Social Media, this
proposal does not aim to be a rigorous solution to Marketers’ needs. Rather, it aims to become a
valuable addition to their available tools, and help them better evaluate the online effect of
sponsorships.
The Overall Sponsorship Performance metric (in short OSP) needs 4 main elements to provide a
result:
• Client’s expected amount of interactions per Social Network
• Exact amount of interactions generated by type (COs | AMs | APs)
• Importance Grade of interaction types by Social (ICOs | IAMs | IAPs)
• Social Network Ranking Index of each Social Network (Rs)
Client’s expected amount of interactions per Social Network - On their first access to the
Crowdsight Analytics Dashboard, clients will be asked to insert the contest’s expected amount of
interactions for each of the targeted Social Networks, which helps generating a custom
benchmark (question similar to Q9 in the questionnaire – See APPENDIX II)
Exact amount of interactions generated by type (COs | AMs | APs) – Once the contest will be
running, Crowdsight will track each interaction and bucket it by type: Conversation,
Amplification, Applause. This will be done in real time by using the relevant Social Network
APIs.

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Importance Grade of interaction types by Social Network (ICOs | IAMs | IAPs) – These
indexes express the importance grade placed by respondents on each interaction type by Social
Network. They are based on the statistical means of the answers given to Q5-8 of the
questionnaire.
Taking the statistical mean for each interaction type and dividing each single mean by the overall
Q5-8 mean yields a measure of relative importance for each interaction type by Social Network
(i.e. ICOf for Conversations on Facebook etc.)
Below a table showing the Indexes identified:
Social Network Ranking Index of each Social Network (Rs) – These measures correspond to
the level of interest given by the questionnaire respondents to each Social Network. Note that the
means for Facebook and Twitter are high and very close (approximately 3,49), while for
Google+ and Instagram are low and very close too (approximately 2,86).
By taking the ratio of the two means (3,49 and 2,86) we get 1,22:
Importance Value
0,988
1,096
0,916
0,987
1,104
0,909
0,962
1,091
0,948
0,952
1,092
0,956
ICOf
IAMf
IAPf
ICOt
IAMt
IAPt
ICOg
IAMg
IAPg
ICOi
IAMi
IAPi
Q5) Facebook Q6) Google+ Q7) Twitter Q8) Instagram
Conversation 3,45 2,75 3,44 2,73
Amplification 3,83 3,12 3,85 3,13
Applause 3,20 2,71 3,17 2,74
Average 3,49 2,86 3,49 2,87
Avg. Q5-6 1,22 Avg. Q7-8 1,22

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Real interactions were then transformed into virtual interactions, with the aim of weighting each
interaction differently, depending on Social Network, while respecting the total number of
interactions.
A system of equations has been used to transform real interaction into virtual ones, grouping
Social Networks with a high average (Facebook & Twitter) into the variable “a”, and Social
Networks with a low average (Google+ & Instagram) into the variable “b”.
The system of equations has two conditions: Rankings need to have a mean of 1, but their ratio
needs to be 1,22:1.
The values resulting from the aforementioned system of equations are 1,1 and 0,9.
Finally, we can assign these values to each Social Network:
Using the information above, we define:
• Custom Benchmark (Bs)
• Social Network Performance (Ps)
Custom Benchmark (Bs) – Corresponds to client-specified contest target in terms of social
interaction counts. This is a vector where each component relates to a different Social Network
and represents the expected amount of social interactions generated by the sponsorship contest
through Crowdsight.
Social Network Performance (Ps) – This measure is a weighted count of interactions generated
(COs | AMs | APs) where the weights are defined by their Importance Grades (ICOs | IAMs |
IAPs), as well as by the relative Social Network Ranking Index (Rs). This yields, for each Social
Network, a measure aggregated over the performance categories.
Ranking Value
1,1
1,1
0,9
0,9
Rf!
Rt!
Rg!
Ri!

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Finally, the Social Network Performances (Ps) are divided by the Custom Benchmark (Bs) and
subsequently multiplied by 100 to generate the OSP relative to each Social Network.
The OSPs are then summed together to provide a global OSP for the entire campaign.
OSP formula:
where
Client’s expected amount of interactions per Social Network
The OSP represents the performance of the contest relative to the client-supplied benchmark in a
percentage scale:
• OSP = 100 | Complete alignment between expected delivered performance
• OSP < 100 | Delivered performance superior exceeding expectations
• OSP > 100 | Delivered performance inferior to expectations
OSP could be successfully applied by Crowdsight in its Analytics Dashboard to better inform
marketers and sponsorship managers about the overall progression of their campaigns.
6.2 Sponsorship Economic Value Identification
On their first access to the Crowdsight Analytics Dashboard, clients will be asked to insert the
Euro value of a single user interaction for each Social Network (question similar to Q10 in the
questionnaire – See APPENDIX II), which will be used for the Sponsorship Economic Value
Identification (SEVI).
The SEVI metric is calculated as follows:
SEVI Formula:

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where
Client’s expected Euro value per single interaction per Social Network
This metric is not influenced by potential wrong estimations made on the expect amount of
interactions. The SEVI metric can be a valuable tool to understand the economic value generated
by a sponsorship for each Social Network.
6.3 Crowdsight Dashboard Recommendations
Crowdsight should offer to clients a series of actionable analytic metrics, ready to be interpreted
and analyzed. OSP and SEVI should occupy a primary position in the dashboard real estate.
Where necessary, the Dashboard UI should use tooltips to further describe metrics and provide
hyperlinks to explanation pages, which would offer a more in-depth look at how these metrics
have been formed. Transparency
Finally, the Dashboard should offer the possibility to visualize First and Second Level metrics
that contributed to the formation of OSP and SEVI.

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7. CONCLUSION
7.1 Final Thoughts & Challenges Faced
The project gave me the opportunity to measure my skills with the difficult task to accurately
investigate and understand market needs in terms of sponsorship analytics. The ambitious plan of
transforming the insights received into actionable propositions and new service features, aiming
to produce a competitive advantage for Crowdsight in such a competitive industry as the
sponsorship one, has given me a solid opportunity to develop my skills both in terms of project
management and client relationship.
The identified results are of immediate applicability in the sponsorship environment but offer
also potential applications in other industries and scenarios that involve and understanding of
social signals and more broadly an investigation into Social Media engagement performance.
Although the new metrics were built taking into consideration preferences and interests proper of
the sponsorship environment, the same approach in terms of market research and mathematical
calculations could be easily applied to other industries in order to effectively assess social
interactions.
While working on the project a series of obstacles arose, which gave me the opportunity to learn
from mistakes made during the preparation phase of the project. My research timeline didn’t take
into consideration the time of the year. The survey was sent out on June 30th
to the identified
sample and I received a number automatic emails from potential respondents saying that they
were on annual leave. This issue most probably led to less survey completions.
Additionally, the working process faced some challenges due to the limited availability of
Crowdsight founder and the occasional lack of a common understanding in terms of research
methodology.
7.2 Work Process
I followed almost weekly meetings at Crowdsight office in order to fully understand the working
environment and the company’s internal strategies; this helped me a lot when interacting with
industry experts in order and earn survey responses.

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Additionally, a series of tools have been used in order to carry the research. Evernote has been
used in the first phase of ideas gathering, primarily to write down notes after each meeting and to
list potential project ideas.
Subsequently, Trello has been used in order to plan both tasks necessary to complete the
research and to implement the identified findings to generate new analytic metrics.
Google Drive has been used as work repository, mainly to draft and display progresses both in
terms of research and strategic plan.
Overall, the work was carried out with a solid pace and has given me the opportunity to better
measure myself with a practical marketing problem and real-life issues that invariably arise
when producing a marketing strategy.
7.3 Project Constraints
The project identified interesting new approaches to analytics for sponsorship in live-events.
Despite that, the research was carried out analyzing results from a limited representative sample
(approximately 70 respondents), thus making project findings and recommendations
experimental. The empirical application of recommendations will constitute a crucial step
towards the proposed models verification and its market appeal.

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8. REFERENCES
Bradlow, E. T., & Ennen, S. (2010). Social Media Myths & Misconceptions (pp. 33). Retrieved
from http://www.slideshare.net/wimisteve/prof-eric-bradlow-steve-ennen-oms-keynote-2010-
final
Calbreath, D. (2010, February 25, 2010). Evaluating the value of Social Media, The San Diego
UnionTribune.
Retrieved from http://www.utsandiego.com/news/2010/feb/25/evaluating-the-value-ofsocial-
media/
Donston-Miller, D. (2012). 5 Social Media Metrics That Matter Now. Informationweek - Online.
doi: 2626348871
Ennen, S. (2010). Measuring Success of Social Media.
http://www.slideshare.net/wimisteve/ennenwhartonoms2010
Facebook, I. (2013). Facebook Reports Fourth Quarter and Full Year 2012 Results. MENLO
PARK, California, U.S.
Gilfoil, D. M., & Jobs, C. (2012). Return on Investment For Social Media: A Proposed
Framework For Understanding, Implementing, And Measuring The Return. Journal of Business
& Economics
Research, 10(11), 637-650.
Heilbrunn, B., Herzig, P., & Schill, A. Towards Gamification Analytics-Requirements for
Monitoring and Adapting Gamification Designs.
Hoffman, D. L., & Fodor, M. (2010). Can You Measure the ROI of Your Social Media
Marketing? MIT Sloan Management Review, 52(1), 41-49.
Jacucci, G., Oulasvirta, A., Salovaara, A., & Sarvas, R. (2005, November). Supporting the
shared experience of spectators through mobile group media. InProceedings of the 2005
international ACM SIGGROUP conference on Supporting group work (pp. 207-216). ACM.

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Interactive Advertising Bureau. (2009). Social Media Ad Metrics Definitions. New York, U.S.:
Interactive Advertising Bureau,.
Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and
opportunities of Social Media. Business Horizons, 53(1), 59-68. doi:
10.1016/j.bushor.2009.09.003
Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011). Social Media? Get
serious! Understanding the functional building blocks of Social Media. Business Horizons,
54(3), 241-251. doi:
http://dx.doi.org/10.1016/j.bushor.2011.01.005
Kim, Y. H., Kim, D. J., & Wachter, K. (2013). A study of mobile user engagement (MoEN):
Engagement motivations, perceived value, satisfaction, and continued engagement intention.
Decision Support Systems, 56, 361-370.
Madison, I. (2012). Why Your Social Media Metrics Are a Waste of Time. Retrieved from
http://blogs.hbr.org/cs/2012/12/why_your_social_media_metrics.html
Mauboussin, M. J. (2012). THE TRUE MEASURES OF SUCCESS. Harvard Business Review,
90(10), 46-56.
Mcnamara, J. (2011). Social Media Strategy and Governance: Gaps, risks and opportunities (A.
a. S. Sciences, Trans.) Research reports. Sydney, Australia: University of Technology Sydney.
Media, C. and Media, C. (2014). 2014 Is The Year of Digital Activation for Media Companies -
Cynopsis Media. [online] Cynopsis Media. Available at: http://cynopsis.com/cyncity/2014-is-
the-year-of-digital-activation-for-media-companies/ [Accessed 11 Jul. 2015].
Narayanan, M., Asur, S., Nair, A., Rao, S., Kaushik, A., Mehta, D., . . . Lalwani, R.
(2012). Social Media and Business. Vikalpa: The Journal for Decision Makers, 37(4), 69-111.

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Oursocialtimes.com, (2015). Using Social Media for events [infographic] | Our Social Times -
Social Media Agency, Social Media Training. [online] Available at:
http://oursocialtimes.com/using-social-media-to-make-your-event-a-dazzling-success-
infographic/ [Accessed 11 Jul. 2015].
Pew Internet & American Life Project. (2012). Two-thirds of young adults and those with higher
income are smartphone owners. In L. Rainie (Ed.), Smartphone Ownership Update (2012 ed.).
Washington DC, U.S.: Pew Research Center.
Qualman, E. (2011). Socialnomics : How Social Media Transforms the Way We Live and Do
Business. Retrieved from
http://library.books24x7.com.ezp01.library.qut.edu.au/toc.aspx?site=BPNPJ&bookid=40816
Springs-Kelley, K. (2014). How to Leverage Live Marketing With Social Media Before, During
and After Events. [online] Entrepreneur. Available at:
http://www.entrepreneur.com/article/238186 [Accessed 11 Jul. 2015].

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9. APPENDICES
APPENDIX I – Competitors’ Analytics Dashboard
Livecube Analytics Dashboard:
QuickMobile Analytics Dashboard:
EventMobi Analytics Dahsboard

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Doubledutch Analytics Dashboard:
Fish Analytics Dashboard:

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Bizzabo Analytics Dashboard:

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APPENDIX II – Survey
ANALYTICS METRICS FOR LIVE-EVENTS ONLINE SPONSORSHIP
ACTIVATION
This survey is part of a research project being conducted for the MSc Digital Marketing
programme at UCD Michael Smurfit Graduate Business School (Dublin, Ireland).
The survey aims to understand the growing relevance of digital activation to sponsorship
decision-makers that are involved in sponsorship of live events.
By collating the input and expertise across the industry, it will be possible to compile a report on
the Social Media analytics that most effectively measure success across the industry.
All respondents will have this report made available to them as soon as the results are compiled
and published (Q3 – 2015).
The questions in this survey will take approximately 5 minutes to complete. The data received
from your participation will be strictly confidential and will only be used for the purpose of this
project. If you have any question regarding the survey, please feel free to contact the researcher.
Thank you for participating in this questionnaire and for your valued input.
Researcher: Matteo Balzarini
E-mail: matteo.balzarini@ucdconnect.ie
UCD Michael Smurfit Graduate Business School
Carysfort Avenue, Blackrock,
Co. Dublin, Ireland

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Q1 When activating your sponsorship using Paid Channels, which Social Networks are most
important?
Please rank the following by order of importance (1 is most important, 5 is least):
______ Facebook (1)
______ Google+ (2)
______ Instagram (3)
______ Snapchat (4)
______ Twitter (5)
Q2 With respect to sponsorship activation, which Social Networks are preferred to generate
Earned Media?
Please rank the following by order of importance (1 is most important, 5 is least):
______ Facebook (1)
______ Google+ (2)
______ Snapchat (4)
______ Twitter (5)
Q3 As an estimate, how much would you spend to digitally activate your sponsorships per
event?
! €0-€10K (1)
! €11K-€50K (2)
! €51K-€100K (3)
! €101K+ (4)
Q4 Please answer the following questions related to sponsorships' fan generated content:
Not at all (1) Somewhat (2) Very (3)
How useful do you
consider fan generated
content for sponsorship
activation efforts (1)
! ! !
How difficult is it to
find fan generated
content that you can use
with permission (2)
! ! !

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Q5 Facebook Social Signals (for both Earned and Paid Media) Please rank the following by
Your grade of interest
1 (1) 2 (2) 3 (3) 4 (4) 5 (5)
Conversation
Rate
(# comments per
post) (1)
! ! ! ! !
Amplification
Rate
(# Re-
tweets/Shares
per post) (2)
! ! ! ! !
Applause Rate
(#
favorites/likes/+1
per post) (3)
! ! ! ! !
Q6 Google+ Social Signals (for both Earned and Paid Media) Please rank the following by order
1 (1) 2 (2) 3 (3) 4 (4) 5 (5)
Conversation
Rate
(# comments per
post) (1)
! ! ! ! !
Amplification
Rate
(# Re-
tweets/Shares
per post) (2)
! ! ! ! !
Applause Rate
(#
favorites/likes/+1
per post) (3)
! ! ! ! !

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! !
Q7 Twitter Social Signals (for both Earned and Paid Media) Please rank the following by order
1 (1) 2 (2) 3 (3) 4 (4) 5 (5)
Conversation
Rate
(# comments per
post) (1)
! ! ! ! !
Amplification
Rate
(# Re-
tweets/Shares
per post) (2)
! ! ! ! !
Applause Rate
(#
favorites/likes/+1
per post) (3)
! ! ! ! !
Q8 Instagram Social Signals (for both Earned and Paid Media) Please rank the following by
1 (1) 2 (2) 3 (3) 4 (4) 5 (5)
Conversation
Rate
(# comments per
post) (1)
! ! ! ! !
Amplification
Rate
(# Re-
tweets/Shares
per post) (2)
! ! ! ! !
Applause Rate
(#
favorites/likes/+1
per post) (3)
! ! ! ! !
Q9 Across the individual Social Networks, what is the expected amount of interactions from fans
per event? (Interactions: clicks, likes, +1, comments, shares, re-tweets, favorites)
______ Facebook (1)
______ Google+ (2)
______ Snapchat (4)
______ Twitter (5)

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Q10 What do you consider the average Euro value of a user interaction with your brand across
the following Social Networks?
______ Facebook (1)
______ Google+ (2)
______ Snapchat (4)
______ Twitter (5)
Q11 Would you be interested in an overall metric, that aggregates and summarizes your
sponsorship performance online?
! Yes (1)
! No (2)

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APPENDIX III – First Level Metrics
Single per$item
Total in$contest
Average per$participant
% per$picture$(or$brand$mention)
LegendInternal Measures
Participants Total
Engagement0Time Total,'Average
Pictures Total,'Average
Picture0Shares Total,'Average,'%
Picture0Comments Total,'Average,'%
Picture0Impressions Total,'Average,'%
Picture0Clicks Total,'Average,'%
Picture0Likes Total,'Average,'%
Average0Session0Duration not'for'sponsor
Pages/Session not'for'sponsor
Age Total,'Average
Gender Total,'Average
Twitter Measures
Picture0Tweets Total,'Average,'%
Picture0Retweets Total,'Average,'%
Picture0Favorites Total,'Average,'%
Picture0Replies Total,'Average,'%
Brand0Tweets Total,'Average,'%
Brand0Retweets Total,'Average,'%
Brand0Favorites Total,'Average,'%
Brand0Replies Total,'Average,'%
Potential0Reach Total
Profile0Followers Total
Audience0Growth Total
Hashtag0usages Total,'Average,'%
Facebook Measures
Page0Likes Total
Potential0Reach Total
Instagram Measures
Hashtag0usages Total,'Average,'%
Google+ Measures
Pageviews Total,'%
+1 Total,'Average,'%

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APPENDIX IV – Email for Survey Distribution
Hi (Respondent’s First Name),
I came across your profile online and thought you would be an ideal candidate to reach out to.
I'm currently a student on the MSc Digital Marketing programme at UCD Michael
Smurfit Graduate Business School (Dublin, Ireland) and am completing my final research
project on the impact of Social Media and digital advertising on sponsorship activation.
Given your background and expertise, I wanted to personally invite you to participate in a
survey that aims to better understand the growing relevance of digital activation to sponsorship
decision-makers - with a focus on the Social Media analytics that define success.
The survey contains only 11 questions and will take approximately 5 minutes to complete.
By collating the input and expertise across the industry, it will be possible to compile a report on
the Social Media analytics that most effectively measure success across the industry. I will make
this report available to all respondents as soon as the results are compiled and published (Q3 –
2015).
Your participation in the survey is completely voluntary and all of your responses will be kept
confidential.
Follow this link to the Survey: Take the Survey
Or copy and paste the URL below into your internet browser:
https://ucdbusiness.eu.qualtrics.com/SE?Q_DL={UniqueID}
Thank you for participating in this questionnaire and if you have any questions at all, please do
contact me.
Sincerely,
Matteo Balzarini
MSc Digital Marketing, UCD Michael Smurfit Graduate Business School
Carysfort Avenue, Blackrock, Co. Dublin, Ireland
Follow the link to opt out of future emails: Click here to unsubscribe

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APPENDIX V – Survey Results Matrix
Variable Variable(label Scale Value(labels
Company
Company(Size compsize ordinal 1:"11200",52:"20111000",53:"oltre51000"
Company(Industry compindu nominal 1:"Products",52:"Services"
Nationality national nominal5 1:"Irland",52:"Multinational"
When(activating(your(sponsorship(using(Paid(Channels,(which(social(networks(are(most(important?(
Please(rank(the(following(by(order(of(importance((1(is(most(important,(5(is(least)(F(Facebook
q1f ordinal 1:5most5important5155:5least5important
Please(rank(the(following(by(order(of(importance((1(is(most(important,(5(is(least)(F(Google+
q1g ordinal 1:5most5important5155:5least5important
Please(rank(the(following(by(order(of(importance((1(is(most(important,(5(is(least)(F(Instagram
q1i ordinal 1:5most5important5155:5least5important
Please(rank(the(following(by(order(of(importance((1(is(most(important,(5(is(least)(F(Snapchat
q1s ordinal 1:5most5important5155:5least5important
Please(rank(the(following(by(order(of(importance((1(is(most(important,(5(is(least)(F(Twitter
q1t ordinal 1:5most5important5155:5least5important
With(respect(to(sponsorship(activation,(which(social(networks(are(preferred(to(generate(Earned(
Media?(Please(rank(the(following(by(order(of(importance((1(is(most(important,(5(is(least)(F(
Facebook
q2f ordinal 1:5most5important5155:5least5important
Media?(Please(rank(the(following(by(order(of(importance((1(is(most(important,(5(is(least)(F(Google+
q2g ordinal 1:5most5important5155:5least5important
Instagram
q2i ordinal 1:5most5important5155:5least5important
Snapchat
q2s ordinal 1:5most5important5155:5least5important
Media?(Please(rank(the(following(by(order(of(importance((1(is(most(important,(5(is(least)(F(Twitter
q2t ordinal 1:5most5important5155:5least5important
As(an(estimate,(how(much(would(you(spend(to(digitally(activate(your(sponsorships(per(event?( q3 ordinal 1:"0110K",52:"10150K",53:"501100",54:"100K5or5more"
Please(answer(the(following(questions(related(to(Sponsorhips'(fan(generated(content:(F(How(useful(
do(you(consider(fan(generated(content(for(Sponsorship(activation(efforts
q4a ordinal 1:"Not5at5all",52:"Somewhat",53:"Very"
Please(answer(the(following(questions(related(to(Sponsorhips'(fan(generated(content:(F(How(
difficult(is(it(to(find(fan(generated(content(that(you(can(use(with(permission
q4b ordinal 1:"Not5at5all",52:"Somewhat",53:"Very"
Facebook(Social(Signals((for(both(Earned(and(Paid(Media)(Please(rank(the(following(by(order(of(
interest((1(is(least(important,(5(is(most):(Your(grade(of(interest(F(Conversation(Rate((#(comments(
per(post)
q5fco ordinal 1:5least5important5155:5most5important
interest((1(is(least(important,(5(is(most):(Your(grade(of(interest(F(Amplification(Rate((#(ReF
tweets/Shares(per(post)
q5fam ordinal 1:5least5important5155:5most5important
interest((1(is(least(important,(5(is(most):(Your(grade(of(interest(F(Applause(Rate((#(
favorites/likes/+1(per(post)
q5fap ordinal 1:5least5important5155:5most5important
Google+(Social(Signals((for(both(Earned(and(Paid(Media)(Please(rank(the(following(by(order(of(
per(post)
q6gco ordinal 1:5least5important5155:5most5important
q6gam ordinal 1:5least5important5155:5most5important
q6gap ordinal 1:5least5important5155:5most5important
Twitter(Social(Signals((for(both(Earned(and(Paid(Media)(Please(rank(the(following(by(order(of(
per(post)
q7tco ordinal 1:5least5important5155:5most5important
q7tam ordinal 1:5least5important5155:5most5important
q7tap ordinal 1:5least5important5155:5most5important
Instagram(Social(Signals((for(both(Earned(and(Paid(Media)(Please(rank(the(following(by(order(of(
per(post)
q8ico ordinal 1:5least5important5155:5most5important
q8iam ordinal 1:5least5important5155:5most5important
q8iap ordinal 1:5least5important5155:5most5important
Across(the(individual(social(networks,(what(is(the(expected(amount(of(interactions(from(fans(per(
event?((Interactions:(clicks,(likes,(+1,(comments,(shares,(reFtweets,(favourites)(F(Facebook
q9f quantitative5(equivalence5ratio)
event?((Interactions:(clicks,(likes,(+1,(comments,(shares,(reFtweets,(favourites)(F(Google+
q9g quantitative5(equivalence5ratio)
event?((Interactions:(clicks,(likes,(+1,(comments,(shares,(reFtweets,(favourites)(F(Instagram
q9i quantitative5(equivalence5ratio)
event?((Interactions:(clicks,(likes,(+1,(comments,(shares,(reFtweets,(favourites)(F(Snapchat
q9s quantitative5(equivalence5ratio)
event?((Interactions:(clicks,(likes,(+1,(comments,(shares,(reFtweets,(favourites)(F(Twitter
q9t quantitative5(equivalence5ratio)
What(do(you(consider(the(average(Euro(value(of(a(user(interaction(with(your(brand(across(the(
following(social(networks(F(Facebook
q10f quantitative5(equivalence5ratio)
following(social(networks(F(Google+
q10g quantitative5(equivalence5ratio)
following(social(networks(F(Instagram
q10i quantitative5(equivalence5ratio)
following(social(networks(F(Snapchat
q10s quantitative5(equivalence5ratio)
following(social(networks(F(Twitter
q10t quantitative5(equivalence5ratio)
Would(you(be(interested(in(an(overall(metric,(that(aggregates(and(summarizes(your(sponsorship(
performance(online?
q11 nominal 1:"Yes",52:"No"

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APPENDIX VI – Survey Data Analysis
Company Size (compsize) - Descriptive
N Mean
Std.
Deviation
Std.
Error
95%
Confidence
Interval for
Mean
Minim
um
Maxim
um
q1f 1 20 1,20 ,410 ,092 1,01 1,39 1 2
2 10 1,40 ,699 ,221 ,90 1,90 1 3
3 28 1,54 ,838 ,158 1,21 1,86 1 5
Total 58 1,40 ,699 ,092 1,21 1,58 1 5
q1g 1 20 3,60 1,314 ,294 2,99 4,21 1 5
2 10 3,50 1,080 ,342 2,73 4,27 1 5
3 28 3,18 ,983 ,186 2,80 3,56 1 5
Total 58 3,38 1,121 ,147 3,08 3,67 1 5
q1i 1 20 3,40 ,681 ,152 3,08 3,72 2 4
2 10 3,50 ,850 ,269 2,89 4,11 2 5
3 28 3,64 ,621 ,117 3,40 3,88 2 5
Total 58 3,53 ,681 ,089 3,36 3,71 2 5
q1s 1 20 4,65 ,489 ,109 4,42 4,88 4 5
2 10 4,70 ,675 ,213 4,22 5,18 3 5
3 28 4,57 ,790 ,149 4,27 4,88 2 5
Total 58 4,62 ,671 ,088 4,44 4,80 2 5
q1t 1 20 2,15 ,587 ,131 1,88 2,42 1 3
2 10 1,90 ,568 ,180 1,49 2,31 1 3
3 28 2,07 1,245 ,235 1,59 2,55 1 5
Total 58 2,07 ,953 ,125 1,82 2,32 1 5
q2f 1 22 1,32 ,477 ,102 1,11 1,53 1 2
2 10 1,20 ,632 ,200 ,75 1,65 1 3
3 29 1,59 ,682 ,127 1,33 1,85 1 4
Total 61 1,43 ,618 ,079 1,27 1,58 1 4
q2g 1 22 3,18 1,140 ,243 2,68 3,69 1 5
2 10 3,20 1,229 ,389 2,32 4,08 1 5
3 29 3,07 1,132 ,210 2,64 3,50 1 5
Total 61 3,13 1,132 ,145 2,84 3,42 1 5
q2i 1 22 3,41 ,590 ,126 3,15 3,67 2 4
2 10 3,50 ,707 ,224 2,99 4,01 2 4
3 29 3,79 ,620 ,115 3,56 4,03 3 5
Total 61 3,61 ,640 ,082 3,44 3,77 2 5
q2s 1 22 4,73 ,456 ,097 4,53 4,93 4 5
2 10 4,70 ,483 ,153 4,35 5,05 4 5
3 29 4,55 ,686 ,127 4,29 4,81 3 5
Total 61 4,64 ,578 ,074 4,49 4,79 3 5
q2t 1 22 2,36 1,293 ,276 1,79 2,94 1 5
2 10 2,40 ,966 ,306 1,71 3,09 2 5
3 29 2,00 1,225 ,227 1,53 2,47 1 5
Total 61 2,20 1,209 ,155 1,89 2,51 1 5
q3estimat
e
1
21 10,9524 10,91089
2,3809
5
5,9858
15,919
0
5,00 30,00
2
13 17,3077 37,50641
10,402
41
-5,3572
39,972
6
5,00 140,00

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! !
3
30 23,8333 35,39636
6,4624
6
10,6161
37,050
6
5,00 140,00
Total
64 18,2813 30,25315
3,7816
4
10,7242
25,838
3
5,00 140,00
q5fco 1 21 3,67 ,796 ,174 3,30 4,03 2 5
2 13 3,31 1,032 ,286 2,68 3,93 1 5
3 30 3,37 1,189 ,217 2,92 3,81 1 5
Total 64 3,45 1,038 ,130 3,19 3,71 1 5
q5fam 1 21 3,90 1,261 ,275 3,33 4,48 1 5
2 13 3,77 1,092 ,303 3,11 4,43 2 5
3 30 3,80 1,375 ,251 3,29 4,31 1 5
Total 64 3,83 1,267 ,158 3,51 4,14 1 5
q5fap 1 21 2,95 ,865 ,189 2,56 3,35 2 5
2 13 3,31 ,855 ,237 2,79 3,82 2 5
3 30 3,33 1,348 ,246 2,83 3,84 1 5
Total 64 3,20 1,115 ,139 2,92 3,48 1 5
q6gco 1 21 2,90 1,261 ,275 2,33 3,48 1 5
2 13 2,92 1,115 ,309 2,25 3,60 1 5
3 29 2,62 1,178 ,219 2,17 3,07 1 5
Total 63 2,78 1,184 ,149 2,48 3,08 1 5
q6gam 1 21 2,90 1,546 ,337 2,20 3,61 1 5
2 13 3,31 1,109 ,308 2,64 3,98 1 5
3 29 3,28 1,533 ,285 2,69 3,86 1 5
Total 63 3,16 1,450 ,183 2,79 3,52 1 5
q6gap 1 21 2,43 1,165 ,254 1,90 2,96 1 5
2 13 3,00 1,000 ,277 2,40 3,60 1 5
3 29 2,83 1,490 ,277 2,26 3,39 1 5
Total 63 2,73 1,298 ,163 2,40 3,06 1 5
q7tco 1 21 3,67 ,856 ,187 3,28 4,06 2 5
2 13 3,31 ,947 ,263 2,74 3,88 1 5
3 30 3,37 1,189 ,217 2,92 3,81 1 5
Total 64 3,45 1,038 ,130 3,19 3,71 1 5
q7tam 1 21 3,67 1,317 ,287 3,07 4,27 1 5
2 13 3,85 1,144 ,317 3,16 4,54 2 5
3 30 3,97 1,426 ,260 3,43 4,50 1 5
Total 64 3,84 1,324 ,166 3,51 4,17 1 5
q7tap 1 21 3,24 1,221 ,266 2,68 3,79 1 5
2 13 3,15 ,899 ,249 2,61 3,70 1 4
3 30 3,20 1,297 ,237 2,72 3,68 1 5
Total 64 3,20 1,184 ,148 2,91 3,50 1 5
q8ico 1 21 2,86 1,424 ,311 2,21 3,51 1 5
2 12 2,42 1,165 ,336 1,68 3,16 1 4
3 28 2,71 1,049 ,198 2,31 3,12 1 5
Total 61 2,70 1,202 ,154 2,40 3,01 1 5
q8iam 1 20 2,90 1,744 ,390 2,08 3,72 1 5
2 12 3,00 1,595 ,461 1,99 4,01 1 5
3 28 3,29 1,384 ,262 2,75 3,82 1 5
Total 60 3,10 1,537 ,198 2,70 3,50 1 5
q8iap 1 20 2,65 1,424 ,319 1,98 3,32 1 5
2 12 2,67 1,155 ,333 1,93 3,40 1 4
3 28 2,86 1,407 ,266 2,31 3,40 1 5
Total 60 2,75 1,348 ,174 2,40 3,10 1 5

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! !
! !
q9f 1 21 132,67 238,885 52,129 23,93 241,41 0 1000
2
14 462,93 1331,949
355,97
8
-306,12
1231,9
7
0 5000
3
31 588,45 1810,498
325,17
5
-75,64
1252,5
5
0 10000
Total
66 416,80 1387,749
170,82
0
75,65 757,95 0 10000
q9g 1 22 7,68 15,264 3,254 ,91 14,45 0 50
2 14 ,43 1,342 ,359 -,35 1,20 0 5
3 31 39,74 127,147 22,836 -6,90 86,38 0 600
Total 67 21,00 87,959 10,746 -,45 42,45 0 600
q9i 1 23 32,52 85,256 17,777 -4,35 69,39 0 400
2 14 ,21 ,579 ,155 -,12 ,55 0 2
3 31 121,55 398,047 71,491 -24,46 267,55 0 2000
Total 68 66,46 275,766 33,442 -,29 133,21 0 2000
q9s 1 23 6,39 18,754 3,910 -1,72 14,50 0 76
2 14 ,43 1,342 ,359 -,35 1,20 0 5
3 31 15,74 50,117 9,001 -2,64 34,13 0 200
Total 68 9,43 35,763 4,337 ,77 18,08 0 200
q9t 1 23 52,78 118,114 24,628 1,71 103,86 0 500
2 14 68,71 158,191 42,278 -22,62 160,05 0 600
3
31 418,45 1049,375
188,47
3
33,54 803,37 0 5000
Total 68 222,76 731,506 88,708 45,70 399,83 0 5000
Company Size (compsize) - ANOVA
Sum of
Squares df
Mean
Square F Sig.
q1f Between
Groups
1,315 2 ,658 1,361 ,265
Within Groups 26,564 55 ,483
Total 27,879 57
q1g Between
Groups
2,248 2 1,124 ,891 ,416
Within Groups 69,407 55 1,262
Total 71,655 57
q1i Between
Groups
,702 2 ,351 ,751 ,477
Total 26,431 57
q1s Between
Groups
,148 2 ,074 ,160 ,853
Total 25,655 57
q1t Between
Groups
,417 2 ,208 ,224 ,800
Total 51,724 57
q2f Between
Groups
1,511 2 ,755 2,047 ,138

! 62!!
! !
! !
Total 22,918 60
q2g Between
Groups
,216 2 ,108 ,082 ,922
Total 76,951 60
q2i Between
Groups
1,981 2 ,990 2,544 ,087
Total 24,557 60
q2s Between
Groups
,430 2 ,215 ,634 ,534
Total 20,066 60
q2t Between
Groups
2,148 2 1,074 ,729 ,487
Total 87,639 60
q3estimat
e
Between
Groups
2065,049 2 1032,525 1,133 ,329
Within Groups 55595,88
8
61 911,408
Total 57660,93
7
63
q5fco Between
Groups
1,457 2 ,728 ,669 ,516
Total 67,859 63
q5fam Between
Groups
,192 2 ,096 ,058 ,944
Total 101,109 63
q5fap Between
Groups
1,971 2 ,986 ,787 ,460
Total 78,359 63
q6gco Between
Groups
1,329 2 ,664 ,466 ,630
Total 86,889 62
q6gam Between
Groups
2,041 2 1,020 ,477 ,623
Total 130,413 62
q6gap Between
Groups
3,132 2 1,566 ,928 ,401
Total 104,413 62
q7tco Between
Groups
1,457 2 ,728 ,669 ,516
Total 67,859 63
q7tam Between 1,112 2 ,556 ,310 ,734

! 63!!
! !
! !
Groups
Total 110,438 63
q7tap Between
Groups
,058 2 ,029 ,020 ,980
Total 88,359 63
q8ico Between
Groups
1,486 2 ,743 ,506 ,606
Total 86,689 60
q8iam Between
Groups
1,886 2 ,943 ,391 ,678
Total 139,400 59
q8iap Between
Groups
,605 2 ,302 ,162 ,851
Total 107,250 59
q9f Between
Groups
2638549,
167
2
1319274,58
3
,678 ,511
Within Groups 1225415
47,273
63
1945103,92
5
Total 1251800
96,439
65
q9g Between
Groups
20715,86
3
2 10357,932 1,353 ,266
37
64 7654,815
Total 510624,0
00
66
q9i Between
Groups
182007,0
94
2 91003,547 1,204 ,307
Within Groups 4913151,
774
65 75586,950
Total 5095158,
868
67
q9s Between
Groups
2581,790 2 1290,895 1,010 ,370
2
65 1278,659
Total 85694,63
2
67
q9t Between
Groups
2183895,
788
2
1091947,89
4
2,108 ,130
Within Groups 3366789
4,448
65 517967,607
Total 3585179
0,235
67

! 64!!
! !
! !
Company Size (compsize) - Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
compsize *
q11
60 85,7% 10 14,3% 70 100,0%
Company Size (compsize) - compsize * q11 Crosstabulation
q11 Total
1 2 1
compsize 1 Count 17 3 20
Expected Count 16,7 3,3 20,0
% within
compsize
85,0% 15,0% 100,0%
% within q11 34,0% 30,0% 33,3%
2 Count 12 1 13
% within
compsize
92,3% 7,7% 100,0%
% within q11 24,0% 10,0% 21,7%
3 Count 21 6 27
% within
compsize
77,8% 22,2% 100,0%
% within q11 42,0% 60,0% 45,0%
Total Count 50 10 60
% within
compsize
83,3% 16,7% 100,0%
% within q11 100,0% 100,0% 100,0%
Company Size (compsize) - Chi-Square Tests
Value df
Asymp.
Sig. (2-
sided)
Monte Carlo Sig. (2-
sided)
Monte Carlo Sig. (1-
sided)
Pearson Chi-
Square
1,394(a) 2 ,498 ,507(b) ,494 ,520
Likelihood Ratio 1,504 2 ,471 ,507(b) ,494 ,520
Fisher's Exact
Test
1,198 ,677(b) ,665 ,689
Linear-by-Linear
Association
,515(c) 1 ,473 ,569(b) ,557 ,582 ,311(b) ,299 ,323
N of Valid Cases 60
a 3 cells (50,0%) have expected count less than 5. The minimum expected count is 2,17.
b Based on 10000 sampled tables with starting seed 2000000.
c The standardized statistic is ,718.

! 65!!
! !
! !
Company Industry (compindu) – Group Statistics
compind
u N Mean
Std.
Deviation
Std.
Error
Mean
q1f 1 11 1,64 1,206 ,364
2 47 1,34 ,522 ,076
q1g 1 11 3,27 ,905 ,273
2 47 3,40 1,173 ,171
q1i 1 11 3,45 ,688 ,207
2 47 3,55 ,686 ,100
q1s 1 11 4,73 ,905 ,273
2 47 4,60 ,614 ,090
q1t 1 11 1,91 ,701 ,211
2 47 2,11 1,005 ,147
q2f 1 11 1,55 ,934 ,282
2 50 1,40 ,535 ,076
q2g 1 11 3,00 1,183 ,357
2 50 3,16 1,131 ,160
q2i 1 11 3,73 ,786 ,237
2 50 3,58 ,609 ,086
q2s 1 11 4,55 ,688 ,207
2 50 4,66 ,557 ,079
q2t 1 11 2,18 1,168 ,352
2 50 2,20 1,229 ,174
q3estimate 1 13 25,0000 36,68560 10,17476
2 51 16,5686 28,55679 3,99875
q5fco 1 13 3,38 1,044 ,290
2 51 3,47 1,046 ,146
q5fam 1 13 3,77 1,301 ,361
2 51 3,84 1,271 ,178
q5fap 1 13 3,54 1,330 ,369
2 51 3,12 1,052 ,147
q6gco 1 13 2,69 1,109 ,308
2 50 2,80 1,212 ,171
q6gam 1 13 3,23 1,536 ,426
2 50 3,14 1,443 ,204
q6gap 1 13 3,00 1,472 ,408
2 50 2,66 1,255 ,178
q7tco 1 13 3,15 1,068 ,296
2 51 3,53 1,027 ,144
q7tam 1 13 3,77 1,589 ,441
2 51 3,86 1,265 ,177
q7tap 1 13 3,08 1,256 ,348
2 51 3,24 1,176 ,165
q8ico 1 13 2,62 ,961 ,266
2 48 2,73 1,267 ,183
q8iam 1 13 3,15 1,676 ,465
2 47 3,09 1,516 ,221
q8iap 1 13 2,62 1,325 ,368
2 47 2,79 1,366 ,199
q9f 1 14 577,14 1317,780 352,192
2 52 373,63 1415,240 196,259

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