Department of Telecommunications

BACHELOR’S THESIS Nr. 3695

ANALYSIS OF SOCIAL MEDIA MARKETING KEY PERFORMANCE

INDICATORS

Marko Sršan

Zagreb, July 2014

Department of Telecommunications

Contents

¨ Introduction ¨ Key Performance Indicators (KPI) definition¨ Data gathering¨ English Premier League (EPL) model¨ Analysis

Hypothesis Statistical Analysis Correlation Analysis Regression Analysis

¨ Conclusion

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Introduction

¨ Brands switch from traditional marketing to social media marketing (SMM)

¨ SMM is still young, therefore it is needed to find ways to improve its KPIs

¨ Analysis will be conducted on the leading English Premier League football brands

¨ Motivation – find out how much real life events contribute to the SMM KPIs

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Key Performance Indicators definition

¨ Key Performance Indicators Social Media Marketing – Facebook

Brand’s reach (daily and weekly) Brand’s growth rate (daily and weekly) Brand’s fan engagement (daily and weekly) Brand’s popularity (daily and weekly)

Search Marketing – Google Search Index Internet presence (weekly) Internet trend (weekly)

KPIs are set from Sunday to Sunday

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Data gathering

¨ Time period – July 1st 2012 to July 1st 2013

¨ EPL brands – Manchester United, Chelsea, Liverpool, Arsenal, Manchester City

¨ Social media marketing Facebook fan pages – Socialnumbers.com (link)

¨ Search marketing Google Search Index – Google Trends (link)

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English Premier League model

¨ EPL model Significant events (games, transfers, news, etc.) for each EPL

brand shown over time Significant events attached to data for further analysis Eventually, events are translated into dummy variables

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English Premier League model

¨ Manchester United model On the March 5th, Manchester United lost a home game in the

UEFA Champions League competition

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Analysis - Hypothesis

¨ S hypothesis: “Leading brands in sports industry have different weekly/daily KPI.”

¨ C hypothesis: “Weekly/Daily KPI of a leading sports industry brand is correlated with its

other weekly/daily KPI.”

¨ R hypothesis: “Event1 of the leading sports industry brand contributes to their

weekly/daily KPI more than the other event2 does.”

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Statistical Analysis - Example

¨ S1a: Leading brands in sports industry have different weekly SMM reach.

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¨ Friedman’s ANOVA non-parametric test concluded that:

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Statistical Analysis - Result

Leading brands in sports industry have different levels of ... Result

S1a) Weekly SMM reach YesS2a) Weekly SMM growth rate Yes

S2b) Daily SMM growth rate YesS3a) Weekly SMM fan engagement Yes

S3b) Daily SMM fan engagement YesS4a) Weekly SMM popularity No

S4b) Daily SMM popularity NoS5a) Weekly Internet presence Yes

S5b) Weekly Internet trend No

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Statistical Analysis - Outliers

¨ Outliers are identified, but they exist as a result of certain internal and external factors that influenced the KPIs

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Box plot (talkingAboutWeekManU)

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Correlation Analysis

¨ Cn: VAR1 of a leading brand in sports industry is correlated with its VAR2 on a W/D basis.

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Regression Analysis

¨ General estimation model for all EPL brands with dummy variables

¨ Most dummy variables (Dni and Bni) and even some constants (c) are insignificant Only 9.5% of the dummy variables was significant They were supposed to estimate the impact a certain type of

event has on the EPL brands’ SMM and search marketing KPIs

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Regression Analysis - Result

¨ Contribution of EPL derbies (B10) to Manchester United’s and Manchester City’s Internet trend

¨ reason for high percent of insignificant variables can be the small sample due to such a high number of regressors

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Conclusion

¨ Statistical analysis showed that there exists significant difference between EPL brand’s KPIs

¨ Correlation analysis showed that Internet presence is highly correlated with Internet trend KPI SMM fan engagement is correlated with SMM growth rate and

SMM popularity on a weekly level

¨ Regression analysis showed that most of the regressors were insignificant One year data sample is too small Further analysis needed

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