The Numbers Game - d1k14t0yx9btx2.cloudfront.net€¦ · Chapter 8: Data acquisition strategies .....55 8.1 Target data 55 8.2 Venue technologies 56 8.3 Spectator buy-in 58 Chapter

David Walmsley

The Numbers GameBig Data and the Business of Sport

A SportBusiness International Knowledge Centre report

Published December 2015

© 2015 SportBusiness Group

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Author: David Walmsley

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SportBusiness International Knowledge Centre iii

Contents

ContentsIntroduction ..................................................................................... 6Acknowledgements 6About the author 7

Chapter 1: Defining big data ............................................................ 91.1 What is ‘big data’? 91.2 Big data typologies 101.3 Where does the value lie? 111.4 Big data philosophies 121.5 Hello big data, goodbye CRM? 13

Chapter 2: Big data strategy ............................................................. 152.1 What does a data-driven sports organisation look like? 162.2 Evolution, not revolution 182.3 Data supporting goals 202.4 Data setting goals 21

Chapter 3: Collecting big data .......................................................... 233.1 Internal data 243.2 External data 253.3 New data 26

Chapter 4: Interpreting results ......................................................... 294.1 Preparation 294.2 Analysis 314.3 Insight 334.4 Results 33

Chapter 5: Big data – the dangers ..................................................... 375.1 Limitations of big data 375.2 Verification 395.3 Context 40

Chapter 6: Using unstructured data ................................................. 436.1 Survey data 436.2 Social media data 45

iv SportBusiness International Knowledge Centre

Contents

Chapter 7: Privacy and data acquisition ........................................... 497.1 Privacy issues 507.2 Willingness to share 517.3 Incentives to share 54

Chapter 8: Data acquisition strategies ............................................. 558.1 Target data 558.2 Venue technologies 568.3 Spectator buy-in 58

Chapter 9: Data segmentation .......................................................... 619.1 Breaking down segmentation 619.2 Putting segmentation to work 62

Chapter 10: Shaping communications .............................................. 6510.1 The right content 6610.2 The right channels 6710.3 The right time 68

Chapter 11: Big data’s future ............................................................ 6911.1 Data sources 7011.2 Data warehousing 7011.3 Real-time analytics 7111.4 A focus on sponsorship 7111.5 Democratisation of data 7211.6 The human factor 73

Charts1: Percentage of large UK business organisations using big data 92: Kinds of data used by global companies 113: The SMART Model 134: Cultural challenges faced around adoption of big data analytics 205: The ‘Perfect circle’ of CRM strategy 216: Barriers to data collection and analysis 237: Valencia CF website mobile shares and referrals 268: Major global corporations’ experience of data analytics 309: Statistical methods of analysis relevant to commercial sport strategy 3210: Social media platforms used to share online sports content 3811: Facebook interactions: 2014 FIFA World Cup final and Super Bowl XLVII 4412: Twitter impressions: NASCAR’s Chip Ganassi Racing Team 4713: Americans’ attitudes towards data trade-off statements 50

SportBusiness International Knowledge Centre v

Introduction

14: Privacy considerations around big data 5115: Venue benefits sports fans are willing to share personal information for 5216: Positive responses to personalised online content among millennials 6517: Visiting professional and collegiate sports team sites, by platform 66

Case Studies1: Horseracing becomes a data-driven sport 172: Using data to build loyalty 193: Milwaukee Brewers and internal data 244: Major League Soccer’s data capture 275: Database analysis in Major League Baseball 316: Using data to form hospitality offer 347: Tracking tennis participation 458: Targeting millennials through social 479: The Jockey Club – rewarding racegoers 5310: Norway’s Connected League 5911: Social completes the Champions League picture 6212: Segmenting the Challenge Cup final 6413: Tackling season ticket churn at Leicester City 67

6 SportBusiness International Knowledge Centre

Introduction

It’s been more than a decade since Michael Lewis’ book Moneyball opened the sporting world to the potentially positive impact of statistical analysis, with his portrait of Billy Beane’s transformation of the Oakland Athletics’ fortunes using the power of numbers to turn established wisdom on its head in the risky business of scouting baseball players.

In that time data has become if not king, then at least the power behind the throne in dressing rooms around the world, influencing transfers, tactics and training in equal measure.

Now, the rules are changing off the field too. As financial performance becomes an increasingly important influence on resources and results, teams, venues and their commercial partners are beginning to look at the data they hold on fans, spectators and viewers and identify how they can unlock the insights they contain to benefit revenue streams across their business.

However, though transferring the concept of data-driven strategy from the pitch to the boardroom is a neat and simple vision to sell, the reality is far more complex. On the pitch, we at least know who the players are, but off it, actually understanding who is watching – let alone how they do so – is a huge challenge for even the biggest sports organisations.

That means a data-driven business of sport is, to quote one practitioner in the field, a largely uncharted “blue ocean market”. This report aims to help readers navigate a course across it.

To understand the importance of data now in all walks of life look no further than the common use of the phrase ‘Big Data’ – ‘data’ itself is no longer deemed an appropriate term to reflect the amount of numbers at one’s disposal, and the techniques and technologies required to uncover the secrets of large datasets that are diverse, complex and of a massive scale.

Using illustrative case studies and interviews, Big Data and the Business of Sport will give readers working in the business of sport a clear sense of what is possible in the

brave new world of data, and how they can begin to move their own strategy onto an analysis-led footing.

And for those already travelling this road, this report will act as a means of benchmarking strategy against current best practice, and as a wide-ranging source of inspiration for future plans.

AcknowledgementsThe author would like to thank the following for their contributions to and assistance in the preparation of this report:

• Garry Adamson, 4Sight Sport & Leisure

• Josh Apiafi, Rewards4Racing

• Richard Ayers, Seven League

• Jon Ola Bergaplass and Thomas Torjusen, Norwegian Professional Football League

• Paul Burditt, Lawn Tennis Association

• Chip Ganassi Racing Team

• Pearse Connolly, FotballMedia AS

• Brandon Doll, Oakland Raiders

• Steven Finlay, Co-operative Bank

• Fiona Green, Winners FDD

• Dr. Michelle Harrolle, University of South Florida

• Haynes Hendrickson, Turnkey Intelligence

• Diny Hurwitz, Milwaukee Brewers

• Jockey Club Racecourses

• Leicester City Football Club

• Bernard Marr, Advanced Performance Institute

• Mintel

• Kyle Nelson, MVPindex

• NewsCred

• Peter Oliver, Green 4 Solutions

• RadiumOne

• Matt Rogan, Two Circles

• Rugby Football League

• Charlie Shin, Major League Soccer

• Rod Street, Great British Racing/British Champions Series Limited

Introduction

SportBusiness International Knowledge Centre 7

Introduction

groups across disciplines including sports marketing, sponsorship and communications, event bidding and hosting, sports participation, and gambling and tourism. He has published more than 100 in-depth reports on current and future trends in these sectors.

As a communications specialist, Walmsley has worked with sports organisations ranging from the British and Irish Lions to FIBA (the International Basketball Federation) and the Spanish Golf Federation (RFEG).

He is a former journalist who worked with media groups including the BBC and Independent News & Media, and has written six successful books on football and rugby union.

• Jeff Strickland, Simulation Educators

• Jim Tobin, SAS

• Dr. Joseph Turow, University of Pennsylvania

• Professor Kirk Wakefield, Baylor University

• Manuel Portilla White, Club Santos Laguna

• Chris Zeppenfeld, Charlotte Hornets.

About the authorDavid Walmsley is a sport and leisure analyst and writer with more than a decade’s experience researching and consulting in these fields. As an analyst, he works primarily with the SportBusiness and Mintel research and intelligence

https://uk.linkedin.com/in/david-walmsley-5271b74


The Numbers Game Chapter 1: Defining big data



What has happened over the past decade or so, however, is that available data has become exponentially larger as technological development has expanded our ability to capture a widening range of information from an ever-broader set of sources, store it in unprecedented quantities and analyse it in insightful new ways. In its own definition of big data, IBM estimates that the volume of data being produced worldwide is now growing by 2.5 billion gigabytes a day.

It is this achievement of scale has made big data a ‘thing’, a phenomenon we need to formalise and standardise into a new science that can be applied to existing business practice.

The Oxford English Dictionary, which added the term to

The specific nature of on-field metrics has enabled sports analytics to come a long way in a comparatively short time: from goal expectancy and earned run average to strike rate and line breaks, almost every aspect of play and performance is now being tracked, benchmarked and fed into tactics, training programmes and transfer strategies by almost every major team in every major league worldwide.

However, the business of consumer data is far more complex, far more variable and just far bigger than the world of statistics generated inside the playing bubble.

Big data is big news, but it is also a big concept with which to get to grips, and not just for sports organisations that may traditionally be less familiar with the business intelligence systems and algorithms at its core.

While a June 2015 survey of global businesses by technology analyst firm Gartner indicated that 75 per cent were either investing in big data or planning to do so between then and 2017, research carried out in Britain by e-skills UK in November 2013 for analytics software provider SAS found only 14 per cent of firms with more than 100 employees had adopted big data practices by 2012, with the number projected to rise to 29 per cent by 2017 (see right).

Among small and medium-sized businesses, according to the SAS study, adoption rates ran at less than 0.2 per cent.

So for almost all industries, understanding the concepts of big data can be as big a challenge as harnessing its potential – which means it is worth taking some time to consider the fundamentals of the discipline before attempting to apply them to commercial strategy in sport.

1.1 What is ‘big data’?Man has been collecting and analysing data for centuries. From Dr. John Snow’s mapping of the London Cholera Outbreak of 1854 to statistical visualisations of Florence Nightingale after the Crimean War, we have a long history of counting, observing and collating data to solve problems and make new discoveries in everything from healthcare and education to commerce and defence.

Chapter 1: Defining big data

Figure 1: Percentage of large UK business organisations using big data, 2012 and 2017 (projected)

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https://www-01.ibm.com/software/data/bigdata/what-is-big-data.html

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http://www.information-age.com/technology/information-management/123460178/75-businesses-will-invest-big-data-next-two-years-gartner

https://www.e-skills.com/Documents/Research/General/BigDataAnalytics_Report_Nov2013.pdf

https://www.e-skills.com/Documents/Research/General/BigDataAnalytics_Report_Nov2013.pdf



of sales figures, customer numbers and demographics will sit unstructured counterparts of, for example, video, audio and user-generated text.

Variety is considered by many data scientists to be the key element of big data, in that it is only through combining information from multiple sources that the most original and far-reaching insights can be found.

VelocityThe new technologies that have created the big data phenomenon have been particularly important in enabling the capture and analysis of rapidly-changing information streams, often in real-time.

High-frequency stock market trading would be perhaps the prime example of big data of this type, while in-play betting platforms or online gaming services supporting potentially millions of players simultaneously would be others found closer to the world of sport.

There is less agreement, however, on where the line sits between normal data and big data: how large does the dataset have to be for it to be deemed ‘big’? How many sources must it be drawn from? How fast should it be moving?

This is partly because the pace of technological change is constantly moving the goalposts, meaning big data today can be small beer tomorrow. But it is also related to the importance of context: a large organisation’s small data can be a smaller one’s big data. So big data can work for small teams with a few thousand fans as much as it can for the world’s biggest franchises.

“The first myth that I want to dispel is that you need a huge amount of data to build a predictive model. A couple of thousand customer records and a few dozen choice pieces of information about those customers are more than enough, and many useful models have been built using less than this.”Predictive analytics specialist Steven Finlay (Predictive Analytics, Data Mining and Big Data: Myths and Misconceptions, July 2014)

1.2 Big data typologiesThe datasets most organisations will be examining as part of

its lexicon only in 2014, takes the term literally, defining big data as:

Data of a very large size, typically to the extent that its manipulation and management present significant logistical challenges.

Others focus on availability of information – looking at the quantity of sources rather than the quantity of data they produce – but the most common definitions consider big data to be characterised by its creation, storage, retrieval or analysis being unusually large in one or more of their volume, variety or velocity:

VolumeThe first characteristic that might enable a dataset to be considered big data is that it is too large to be stored on a standard PC and to be analysed with traditional software products such as Microsoft Excel or SQL databases.

The decreasing cost of cloud computing is enabling the storage of ever-larger datasets, while new tools such as NoSQL and Hadoop have been developed to interrogate them.

VarietyBig data will typically be drawn from a wide and significant number of sources, which produce both structured and unstructured information – so alongside the structured data

Social media posts are an example of semi-structured data

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the value it can add to business planning and performance.Consulting firm McKinsey identifies five broad areas in which big data can create this value:

• Making information transparent and usable at a much higher frequency

• Producing more detailed and accurate information to improve forecasting and support experimentation

• Enabling narrower customer segmentation to create more precisely-tailored products and services

• Improving decision-making

• Enhancing product development.

However, value addition is not an automatic by-product of adopting big data processes, and bigger does not always mean better, only more expensive and harder to manage.

Dino Citraro, co-founder and design director at data

their big data initiatives fall into the categories of structured and unstructured information, as mentioned above.

1) Structured dataStructured data is data that can be contained in a fixed field within a file or record such as a spreadsheet or database, and is relatively easy to input and analyse as a result.

Examples of structured data include sales data, financial data and customer data

2) Unstructured dataUnstructured data is data that cannot be organised in a pre-defined manner, making relationships between data points harder for traditional software to spot.

Examples of unstructured data include video, photographs or e-mails

3) Semi-structured dataSemi-structured data does not have the formal structures found in relational databases but can still have tags and markers applied to it to identify separate elements and create hierarchies of records and fields within it.

Examples of semi-structured data could include survey data, webpages and social media posts.

These types of data will be available internally (from sources within the organisation, such as transactional data or customer feedback) and externally (either from public sources, such as census or government data, or from third parties including social networks or research agencies).

Typically, successful analytics programmes will look at multiple types of data. A survey of 316 executives of large global companies published in September 2015 by Forbes Insights, for example, showed a combination of structured, unstructured and semi-structured types of information comprising the most popular datasets being utilised (see right). The top three was led by location-based data (structured), text (unstructured) and social media (semi-structured).

1.3 Where does the value lie?Big data can often – if not always correctly – be seen as requiring big investment, meaning it needs to demonstrate

Figure 2: Kinds of data used by global companies

Source: Forbes Insights, September 2015

Location-based data

Text (e-mails, fax, PDF)

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Other

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Consider this graphic I recently came across on Twitter:

I wasn’t really sure what this meant, so I asked:

Useless data? What is useless data?

This ‘useless’ data is seen as an inevitable by-product of our ability to capture and store an ever-growing volume and variety of information, a situation that makes the case for increasingly sophisticated software systems being needed to sort the wheat from the chaff.

An alternative response, however, is emerging in the advocacy of improving the relevance and quality of what goes into the funnel in the first place, rather than simply getting a bigger funnel.

Many practitioners in the field are looking to qualify the concept ‘big data’ with a range of terms all pointing in this direction, such as ‘smart data’, ‘right-sized data’ and even ‘small data’.

So while McKinsey’s five benefits outline the case for the value of big data, at a micro level the successful organisation is targeting an increasingly smaller, but more relevant, portion of the available pool rather than trawling its entire depth.

“You don’t have to boil the ocean to determine its salt content. You don’t have to eat the whole steer to know it’s tough.”Jerry W Thomas, President and Chief Executive, Decision Analyst Inc

1.4 Big data philosophiesAccessing data is not an issue for most businesses – more typically the issue is drawing out the analysis that will deliver its value.

Bernard Marr, chief executive of the Advanced Performance Institute, says “the reality is that most businesses are already data rich, but insight poor”. The response to the imbalance between information quantity and insight quality has been the articulation of model processes designed to keep a business’s focus on its objectives and outcomes to avoid becoming lost in the uncharted depths of the data ocean.

Two of these are the SMART model proposed by Bernard Marr, and the SSAA philosophy of Steven Finlay.

SMARTAs its name suggests, the SMART model advocates taking a targeted approach to big data, using a series of prompts

visualisation agency Periscopic, provides a pertinent example of this in a blog following a Twitter conversation he had with Microsoft in 2014:



5) TransformThe final stage of the process is arguably the most important: ensure the actions identified are put in place so that smart data analytics have a transformative effect on the business and become embedded in best practice.

SSAAThe SSAA approach outlined by Steven Finlay in his book Predictive Analytics, Data Mining and Big Data: Myths and Misconceptions identifies a set of principles to follow when dealing with big data.

The four elements of this philosophy are Seek, Store, Analyse and Act:

• Seek: Proactively search for and obtain new data

• Store: Bring all acquired data together in a single entity

• Analyse: Interrogate the data to produce insights about people’s past, present and future actions

• Act: Use these insights to inform decision-making and the actions resulting from this.

Finlay describes big data philosophy as taking a holistic approach to the subject and imposing the same processes regardless of the scale, structure or source of the information.

1.5 Hello big data, goodbye CRM?From ticketing and merchandise to sponsorship and broadcast rights, many of the areas in which big data promises sports organisations improvement and success have previously been targeted through customer

to narrow data requirements to a manageable scale for the business’s resources and ambitions (see above).

SMART’s five components are described as follows, with the middle three phases all underpinned by the power of technology in collecting, analysing and visualising the data required:

1) StrategyDon’t start with the data. Instead, begin with the specific objectives of your business strategy. Knowing what you want to achieve helps you identify the information that will be most useful in answering the questions you need to ask

2) MeasureOnce you know which types of data will be most relevant to your objectives, determine how you will capture these in a consistent, comparable manner, and which metrics will be most important to quantify

3) AnalyseWith the data in place, the mid-point of the process requires the application of data mining and analytics to extract the relevant trends and insights they contain

4) ReportThe outcomes of the analysis process need to be reported in a manner that makes clear the implications and opportunities suggested by the data, enabling the appropriate actions to be developed from them across the business

Figure 3: The SMART Model

Source: Bernard Marr

Start with strategy

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but to the whole world. And that’s because we have access to data – and lots of it: who are my fans, where do they live, how do they live, what do they look like, how do they behave, and what do they want from me? But most importantly, what will they do for me?”Fiona Green, Director and Co-Founder, Winners FDD

Without data and the insights it can generate, CRM is indeed primarily about managing customer relationships, ensuring existing processes run smoothly and enabling sales teams to respond to any disruptions that emerge.

With data, however, CRM becomes a far more proactive business function that can build, foster and strengthen customer relationships rather than simply marshall them.

Big data – when sourced, stored, analysed and acted upon in the manners suggested above – is both a critical friend to CRM in the present and the foundation of its growth in the future.

relationship management (CRM). So does the advent of all-singing, all-dancing, all-powerful big data spell the end of CRM?

The answer is a qualified no, big data will not kill CRM. However, big data is already fundamentally changing the way we view CRM for the better by shifting the emphasis from the ‘management’ focus of its software to the ‘customer relationship’ element that can be measured, tracked and cultivated through data.

“We’d like to rewrite the definition of CRM because when the term was first coined back in the early 1990s it was just about B2B software: they were programmes that helped sales representatives stay on top of their leads as they moved them through the sales funnel.

“Fast forward to 2015 and CRM means something a whole lot different, not just to the sports industry,


The Numbers Game Chapter 2: Big data strategy

That is not a reflection of lack of interest in the potential of data; rather there is a historic lack of need to explore it, a hangover from the days when sports properties had a primarily local reach and an easy monopoly of Saturday afternoon entertainment. If you staged it, they would come.

“For companies like Amazon – built in the digital age – data was always going to be at the centre of the business model, but a football club that has been established for 100 years has never had to think about data or spend any money on advertising. They are used to people automatically renewing their season ticket because they are a fan, and turning up on a Saturday because that is what they do and that’s what their dad did.

“But that is all changing now as technology has given us so many more choices. When I was a kid we didn’t have Wi-Fi, we didn’t have Twitter, we didn’t have Facebook and we didn’t have the quality TV broadcasts we are now experiencing.

“So rights-holders now have to use data, and have to use CRM, because they have to fight for share of people’s attention, for a share of people’s wallets. We have to get these clubs behaving like Amazon or Vodafone. It’s hard for people to hear – particularly fans – but if a business is losing ticket sales, unless they change the way they do business it will only go one way.”Fiona Green, Winners FDD

So big data needs a seat at the strategy table, but does it need to set the agenda and make all the decisions? In the data-driven sports organisation, is data at the wheel or just helping to read the map?

Of course, no two properties are exactly alike, meaning big data will sit somewhere along a continuum of influence according to a range of individual circumstances, but there are some universal reference points that all organisations can set their compass by.

In Chapter 1 we talked about ‘right data’ as much as ‘big data’, reflecting the consensus that bigger is not always better and that the best data is the type that is most relevant to your business goals.

The logical next step, therefore, is to consider where this data stands as a driver of sports organisations’ commercial strategies, and to ask how much influence it should have on their development process.

For most, “more than currently” will be the answer to the latter question; whatever the sophistication and worldliness of their on-field analytics, in commercial terms most sports organisations are considered to be talking a good game, but not playing it when it comes to big data.

“Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it...”Dan Ariely, Professor of Psychology and Behavioural Economics at Duke University

“There are some sports organisations that are optimising data, there are some that are doing nothing with data, and there will be some that are using data but not using it optimally. I would say the last two groups are in the large majority. There are very few sports organisations that are operating at the top end of the scale.”Fiona Green, Director and Co-Founder, Winners FDD

“Compared to mature markets like retail, sport has not come to the power of data anything like as quickly. Supermarkets know a huge amount about their customers, their habits and preferences; [horse] racing and other sports are coming off a very low base by comparison.”Rod Street, Chief Executive, Racing Enterprises Limited

Chapter 2: Big data strategy



don’t just show up in your arena like they did in the 1990s. We were the number one team in the NBA (National Basketball Association) back then – you just had to roll a basketball onto the floor and you had a full house.

But now every team is struggling to fill arenas – there are so many ways you can consume the product and so many more competing entertainments, so we have to be much smarter. That is where the management has to be willing to invest in data analysis to make better decisions.”Chris Zeppenfeld, Director of Database Marketing and Analytics, Charlotte Hornets

“Prior to 2006, we didn’t understand the power of data, and even then, it took three years to convince the leadership.”Charlie Shin, Senior Director, Strategic Planning, Major League Soccer

The next four elements – acquiring the expertise to analyse data, ensuring the data is of the right nature and quality, having a clear idea of the aims of the data mission and revisiting operational structures to be able to respond to the opportunities emerging – can be characterised as technical essentials of a data-driven strategy.

The sixth, however, is another reminder of the importance of the human element of success, underlining the point that buy-in is not restricted to the boardroom alone: the power of data needs to be recognised across the organisation, and particularly by the people it can help the most.

Those at the centre of analytics operations can win these hearts and minds by delivering actionable insights that have a direct impact on each internal team’s business priority, but they need to have the opportunity to put boots on the ground to do so.

“It might sound trivial, but you need to put your analytics specialists in the office next to your decision-makers.

“One of the reasons we had such success was my office was right next to my boss’s office. There is a

2.1 What does a data-driven sports organisation look like?Successful teams wear different kits, but are often underpinned by similar strengths. Successful data-driven sports organisations are no different: they may pursue different objectives through widely differing means, but there exists a core of behaviours and characterisations that are broadly common to all.

“A data-driven organisation is one that doesn’t make any decision of significance without asking, ‘what does the data say?’ So it’s making decisions based on evidence rather than instinct, and it tests and trials as opposed to taking wholesale up-front risks.”Matt Rogan, Chief Executive, Two Circles

Practitioners in the field describe a number of behaviours that create a data-driven mindset in a sports organisation, but the CRM Roadmap of the 4Sight Sport and Leisure agency provides a useful overview, and can be summarised as identifying seven key characteristics:

• Buy-in from top management

• Analytics expertise

• Prioritisation of data quality

• A clear understanding of current positioning and future objectives

• Relevant support structures in place

• Involvement of people across the organisation

• Continuous development.

It is not by chance that management buy-in is top of that list – the teams that are already leading in data analytics outline senior executive support as the single most important enabler of their development and success. If the leaders don’t believe, nor will anyone else.

“There has to be a yearning from top management for the organisation to be more data-driven, even if they are not necessarily data-driven themselves. They have to want to be smarter, and sometimes that is not always the case.

“There was a disease of ‘let’s just do what we did last year’, or of gut feeling. But 19,000 people

http://www.4sight-sport.com/crm-developing-a-crm-roadmap/

http://www.4sight-sport.com/crm-developing-a-crm-roadmap/



Case study 1: Horseracing becomes a data-driven sport

In 2015, British horseracing set out to become a data-driven sport to achieve its ambition of growing attendances from six million to seven million by 2020.

“It’s about a step change in our thinking. If we carry on as we are, we will continue to do well with some incremental growth, but to shift from six million to seven million – that needs some big changes in our behaviours and attitudes and we go about things.

“Sport tends to make decisions based on anecdotal evidence. We need to learn to plan through insight rather than instinct, and we think this process is how we will achieve that.”Rod Street, Chief Executive, Racing Enterprises Limited

The year-long project, run with the data-driven agency Two Circles, involves the sport’s biggest ever analysis of consumer data, and is split into three phases:

• Engage: securing the buy-in of racecourses across the country

• Discover: capturing, integrating and analysing the courses’ consumer data

• Act: identifying new opportunities for attendance growth and creating new processes to increase the value of data capture.

The engage element of the project is very much about involving people across the entire organisation – or in this case, sport – to ensure not just understanding and acceptance of strategy, but to offer a tangible stake in its development and a measurable share of its rewards.

“All 58 tracks have agreed to participate, which is unprecedented and a major leap forward; individual racecourses are very protective of their own consumer data and will very often actually consider each other as competitors.

“It’s all being managed in a confidential way

with the sport able to look at the aggregated, anonymised data, and give the courses a much clearer idea of who racing’s customers are and how they can reach them more effectively.”Rod Street

The same idea is also central to the ‘act’ strand of the process, which enables disparate departments – racecourses – to learn from each other and adopt best practice, as determined by quantifiable measures of success.

“A lot of courses are doing a lot of different things well, but we have not come up with a more central strategy that all courses can apply for collecting data. Chester is one course that runs a lot of really good initiatives on racedays to encourage consumer interaction that captures data, and Ascot and Cheltenham have introduced free Wi-Fi, which you have to share an e-mail address to log on to.”Rod Street

The Jockey Club is using data to grow attendances at its racecourses

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Just as some teams are better resourced than others in the transfer market, so too will there be a range of analytics capabilities imposed by constraints of finances and headcounts.

Matching ambition to ability is therefore key, with the advice of the more experienced being look before you leap – and be wary of going straight in at the deep end.

“Before any data-driven strategy can be implemented, the availability of human and technological resources must be taken into consideration. The level at which the organisation pursues such a strategy must line up with the organisation’s structure. Without this consideration, there is a higher risk of failure.

“Another area to consider is the financial and opportunity costs to implement a data-driven strategy. Does the predicted ROI make a good case for the investment? How in-depth must an analysis be to run a successful campaign? It may be worth investing in a data-driven strategy over a period of time, building out databases or adding human capital slower to prove the value it may provide the organisation.”Diny Hurwitz, Data Analyst, Milwaukee Brewers

“Whether you are in a minor league or the NFL (National Football League), our emphasis is on starting slow and gradually getting yourself to the level of the organisations that are really doing it well. It’s not like you can just hire the right people and get there tomorrow.

“Start slow, collect some information, survey your fans – ask who they are and what they like. That’s pretty basic, but you don’t have to be running regression analysis on day one.”Haynes Hendrickson, President, Turnkey Intelligence

2.2 Evolution, not revolutionHow does data-driven strategy evolve? The answer is slowly – and the comparative youth of the discipline is the principal reason for that.

Because of the lack of an established roadmap, the

real value in being able to pop in and say ‘check out what’s happening in the secondary ticketing market right now’. That five-minute conversation can be very valuable because you can have it on the spur of the moment, or you might overhear a conversation about a challenge and have a piece of analysis to hand that might help with it.

“When you have a formal meeting only once a week, you’re not in tune with transactions, pricing and marketing. Our job is to help the management make better decisions, and if we don’t know their thoughts on pricing, for example, we can’t provide that value.

“However that analytics team is made up, it should be right next to the VP of sales or whoever it is making the key decisions.”Chris Zeppenfeld, Charlotte Hornets

The final strand, of ongoing development, is a recognition that data is continuous even when it is discrete. The flow of information available to sports organisations is unceasing and should be seen not as identifying a final destination for strategy; instead it should be seen as producing a series of signposts that point out where it should be heading next, whether by drawing out new sources of consumer demand or highlighting elements of best practice that can be re-purposed across the organisation as a whole.

The differences between data-driven sports organisations tend to be not so much in the degree to which they embrace these characteristics, but rather the scale on which they are able to turn them into actions.

Consider resources before undertaking a data-driven strategy - Diny Hurwitz, Milwaukee Brewers

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saying ‘we think this’; we’re coming in with hard facts. We didn’t decide as an organisation to be data-driven, it just evolved as we got more and more people involved. It used to be discussions with a lot of people in a room throwing stuff at the wall, now it’s me and my team who are in charge of that meeting. But there wasn’t a eureka moment.”Chris Zeppenfeld, Charlotte Hornets

For the next wave of adopters, there is an understandable urge to grab hold of the most tangible element of this type of business strategy – the data around which it is based – rather than push on into the uncertainties of analysis, insight and ultimately action.

“I’m always reminded of that flow: data – information

pioneers of commercial data analytics in sport had to feel their way carefully into unknown territory.

“It’s been an evolution over the seven years I have been here. I don’t think we were very data-driven at all when I started. Teams weren’t really hiring people to do business analysis then. I was hired to do CRM and we were one of the first teams to have a CRM system, maybe only the fifth or sixth in the NBA. We had had it for eight months and it was a disaster.

“They hired me and gave me six months to figure it out or they were going to cut it. We rebuilt it from scratch with the help of one of our vendor programmers and things began to improve once we got the ship sailing in the right direction.

“We are no longer coming into pricing meetings

Case study 2: Using data to build loyalty

In 2013, racecourse operator The Jockey Club launched the first retail bond in UK sport as a means of funding a major redevelopment of Cheltenham.

The Racecourse Bond offered a return of 7.75 per cent on a five-year, fixed-term investment of between £2,000 and £100,000. It exceeded its £15 million target by almost £10 million, making it the second largest unlisted UK bond ever.

The 7.75 per cent return is paid as 4.75 per cent in cash interest and three per cent in points for the Rewards4Racing loyalty scheme, which can be redeemed against items ranging from tickets and food to hospitality and annual memberships at Jockey Club racecourses.

Rewards4Racing is an important data collection tool for the Jockey Club, but played a central role in actually creating the bond strategy as well: the club and its financial advisers Rothschild used feedback from scheme members to determine the potential and structure of the eventual public offer.

“We were used initially as a survey tool to go out to our members and find out if they would be

interested in investing in a bond, and if they were, how they would like it be structured.”Josh Apiafi, Co-Founder, Rewards4Racing

Rewards4Racing is also used to track the bond’s impact on investors’ racegoing behaviours as they redeem their loyalty points back at Jockey Club racecourses.

“The programme has enabled The Jockey Club to track what’s going on with the bond as points are given every quarter as part of a bond-holder’s interest payment.

“We can track how many investors are new customers to Jockey Club racecourses, how many were lapsed and how many were engaged at the launch of the bond. We can then measure whether investors are spending more at Jockey Club racecourses because of the points they receive on a quarterly basis. The Jockey Club is then able to measure the impact of this spend on the real rate of interest it is paying.” Josh Apiafi

http://www.thejockeyclub.co.uk/news/the-jockey-club-racecourse-bond-closes-after-raising-24743500

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right. Coming in with data that can help people understand why their decision might be good or bad is the best way to gain acceptance.”Chris Zeppenfeld, Charlotte Hornets

“The reality is that senior management in a lot of sports still aren’t data-savvy, so the art of a lot of what we do is about making the complex simple.”Matt Rogan, Two Circles

The Forbes Insights global executive survey of September 2015 found that just 10 per cent of respondents considered their company to face no significant cultural challenges around the adoption and implementation of data analytics.

In contrast, 43 per cent identified establishing the culture needed to enable data-driven strategy as being an issue.

– knowledge – wisdom. People get terribly obsessed by just the ability to gather data, whether that is e-mail addresses, understanding usage patterns on websites or even the flow dynamics of how people move around stadiums.

“But that data doesn’t lead you necessarily to the information, the knowledge and the wisdom to make the right decisions. At the moment I definitely feel there are some parts of the sports industry that are a little stuck in the data gathering and information areas.”Richard Ayers, CEO, Seven League

Ironically for a discipline concerned with dispassionate facts and the cold truth of numbers, though, the roots of both these roadblocks lie in human nature and its ingrained wariness of new ideas.

That means the role of the data analytics sponsor in any sports organisation is that of salesman as much as scientist – both are needed to demonstrate value and build the relationships across the organisation needed to buy time for the process to run its course.

“A lesson I’ve learned over the years is that if the management doesn’t agree with a data-driven argument, it is because of one of two things:

“The first is that they don’t trust you yet. It’s a human thing; top management probably doesn’t know the ins and outs of regression analysis and algorithms, and they don’t have the relationship with you to trust you. That relationship is key because at some point there will be a conflict and they will have to trust you.

“Alternatively, they may not understand it at all. That’s a warning sign. If teams are just dismissing data because they don’t understand it, the job of the analyst is to present it in a different way.

“I would start where the majority of revenue comes from. Quick wins are great but unless they are impactful on decisions that drive a lot of revenue, they will be easily dismissed.

“We started with ticket pricing because that’s the most fundamental thing we have to get

Figure 4: Cultural challenges faced by global companies around adoption of big data analytics

Source: Forbes Insights, September 2015

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Leveraging data to improve customer experience

Valuing creativity and experimentation with data

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2.3 Data supporting goalsOne issue at the heart of the cultural challenges facing big data adoption in sport is the perceived threat it poses to established command structures, and the value of experience on which the authority of senior management is primarily based.

In the same way that robots are coming to take our jobs from us, so too is big data coming to take our decisions for us. The antidote to that contagion is the promotion of data analytics as a new means of





If one element is missing, the circle – and the strategy – is incomplete.

“Without any one of the five elements you don’t have a CRM approach: without strategy, what are you going to do with the data? Without technology, you will have to interrogate the data manually and you are not going to get the same impact. Without process and culture, the things you want to achieve will fall down. Conversely, if your data is dirty or you don’t have enough of it – or you aren’t analysing it properly to enable you to inform your decisions – then the other four are useless as well.

“For us, data is at the core, but it is no more important than any other element of CRM strategy.”Fiona Green, Winners FDD

There is also a highly practical reason for stepping back from the idea of big data as the answer to every question of strategy: simple manageability.

For most sports organisations, exhaustive coverage can quickly become exhausting coverage. Returning again to the idea of ‘right-sized’ data, leading practitioners argue that businesses should target their resources on areas they can have the strongest impact, rather than attempt to see every strand of strategy through the prism of analytics.

“Your data requirements, costs and stress levels are massively reduced when you move from ‘collect everything just in case’ to ‘measure x and y to answer question z’. Big data goes from ‘impossible for us’ to ‘absolutely possible for us’.”Bernard Marr, Chief Executive, Advanced Performance Institute

2.4 Data setting goalsStressing the collaborative role of big data in the commercial sports strategy mix should not overlook its huge potential to identify new opportunities, as well as confirm or disprove established business hypotheses.

“Ultimately, the data analytics team in any organisation needs to be driving – or at least heavily

strengthening established powers rather than of undermining them, and of a recognition that it cannot succeed in isolation to the same degree it can in concert with the existing pillars of sales, marketing and strategic planning.

“We have a tremendous amount of experience across our senior leadership and I don’t think data can ever replace traditional decision-making. But I do think it can supplement it.”Brandon Doll, Director of Strategic Projects, Oakland Raiders

“When I say an organisation has to be data-driven, that does have to be tempered with a little intuition and understanding of the context and nuances that underpin this industry.

“There are a lot of external factors that impact on the data – everything from results to the weather. Being aware of context is half the battle.”Matt Rogan, Two Circles

CRM agency Winners FDD presents clients with what it calls the ‘perfect circle’, where five elements of a data-driven strategy are given equal status and seen as mutually dependent on the other four.

Figure 5: The ‘Perfect circle’ of CRM strategy

Source: Winners FDD

Strategy

Data

Technology

Process

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influencing – the strategy, as well as providing the means to understand whether you are on track to hit numbers.

“The ideal would be that by Christmas, or maybe earlier, the analytics team in let’s say a football club would talk to the commercial director about the patterns they are noticing and what, therefore, a viable number would be for matchday revenue for next year.

“The commercial team will then be using that in their flash financial forecasts for the next year, but at the same time go back to the analytics team to ask what four or five KPIs it should plan against to achieve that revenue number. The analytics team can then say, for example, it’s about managing churn, customer acquisition and price points. That then becomes your marketing plan.”Matt Rogan, Two Circles

Smart organisations are already using big data to determine what future strategy should look like, based on the behaviours and preferences of existing and potential customers.

The example of the Racecourse Bond is one of data targeting – asking specific questions to gauge strength of interest in a range of possible strategy options.

But big data also offers more random routes to strategic

insight and future goal-setting, with what is known as ‘data discovery’ – a search for answers without specific questions. This is an approach akin to Google’s famous ‘20 per cent time’, that encouraged its employees to devote a proportion of their work to side projects they thought could help the company, without the pressure of having to be certain they would deliver results.

Data discovery is a similar concept of following the information trail without any agenda or preconception beyond asking whether the knowledge gained can be applied to the benefit of the business.

“Data discovery is looking at data from the other direction. When you start with strategy you work out what you need to know and therefore what data you need to collect to provide those answers. In data discovery you just look at the data with no questions or agenda to see what the data tells you about your business.

“This process can be a useful addition to the more tailored approach...and can potentially throw up all sorts of data gems. If a business has a huge amount of data that can be mined and analysed, then it’s definitely worth spending 10 per cent of their analytics effort on data discovery.”Bernard Marr, Advanced Performance Institute


The Numbers Game Chapter 3: Collecting big data

A 2013 data analytics survey of executives at more than 400 major global corporations, carried out by management consulting firm Bain & Company, identified these issues as a concern to a majority of those businesses.

Consequently, Bain recommends that relevance be considered the touchstone of data-collection planning: where capture and storage capabilities are limited, businesses cannot afford to be wasting time and space on sourcing and warehousing anything that is not making a direct contribution to strategic objectives.

“Companies need a strategic plan for collecting and organising data, one that aligns with the business strategy of how they will use that data to create value...a critical aspect of good data policy is to focus on identifying relevant sources of data.”Rasmus Wegener and Velu Sinha, Partners, Bain & Company

The age of big data has been created by the ability of technology to track almost everything we do, not just digitally but in the ‘real-world’ too; consumers are not necessarily producing more data, we are just able to capture more of it than ever before and do so from across an ever-widening range of sources. ‘Datafication’ is the term most used to describe this shift.

“Actions, movements and behaviour all leave a trail and tell a story. Technology is now allowing people to listen to this data, learn from it and react.”Stacy Glasgow, Consumer Trends Consultant, Mintel

Sports organisations have become very much part of the datafication trend, with digital media platforms in particular opening up new streams of data opening up before their eyes.

“Five years ago we were very much prioritising the collection of contact data – names, addresses, postcodes – and transactional data, what have they bought from our organisation. That would traditionally be something we would be very happy with.

“But with the rise of digital, social media and online shopping, customers leave footprints in many more trackable places, and that footprint is data. These new types of customer data are all usable and can also be harnessed in ways that we weren’t able to just a few years ago.”Garry Adamson, Managing Director, 4Sight Sport & Leisure

The main challenges businesses of all natures face in sourcing big data lie in identifying the wells they should seek to tap, and ensuring they have the capability to channel and collect the streams of information they unleash.

Chapter 3: Collecting big data

Figure 6: Barriers to data collection and analysis(%

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Big data practitioners identify three sources of data that sports organisations can usefully capture and then mine for insight, on a sliding scale of ease of access. These are internal data, external data and new data.

3.1 Internal dataInternal data is the easiest type of big data for sports organisations to source as it is all information they already possess, own and control access to. Examples of internal data common to sports teams and venues typically include:

• Ticketing information

• Transactional data (food, beverages, retail)

• Membership records

For much the same reason, alongside relevance sits accessibility – with little time and capacity to spare, it is a better idea to begin by picking the low-hanging fruit than immediately start climbing to the top of the tree, particularly for sports organisations who have typically little history of past data harvests.

“More data is better, but we advise clients to start simple, with ticketing data, CRM data or e-mail responses. Those are three key data points that can get the system off the ground and running.”Jim Tobin, National Sales Executive, SAS Sports Industry Practice

Case study 3: Milwaukee Brewers and internal dataThe Milwaukee Brewers are described by Major League Baseball (MLB) as “the model” for the sport’s use of off-field analytics, despite being one of its smaller-market and, in playing terms, less-successful franchises.

The Brewers use both primary and secondary data in their analysis projects, but put a strong emphasis on prioritising the data they have within the organisation before contextualising it with demographic data from outside.

“The key elements that are the main drivers of any analysis are the current and historical purchases of our fans on both the primary and secondary markets. Past buying behaviour is a key indicator of future buying behaviour.” Diny Hurwitz, Data Analyst, Milwaukee Brewers

Alongside ticket purchase and usage information, the Brewers also track fan behaviours and attitudes through a number of other sources across the organisation:

• Survey data, including self-noted demographic information and fan experience rating

• E-mail data, including textual analysis as well as open and click-through rates

• Online purchasing through both the Brewers’ shop and MLB.TV.

The accelerating migration of purchasing and interaction online is pushing the Brewers’ data capture systems in the same direction, with web-based systems allowing them to collect more and more data each season and evolve the analytical processes they apply.

“The majority of individual game purchases are now conducted online, so having the ability to easily capture these sales and enhance the user experience is key to the growth of the online space.” Diny Hurwitz

The Brewers are one of sport’s leading proponents of big data

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“MLBAM has created an environment and has helped develop technology for MLB clubs, including the Brewers, to better understand fans’ online behaviours. The partnership between the MLB clubs and MLBAM has greatly expanded the range of data we are able to collect both in-stadium and online.”Diny Hurwitz, Milwaukee Brewers

On a more individual basis, sports organisations can often tap into their commercial partners’ data collection capabilities to add to their own resources and datasets.

The Phoenix Suns of the National Basketball Association (NBA), for example, used their tie-up with telecoms provider Verizon Wireless to add a new layer of locational and demographic data to their internal analytics by importing information from mobile network records.

Verizon was able to identify:

• Where fans attended games live

• How many spectators were attending from out of town (typically around 20 per cent-plus)

• Demographic characteristics of game-attending fans (primarily aged between 25-54, parents with children at home, and living in households with collective income of $50,000 or more)

• Attendance at pre-season training games in the Phoenix area

• 24-hour increases in traffic at a fast-food chain featured in a promotion around a Suns game.

This example shows the high level of detail and targeting that external data can offer. In a similar way, another NBA team – the Cleveland Cavaliers – has used secondary ticketing market data to triple the number of fans it can track to 60,000 per game, and collect more information on a highly granular scale, right down to the price point at which supporters stop bidding on a seat.

Elsewhere, in European football, Valencia of Spain’s La Liga used structured social media data to give a value perspective to its decision to become the first team to add a ‘Share to WhatsApp’ function to its mobile website, an opportunity identified through a mixture of internal, external, primary and secondary research (see next page).

• Customer feedback

• Stock controls.

Internal data therefore is a solid starting point for any data strategy.

“For sports organisations, the number one source of data is the ticketing system. We almost always start with that because it is the biggest source of data you own. You may have millions of people following you on social media but that is not your data, it belongs to Twitter and Facebook, so the best place to start is with what you already own.”Garry Adamson, 4Sight Sport & Leisure

3.2 External dataExternal data is data sourced from outside the organisation and can be either public – and free to access from, for example, the government – or proprietary, where information is collected and owned by a third party. For the latter, third parties can range from a research agency to a commercial partner, and typically they make data available on a paid-access basis. External data types can include:

• Government data

• Secondary ticketing

• Social media

• Weather records

• Consumer research surveys

• Commercial market analysis.

Access to each of these sources will vary significantly in ease and cost, with data that can be provided by leagues or sponsors sitting at the more available end of the spectrum and commercially-owned information at the more restricted – and expensive – one.

The most accessible controllers of external data will typically be commercial partners or governing bodies. MLB, for example, has been managing all online properties of the league’s 30 clubs through its MLB Advanced Media (MLBAM) subsidiary since 2001, as a means of leveraging technology and resources on a national scale.



at social data, which can tell us how avid a fan someone is. The big question is how we get our arms around that data.”Haynes Hendrickson, President, Turnkey Intelligence

3.3 New dataThe datafication of the modern world means that more and more new types of data are emerging all the time, not just online but also from our movements and actions when we’re not in front of a screen.

If we use an e-reader, the device can track what we read, how long we read for, what we highlight and what we skip. If we wear an activity tracker, an app on our smartphone will record our steps and our heartbeat, our sleep patterns and our levels of stress.

Sources of new data are considered to include:

• Physical activity

• Conversation

• Photographs and video footage

• Sensor technologies

• The Internet of Things.

For sports organisations that have limitations in the amount of individual data they can collect internally or externally, new data is a particularly significant addition to their information arsenal.

“One of the big challenges for racing is that we only capture a relatively small part of the total available data on the racecourse.

“One reason for that is the big ‘walk-up’ element to racing attendances; the other is that we only capture the ticket buyer and not always the ticket holder – so the guy who organises a coach trip to the races is standing in front of the data of 40 other people we would like to capture.

“If you compare that with a well-supported football club with many season-ticket holders and regular ticket buyers, they will have data covering a much greater proportion of their fanbase. So the challenge for racing is to improve data capture.”Rod Street, Chief Executive, Racing Enterprises Limited

For the properties involved, these examples of external data in action all have in common a relative ease of access: through associations with the providers (Phoenix) and the relatively basic nature of the numbers being collected (Valencia).

However, the growing focus on social media as an external source of prized attitudinal and sentiment data is shifting attention to data types that are far harder for clubs to catch and far more expensive for them to buy in ready-made. It’s a new issue, but with a familiar core – once again the trade off between relevance and cost.

“It’s all about collecting as much behavioural and demographic data as possible. Those are the two big buckets. Demographic data is pretty straightforward but behavioural data is still evolving.

“It’s quite simple to procure transactional data because people buy tickets and merchandise, but the big area of growth in the behavioural data world is everything the fan is doing tangentially with the property rather than directly with it.

“So here we would be looking particularly

Figure 7: Valencia CF website mobile shares and referrals, 2014/15

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Case Study 4: Major League Soccer’s data captureMajor League Soccer (MLS) runs a CRM programme it calls its ‘fan funnel’; a four-phase process beginning with data acquisition, which it pursues through internal and external sources.

Internal data is sourced primarily from ticket purchases, merchandise sales and digital subscriptions, but the range of different systems in use across the typical sports organisation means the final list can be much longer.

“Lots of information exists within organisations that isn't integrated. People may not even be aware of it.”Charlie Shin, Senior Director, Strategic Planning, MLS

External data typically takes the form of demographic, household and lifestyle information sourced from third-party providers, or is sourced through co-promotions with corporate partners, such as Star Wars creators Lucasfilm.

“Star Wars fans are similar to MLS fans in terms of demographics and psychographics. So we created a sweepstake called the Star Wars Soccer Challenge which we promoted through Lucasfilm as a way to gather additional prospects for MLS.”Charlie Shin

MLS found value using the force

“While we have pockets of data – some sites have online booking systems, for example – they are usually owned by that site and we probably don’t even know where they are. Half of all tennis clubs have only one, two or three courts so they won’t have anything, and yet a lot of participation goes on.”Paul Burditt, Head of Business Insight and Planning, Lawn Tennis Association

“In tennis there is very limited what I would call ‘real data’, so non-survey-based data. If I could wave a magic wand I would want to be able to have a data warehouse in which there would be all the participation data of people on the courts, which could then plug in to frequency metrics, loyalty metrics, programming…the real nuts and bolts of site-specific things.

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The Numbers Game Chapter 4: Interpreting results

The quality versus quantity issue at the heart of all data-driven strategising applies not just to the sourcing of information, but the analysis of it too.

Just as success in data capture depends on identifying and prioritising the right kind of information, producing actionable insights from that data is predicated on subjecting it to the right kind of analysis. Such analysis needs to ask the most appropriate questions and serve up the relevant evidence to answer them.

Kevin O’Toole from National Basketball Association (NBA) franchise the Cleveland Cavaliers, summed up that requirement neatly in a panel discussion at the 2014 Veritix Sports Facilities & Franchises/Ticketing Symposium.

“The objective of analytics is to take data and transform it into meaningful insight for people that are making business decisions so they can make more informed judgements, and hopefully better decisions, as a result of the information they are being provided.”Kevin O’Toole, Senior Director of Business Intelligence, Cleveland Cavaliers

To achieve that goal, sports organisations can usefully consider breaking the data analysis process down into four distinct but successive phases: preparation, analysis, insight and results.

4.1 PreparationThe first phase of the data analysis process is about preparing not just the data, but the systems that will be used to interrogate it.

Data preparation centres on relevance, cleansing and integration. Relevance requires a double check that the data to be analysed is appropriate to the business objectives or commercial teams it is being asked to support, while a cleansing process needs to be carried out to filter any rogue entries, duplicates or blanks that might impact results.

“The analysis process depends on so many factors, but in the first instance you need to ensure the integrity of the data, and ensure you understand it in terms of whether there any outliers; these would typically be identified through regression techniques and other statistical methods.

“We often find a big spike in the number of 115-year-olds because many date-of-birth fields default to 1/1/1900 if you don’t fill them in.” Garry Adamson, Managing Director, 4Sight Sport & Leisure

Systems preparationAlmost all organisations will have to carry out some degree of integration of datasets produced by different business units, in different formats, and from different software systems.

The favoured venue for this is the data warehouse, a central storage area in which these individual streams can be merged into a single pool of navigable information. For the information owned by most organisations across the sports industry, however, the data warehouse remains a dream home rather than the current abode.

Chapter 4: Interpreting results

The Cavaliers have benefitted from secondary ticketing market data

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across the business spectrum – while fewer than two in five respondents had a dedicated analytics team and were using the latest tools for data mining, just 19 per cent rated the information they had to work with as high quality and consistent. Sixty-six per cent, meanwhile, said they lacked the technology to store and access data effectively.

Bain recommends the right people and tools to do the job should be put in place at the outset of the analytics process. With people, it identifies a mix of data, technical and business talent that should be assigned across a range of roles:

• Data scientists, to provide expertise in statistics and correlations

• Business analysts, to identify and prioritise the relevance of the identified patterns to commercial objectives

• Technical specialists, to manage hardware and software solutions.

With systems, Bain endorses Hadoop, HPCC and NoSQL, but highlights the need to continue looking at the new alternatives that continue to emerge at pace, particularly in the often free open-source sector.

However, that is not to say a data-driven organisation cannot exist without a multi-functional analytics team and a large IT budget behind it: no two practitioners are exactly alike, and they will all allocate a varying degree of resources and investment depending on their size and sophistication,

“Of the 30 teams in the NBA, about half either have a data warehouse or are looking at them. We have one, and we have access to data, but it is in nine or 10 different databases that are siloed off from each other.

“Our retail store knows that so-and-so bought a hoodie but it doesn’t know that their favourite player is Kemba Walker and that they are a season-ticket holder. That information exists but it is in three different places – being able to combine all that will make us much more efficient.”Chris Zeppenfeld, Director of Database Marketing and Analytics, Charlotte Hornets

That sport has been comparatively slow to embrace the data warehouse concept is seen by many specialists as another manifestation of the short-term focus that has delayed the introduction of commercial analytics more widely. Many organisations have instead sought to force the role upon their established CRM systems.

“CRM used to be the data warehouse system but that isn’t really its role – the purpose of the CRM system is, as its name suggests, to help manage the customer relationship; the data feeds the system and there is now too much data to be managed within it.

“The sports industry went about it backwards: everyone invested in CRM and only now are they building data warehouses. In a perfect world, it would have been the other way round.

“In sport, we have such a sales and service-oriented, and tomorrow-oriented, business. We need to sell tickets for tomorrow’s game – that’s the priority, so the first thing we invest in is what is closest to that, a CRM system. There’s nothing wrong with making that investment, you just need to recognise it can work better with some better infrastructure behind it.”Haynes Hendrickson, President, Turnkey Intelligence

Research carried out across a range of business sectors by management consultants Bain & Company in 2013 suggests these issues present a significant challenge for companies

Figure 8: Major global corporations’ experience of

data analytics

Source: Bain and Company, September 2013

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bookshop; for the purposes of developing an initial data strategy for sport, the non-specialist needs only at this point to be aware of some of the techniques that are likely to be applied to their information.

In his book Team Sports Marketing, Kirk Wakefield, Professor of Retail Marketing at Baylor University, identifies central tendency – comprising basic elements of mean, median, mode, standard deviation, standard error and confidence intervals – as the starting point, followed by a supplementary range of techniques (see next page).

as well as the nature and quantity of information that is available for them to analyse.

“All retail insight departments will have full-time data miners and data analysts writing the trend reports. That’s the kind of role I would want in sport, but at the moment there’s no point because we haven’t got the data.”Paul Burditt, Head of Business Insight and Planning, Lawn Tennis Association (LTA)

4.2 AnalysisWith a cleansed and consistent set of data to work with, the data-driven sports organisation now moves on to analysing this information to identify the trends, anomalies and correlations that can best inform its commercial strategy.

There are a range of processes and technologies that can steer the analysis phase, but – as ever – the bottom line is to stay focused on pulling out the information that will have most value to the priorities identified by the initial setting of the data compass (see Chapter 2). Know what you want to influence, and focus on the data that relates most strongly to that.

“If you are looking at, say, matchday revenue for a professional football club, you can pretty much drill that down to four or five KPIs that the marketing plan, strategic direction, pricing and product development for the year should be predicated on. The club should know that if they hit those KPIs, everything will follow.

“A data-driven organisation understands the key metrics it needs to keep an eye on and bases everything around these.

“It’s less about swanky kit and more about having a way of managing the data in your organisation that enables the business-critical information to be readily available, understood and communicated across the organisation to be acted upon.”Matt Rogan, Chief Executive, Two Circles

The theory and process of statistical analysis already has its own large and expanding section of your local library or

Case Study 5: Database analysis in Major League Baseball (MLB)

Diny Hurwitz, Data Analyst at the Milwaukee Brewers, identifies three priorities in their approach to database analysis.

Quality of the dataHow clean the data is and if elements such as purchase data have been properly aggregated across all accounts. The accuracy of the data greatly impacts any analysis.

Number of individuals in a datasetThe size of a dataset will help prioritise a campaign and will help identify whether the time and resources needed are worth the efforts by sales staff when compared to other available datasets.

Transactional data for game tickets takes precedenceThough the Brewers may have data elements on an individual – such as those that imply an available discretionary income or that an individual is engaged online with the team – the data elements that involve the action of buying baseball tickets are the key element in the segmentation of the database.

http://teamsportsmarketing.com/



campaign datasets, the Brewers – with assistance from MLB Advanced Media – have been transitioning to a more complex, predictive data model. The elements relating to purchase history used within the RFM model have become a large piece to the model’s development process.

“The theory behind the development of such a predictive model is that it will provide the Brewers with better insight on future buying behaviour using multiple data sources, rather than only use historical data to gauge future results.”Diny Hurwitz, Milwaukee Brewers

So at its root, data analysis has a strong element of science to it – but not always an exact one.

The analysis process can be complicated by potential differences in the nature of the multiple datasets that have been integrated into the overall pool. This can create issues of not so much compatibility, but of comparability.

“My participation figures from our [online] tracker survey never match up with Sport England’s Active People Survey, which is done by landline phones, so it’s a different survey frame. Online research panels tend to get slightly higher percentages anyway for a variety of reasons: people are more engaged clicking away, whereas with a landline survey they are trying to get off the phone.”Paul Burditt, LTA

The Wakefield model, backed by the experience of data analysts in the sports industry, stresses that statistical methodologies are available in a range of complexities and can be selected to match an organisation’s resources and abilities.

“At the time of my hire in 2007, the Brewers had no major segmentation strategy integrated into direct mail or call campaigns. Coming from a direct marketing agency where Recency, Frequency, Monetary Value (RFM) models are used extensively to segment data files, we initially integrated this simple model in with the Brewers data.

“In the baseball world, the RFM model essentially segments data by the number of games an individual has attended in the last year, the dollars spent over the last year or and how recently an individual has attended a game. This was the first stage in using our data to help drive the strategic marketing and ticket sales goals of the organisation.

“After cleaning up the data by running it through data hygiene processes, we began using the RFM model to help form the criteria used to segment individual game-buyer sales leads and to help create the segments for our direct marketing lists and CRM call campaigns. What the RFM model did was help weed out any buyers who tend to buy discounted tickets, but on the surface looked like any other Brewers ticket-buyer.

“Since the initial integration of an RFM model into

Figure 9: Statistical methods of analysis relevant to commercial sport strategy

Statistical method Looking for Variable types X Y examples

Crosstabs (Chi-square) Category differencesX = Nominaly = Nominal

Season ticket holders (y/N) differ by gender (M/F)

ANOVA (F-test) Differences in means between groupsX = Nominaly = Interval/ratio

Games attended (0-82) differs by gender

Correlation (t-value) Relationships between two variablesX = Interval/ratioy = Interval/ratio

People who are more passionate are likely to attend more games

Multiple regression (t-value)Predicting one variable based on two+ variables

X = Interval/ratio or dummy (0,1)y = Interval/ratio

Multiple personal traits (passion, involvement etc.) predict games attended

Source: Professor Kirk Wakefield/Baylor University



Insights can come from many places and many processes, and often from the most unexpected ones. That means there are few hard and fast rules to extrapolating them from the analysis, other than focusing on objectives, keeping an open mind, and focusing on objectives again.

To do this, organisations must look at every piece of analysis – every correlation, every spike, every anomaly – and ask: what does this mean for A, B or C?

This is the intersection at which big data and big experience meet, where the knowledge and understanding of the human capital within the organisation adds value to the analytics function with the magic ingredient that data cannot capture: the context that determines what is relevant and what is not.

Keeping an open mind means giving that context and experience a thorough and continuous sense check. Data can confirm the strength of established strategy, but the most valuable insights are often those that go against the grain and challenge the status quo – using facts to trump received wisdom.

Organisations already succeeding in analytics are those that check their preconceptions at the data science lab door.

4.4 ResultsInsights are the tangible results of the analysis process, but ultimately they mean little if they are not translated into immediate or longer-term actions that will have a demonstrable impact on commercial performance.

The final phase of the process, therefore, is the presentation of results – sharing the insights in a manner that people can understand and that enables them to buy in to the actions being recommended.

Results therefore need to be communicated to the right people, in the right format and with the right recommendations front and centre.

“One of the key principles is being able to visualise data. You need the capability to remove the noise from the data and explore the information – whether that’s through modelling techniques or correlation matrices – but what is key is allowing people to see the relationships within the information. That way

In that situation, identifying trends can be as valuable as drawing out actual numbers – knowing how an element of the business is changing is arguably more important than knowing what the actual size of that segment is.

“Online, for example, is just one particular panel frame, so I tend to look at the data more in terms of trends and relativity of sizing rather than hand on heart saying, for example, that 1.6 per cent of people are playing tennis once a month. I can’t say that is categorically true, but to get a relative sense of what’s going on I can compare that group to people who say they aren’t playing, or to other sports.”Paul Burditt, LTA

4.3 InsightThe identification, sourcing, cleansing, integrating, mining and analysing phases of the data analytics process are all positioned as successive rungs on a ladder that lead the data-driven organisation to new insights about its customers, its business practices and its future ambitions. It is, however, easy to lose sight of that destination along the way.

On the field, sports teams often talk about letting the scoreboard look after itself – keep focusing on the process, on doing the right thing and the goals, points or runs will follow.

Big data does not play by the same rules: at every stage of the process, the data-driven organisation needs to repeatedly ask itself whether or not what it is doing is contributing to its ultimate goal. How does it help put points on the board?

For the unfocused, it can be all too convenient to take the easy option: to collect data for data’s sake, to follow the numbers that make the best chart.

“I think the key word in ‘data-driven’ is ‘driven’. You may have the data but can you drive decision-making and commercial action based on it?”

“A less purposeful data-driven organisation will just have a lot of data – dashboards and systems and metrics all over the place – but fundamentally it won’t make any different decisions because of it.”Matt Rogan, Two Circles



Case Study 6: Using data to form hospitality offer

The Oakland Raiders are the only National Football League team to share a stadium with another professional franchise in a sport other than football (soccer): the Oakland Athletics of MLB – coincidentally, the team at the centre of Moneyball.

While that creates a number of challenges for the Raiders – in particular around stadium configuration and playing conditions – it also offers some more unusual commercial opportunities, such as potentially using the A’s dugouts as field lounge hospitality areas.

“It was our idea, but we didn’t have any idea whether it would be of interest, so we needed to test our hypothesis that it was a good idea that would sell. The two questions for us were whether there was a demand, and if there was, at what price point would it be?”Brandon Doll, Director of Strategic Projects, Oakland Raiders

The Raiders turned to data analytics for the answers, sourcing external data on the premium suite offers of other teams across the major leagues – such as pricing – and surveying a sample of their existing corporate partners on a wider range of suite-related issues.

“That inspired an initial concept, which we then tested in a focus group of nine corporate partners. Some of our hypotheses we confirmed, and some we disproved. For example, we thought most of them would not want to share the lounge with other companies, but in fact we found that many of them actually saw that as added value.

“We had a capacity of 30 people and some didn’t want to make the financial commitment of buying 30 tickets at quite a high price point so were willing to share. That was one insight we

got from the focus group that proved our initial hypothesis wrong.”Brandon Doll

The Raiders recognised that while interesting, the initial findings were largely qualitative and not from a statistically significant sample. They therefore developed a new set of hypotheses informed by the focus group findings and tested them through a 13-question survey sent to a larger set of current and potential partners taken from the team’s CRM database.

“We had a very good response rate and were able to confirm and disprove more hypotheses through that. We were able to ask how many games people wanted to experience the field lounge for, and at what price point, which was really helpful.

“Based on those responses, we were not only able to determine that demand exceeded supply, but also feel fairly confident that we could sell out for a certain price point.”Brandon Doll

The offer that emerged from the analytics process proved hugely successful, with the Raiders selling out their seven home games in 2014 to boost suite revenues by around eight per cent year-on-year. It is projected to add another seven per cent in 2015.

“It helped show the stakeholders in the organisation that while going through the process can be tedious and slower than a more traditional approach, it definitely works. As a result I think we are moving towards a position of combining experience with data to make smarter decisions.”Brandon Doll



“Often when we start working with a club, we can tell them their churn rate for a first-year season-ticket holder is, say, 25 per cent. They will look at you and be surprised it’s high, but the real question in the back of their head is whether that is normal.

“With permission, we do a lot of work with our clients to help them, anonymously, through combined averages, getting a sense of whether that churn rate is higher or lower than we would expect to see in their sport. Without giving away anything confidential, we can give them something to calibrate against.”Matt Rogan, Two Circles

“In the sports industry everyone looks at their own Google Analytics and there is no solid benchmark – it’s a big gap in the market.

“There’s no-one saying, for example, that three per cent growth in that territory on this platform is pretty good. They are all comparing themselves with themselves and what they did last month or last year, and of course the natural flow of performance on the pitch massively skews that. No-one wants to come and look at the team website if you have lost.

“Trying to do analysis when you have that level of variance is very tricky unless you are comparing it with other people. That is why benchmarking is so important.”Richard Ayers, CEO, Seven League

they can then look for the trends that are significant for the organisation.”Jim Tobin, National Sales Executive, SAS Sports Industry Practice

Visualisation is at the heart of data communication and acceptance strategy, which is what any analytics team must develop; they must be able to demonstrate that the new approaches suggested by data can be better than the engrained habits of traditional practice, and persuade the owners of that practice to embrace an alternative tack.

Like any communications strategy, visualisation approaches will vary according to the target audience, but could include graphs and charts, infographics, or traditional reports.

Ideally, the team or person responsible for producing the insights will work collaboratively with the analysts behind them – and the commercial teams in front of them – to present the right data in the right way for both the commercial teams and to gain the upper hand in internal politics.

“As much as we are talking about black and white numbers, human interaction is also important, and there may be a little massaging of egos to be done too.”Chris Zeppenfeld, Charlotte Hornets

Achieving the acceptance that turns insights into actions is largely dependent on being able to prove that the new direction being suggested is better than the one currently being pursued, or that a new strategy will deliver tangible results.

The trend analysis used in tracking sports participation (see 4.2) is one means of demonstrating this, but requires a relatively longer time period to deliver solid results.

The alternative approach is benchmarking, which is being used as an important selling point by data analytics practitioners whose agency status can give them a broader view of the market than an individual team or venue can obtain alone. After all, trailing a rival in a certain area in the league-table world of sport can be a huge driver of willingness to try a new tactic.

The Raiders have achieved results in the hospitality space (see left)

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The Numbers Game Chapter 5: Big data – the dangers

There is another powerful but simple reason why advice to the budding data-driven sports organisation centres on prioritising quality over quantity – not all the numbers will add up.

Thanks to Benjamin Disraeli and Mark Twain, we all know there are “lies, damned lies, and statistics”. Or if you prefer a more contemporary version, Don’t Believe The Hype was the message from Public Enemy in 1988.

While big data can certainly answer a raft of questions about human behaviours and preferences we have previously only been able to guess at, it does not hold a monopoly on truth – let alone wisdom – and the costs of following it in the wrong direction can be high.

Research from Experian Data Quality published in January 2015 found that, on average, companies in the United States believe 32 per cent of their data is inaccurate – up from 25 per cent in 2014.

The cost of that error rate is high: 91 per cent of the survey respondents said their organisation’s revenue was being adversely affected by inaccurate data, in terms of wasted resources, lost productivity or misdirected marketing and communications spend. For incomplete or erroneous customer and prospect data, the average estimated hit was a startling 27 per cent of income.

The smart organisation is aware of the limitations of big data, does not consider and accept its findings in isolation, and always follows the old carpenter’s adage of ‘measure twice, cut once’.

“The successful data-driven organisation tests and trials rather than takes wholesale, up-front risks.”Matt Rogan, Chief Executive, Two Circles

5.1 Limitations of big dataThe risks related to the use of big data as a determinant of business strategy can typically be categorised as stemming from two primary sources: issues with the data itself, and issues with the people analysing it.

Data issuesThe first major issue critics have with big data is the reliability of its raw materials. This is related, in particular, to the field expanding beyond the hard facts of structured data and into the fuzzier regions of unstructured information, and from the physical to the digital.

This can often be a double-edged sword: while cold data is often accused of ignoring the human side of customers and their experience, the user-supplied data that can offer that personal touch is simultaneously criticised as subjective and potentially unreliable as a result.

In a study by software vendor Symantec published in February 2015, 36 per cent of German respondents and 34 per cent of those from the UK said they provide false data to protect their privacy online.

Researchers generally have faith in people’s willingness to tell the truth – particularly on subjects about which they are passionate, such as sport – but also recognise that some types of survey question can elicit responses guided by expected, typically virtuous behaviours rather than actual ones that may be less so.

“Do consumers tell the truth? They do if the questions are not to do with their own self-esteem.

“I have asked questions to thousands of people across the country, across the world even, about very similar products and the reality is responses are typically very similar. There is not a huge difference across teams and across demographic groups when you are talking about the same products.

“But if you ask me a question about how much sugar I ate last week, I’m going to fib a little bit, whether consciously or sub-consciously – I’m never going to give you an answer that dents my self-esteem.”Dr. Michelle Harrolle, Assistant Professor, Sport & Entertainment Management, University of South Florida

Chapter 5: Big data – the dangers

http://go.experian.com/global-research-report-2015

https://www.symantec.com/content/en/us/about/presskits/b-state-of-privacy-report-2015.pdf



traffic that web analytics are unable to track – such as pasting a URL into an e-mail, forum or instant message. In contrast, just 23 per cent takes place on Facebook (see above).

Human issuesWhile veracity of available information – what we might consider the fourth ‘V’ of big data, alongside volume, variety and velocity – is clearly a crucial influence on the potential of any data analysis exercise, so too is what can be characterised as human error in the way we both approach the process and view its results.

“You are somewhat limited by the data you have in that there are all kinds of bias that can be introduced.”Brandon Doll, Director of Special Projects, Oakland Raiders

Two important examples of this type of vulnerability can be motivated reasoning and inaccurate assumptions.

Motivated reasoning is an extension of confirmation bias. Where confirmation bias leads us to look for information that confirms our existing beliefs, motivated reasoning doesn’t just ignore contradictory evidence, but seeks to discredit it even when it is stronger than the data supporting the preconception under threat.

Other objections can have a similarly contradictory feel – typically we either have too much data to make sense of, or not enough to make a reliable judgement from.

Support for the charge of information overkill often calls on a 2013 Harvard University study that found analysts scrambling to identify a significant change in the US economy when their tracking data identified a spike in online mentions of the word ‘jobs’. They found the activity was actually sparked by conversation about the death of Apple co-founder and CEO Steve Jobs.

The criticism of insufficient data, meanwhile, regularly focuses on the incomplete nature of capture systems, with particular attention paid to those of social media, which come with a warning that this type of information often relies on small sample sizes; reflects the news cycle, TV and media as much as individuals’ interests; and is vulnerable to manipulation by marketers.

There is also the degree of difficulty involved in being sure that all relevant data – or at least a representative sample of it – is being captured.

Research by programmatic advertising platform provider RadiumOne carried out in October 2014 claimed that 69 per cent of all online sharing activity worldwide takes place on ‘dark social’ channels, which are defined as sources of inbound

Figure 10: Social media platforms used to share online sports content

(%)

100

90

80

70

60

50

40

30

20

10

0 Worldwide North America Europe UK France Australia

Dark social Facebook All other social

Source: RadiumOne Po.st social data, August 2014

9 11 10 923

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119

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71

5379

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http://info.radiumone.com/rs/radiumone/images/RadiumOne_DarkSocial.pdf

http://info.radiumone.com/rs/radiumone/images/RadiumOne_DarkSocial.pdf



is clear, consistent and cleansed of obvious errors, blanks and duplications.

However, there is only so much of this we can do, particularly on a cost-effective basis, so there comes a point at which the data-driven sports organisation has to recognise that complete knowledge is often impossible, and that it may have to settle instead for accepting and accommodating the existence of, in the phrase popularised by former American politician Donald Rumsfeld, “known unknowns”.

“We did some tracking with Ticketmaster for the UFC (Ultimate Fighting Championship)’s London event, and we identified that without spending any advertising or marketing money – just doing creative content marketing – we delivered 7,000 previously uncontacted and unseen people into the Ticketmaster funnel.

“But how many tickets they actually went out and bought, we don’t know. We do know that £80,000 of tickets came from specifically digital codes inside the Ticketmaster system, but whether they were new or existing customers, you just can’t tell.”Richard Ayers, CEO, Seven League

If we insist on perfect knowledge before we act, then we will be sitting quietly for a very long time. The decision the organisation has to make is over the degree of uncertainty the analysis or modelling process can tolerate.

“The question we often ask is, ‘How good is your model?’ We should ask, ‘Is your model’s performance better than no model at all?’ The answer to the former is probably, ‘Not very good’, while the answer to the latter is, ‘Probably better’.”Dr. Jeff Strickland

So ‘good enough’ is often indeed good enough, particularly in organisations where resources are limited and competing priorities often appear limitless.

If they know where the gaps are, analysts and strategic planners can factor that into their thinking and seek to

Psychologists see motivated reasoning as posing a real danger to our ability to take new insights from data analytics.

“Unfortunately, the mechanisms of motivated reasoning kick in unconsciously from the moment we look at data. As a result, there is a tendency to see what we expect to see. That is, there is a danger that data will not lead us to think differently, but instead to solidify existing beliefs that really should have been challenged.”Dr. Art Markman, Professor of Psychology and Marketing, University of Texas (Harvard Business Review, October 2015)

The answer to this threat is to take a scientific approach to data analytics, based on testing a hypothesis more formally and being willing to reassess your expectations should that initial prediction be disproved by the facts.

Inaccurate assumptions are another risk factor that springs from engrained preconceptions, however this time it is not just about the subject being addressed but about the systems being used to probe it.

In his 2015 book Data Science and Analytics for Ordinary People, predictive analytics consultant Dr. Jeff Strickland contrasts the assumptions and reality of most models of prediction. He writes that analytics models are based on the assumptions that the data is good; the statistical processes are fail-safe; and the models are exact duplicates of the real system or phenomenon. However, in reality, Strickland says the data is rarely good; we rarely implement the statistics correctly; and the models are simplistic abstractions of “real stuff”.

So the world of data is anything but perfect, but thanks to the range of possible strategy options available, it does not need to be. Just as sports teams adapt to imperfect conditions on the pitch, their commercial teams can do likewise in the field of data.

5.2 VerificationThe principal means of avoiding many of the pitfalls associated with reliability of big data sit with the preparation element of the analysis process described in Chapter 4 – ensuring the information to be interrogated

https://www.linkedin.com/in/jeffreystrickland



the interpretation and insight stages of the data analysis process outlined in Chapter 4 – asking if what the data is telling us tallies with other evidence we have available.

This is a question of triangulation and is why data scientists value variety of data so highly (see Chapter 1) – an insight drawn from two distinct evidence bases being more powerful and verifiable than just one.

A data pattern, trend or relationship can demonstrate a correlation, but it is only by cross-referencing and contextualising the proposed link that we can isolate a causation effect from which an insight and ultimately an action can be derived.

“Looking only at total ticket sales numbers can paint a misleading picture. For example, it’s necessary to look at the average price paid per person rather than just total ticket sales numbers for an individual game because of the varying price points.

“For games that are expected to have lower demands, discounted tickets in specific locations may be offered to the public. Therefore, ticket revenue may vary from game-to-game even if game attendances are similar.

“And when comparing season-versus-season data, it’s necessary to take the team’s performance into consideration for context. Team performance drives ticket sales numbers and revenue for obvious reasons. If this information is not considered, a comparison across multiple seasons may inaccurately give credit to a component of a campaign or game-specific ticket sales numbers.”Diny Hurwitz, Data Analyst, Milwaukee Brewers

The first checks will be with other data sources to see if a consistent story will emerge from a different angle, but it is often worth doing some good old-fashioned legwork to verify an apparent fact in a real-world context.

If you can see the problem or opportunity the data is describing, it becomes much easier to recognise its truth or put a finger on any flaw.

“You need boots on the ground experiencing an event; you need people that are familiar with the

compensate with insights from other areas, rather than pour time and money into plugging them at source.

“If someone put a gun to my head and asked me whether I was absolutely confident about my participation figures, I would say no. But they’re good enough, and they enable us to look at the trends.

“If someone isn’t happy with that, I would ask them to come up with a better, and more cost-effective way of doing it. I wouldn’t want to spend a huge amount of cash coming up with an even more accurate number for weekly participation rather than putting together a full insight programme. I don’t see the benefit.

“It’s the difference between having an encyclopaedic knowledge of a sport but not being able to say ‘so what’, versus actually producing an insight programme that equips the big strategic teams to be able to make decisions and optimise what they are doing. Many people would disagree, but I am quite utilitarian about research.”Paul Burditt, Head of Business Insight and Planning, Lawn Tennis Association

5.3 ContextIn the world of digital marketing and communication, content, they say, is king. In the world of big data, however, it is context that wears the crown.

What that boils down to is a simple sense-check at

Putting big data in context is key – Diny Hurwitz, Milwaukee Brewers

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displeased at not being able to get water in the fourth quarter.

“But we couldn’t tell that from the survey because we didn’t ask a specific question, only asked them to rate their experience on a scale of one-to-seven. It was only when we talked to our concessionaire that we understood the reason behind the rating. You can only ask so many questions – you then have to humanise the process to add context.”Brandon Doll, Oakland Raiders

business, familiar with the fans, familiar with the more qualitative aspects of the business and have experience of going through the trials and tribulations – the data can’t always tell you the entire story alone.

“We were looking at a survey on the food and beverage experience at a recent home game and the beverage experience rated low. That was because it was such a warm day and bottled water was in such high demand we ran out; the fans were extremely



The Numbers Game Chapter 6: Using unstructured data

Unstructured and semi-structured data has been made fashionable by its association with social media, and the new possibilities these channels offer of collecting first-hand opinion and sentiment in real-time. However, this data has long been a part of the sports research mix – on the brand side at least – through quantitative and qualitative surveying.

What these types of data bring to the table is an additional layer of insight into the motivations and preferences behind the transactional and activity data that is being accumulated from an ever-widening range of touchpoints.

“Combining this messy and complex data with other more traditional data is where a lot of the value lies.”Bernard Marr, Chief Executive, Advanced Performance Institute

However, despite that obvious value add, the current movement towards big data utilisation has pitched traditional quantitative and qualitative research into direct conflict with social media monitoring. The latter has come to eat the former’s lunch, in the same way that data analytics in general have come to usurp the old ways of business planning. Certainly social media data can cover bases outside the remit of qualitative/quantitative, but the reverse is also true, meaning it is actually possible that the two can co-exist constructively in the data-driven organisation’s toolkit.

“We still run focus groups to understand what the customer is thinking and we really like surveys. Qualitative and quantitative research are still crucial; big data is just another tool.”Garry Adamson, Managing Director, 4Sight Sport & Leisure

“I don’t buy into this idea that big data means the death of market research; it’s just a different data point.”Paul Burditt, Head of Business Insight and Planning, Lawn Tennis Association (LTA)

6.1 Survey dataSurvey-based research typically falls into one of two categories:

• Quantitative: Consumer sampling produces numerical results from defined variables – such as attitudes, behaviours and opinions – that can be manipulated mathematically to estimate future trends or activity levels. Assuming the sample is representative, this can also be used to project the behaviours of a much larger population

• Qualitative: A survey, focus group or participation/observation study is used to gain an understanding of underlying reasons or motivations behind a particular behaviour, trend or problem. This focuses on thought and opinion to delve more deeply into an issue.

In the world of unstructured and semi-structured data, market research practitioners see the principal advantage of surveys over social media monitoring as lying in its more scientific and structured nature. This approach allows defined samples to be selected, controls imposed and hypotheses tested directly through targeted experimentation.

Research design, normative data, stimulus and statistical controls, past experience and quality assurance standards are also cited as means by which the precision of survey results can be guaranteed and distracting noise kept to a minimum.

“I’m biased because we do custom research for many properties, but I see data as additive to – rather than as a replacement for – other methodologies.

“Almost all the new data we have been collecting is behavioural or demographic. It’s one thing to know what people are buying, but it’s a whole other to know whether they are satisfied with that purchase and what their perception is of their relationship with the property.”Haynes Hendrickson, President, Turnkey Intelligence

Chapter 6: Using unstructured data



groups to see the best way of doing it. In sporting organisations that is not happening, and I really hope it increases.”Dr. Michelle Harrolle, Assistant Professor, Sport & Entertainment Management, University of South Florida

Reasons that this kind of activity remains a minority interest in sport are typically led by the old standard of lack of the time, money and knowledge needed to develop a full and meaningful programme.

“Part of the problem is that [qualitative research] is very time-consuming. In sport, at 7.30 on a Friday, the pot is going to drop regardless of what anyone does, so there is not a lot of time for energy and effort to be put into qualitative data and really getting to know the fans.

“Some teams do a good job, but a lot can’t see the benefit because it’s a long sell, because they don’t have the expertise or because they don’t have the time – they get the focus group data and it just sits there.

“Numbers are easy to read, manage and make decisions from. Qualitative data is more cumbersome but it can be more insightful.”Dr. Michelle Harrolle, University of South Florida

“Attitudinal data is the final piece of the jigsaw – once you know the motivation for attending a match or making a particular purchase, that’s when you can start to build some really nice profiles and propositions.”Garry Adamson, 4Sight Sport & Leisure

The verified insights into motivations, desires and expectations that quantitative and qualitative surveys can deliver provide solid evidence for service and strategy development, which can then be linked directly to outcomes on the bottom-line.

“With the results of the marketing research, we started to know what our fans want, how much they spend in a night out, what our main competitors are [in the wider entertainment sector], and some other important information that helps us to know our current fans and the new ones.

“After that, we increased our number of season-ticket holders, we formed a sales team that has better communication channels with our fans, and we started to create a better environment at the stadium. Our fans now have a better experience when they come to a game.”Manuel Portilla White, Commercial and Marketing Director, Club Santos Laguna

Experimentation is considered the most effective form of qualitative/quantitative research in that it allows practitioners to test specific hypotheses in a controlled environment, and then trial a range of possible responses to the issues emerging on a highly-targeted sample of consumers from the key demographics affected.

“We need more experimentation. Instead of asking why people come to a game, let’s go a lot deeper. Maybe we do a focus group, speak to people and see what the problem is, asking whether it’s our sales pattern with questions like ‘how many times should I call the prospect, and what time of day should I call the prospect?’

“Answering that is about diving down further and then running an experiment with two different

Figure 11: Facebook interactions: 2014 FIFA World Cup final and Super Bowl XLVII

World Cup final 2014 Super Bowl XLVII (2013)

Source: Facebook

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Case Study 7: Tracking tennis participation

In 2013, the LTA decided it needed to develop its own mechanisms for monitoring trends in participation in tennis across the UK.

The governing body created an online tracking survey that sees market research company youGov poll around 1,700 people each month, asking questions on topics such as experience of tennis, frequency of play, location of play, playing partners, barriers to play, facility standards and perceptions and awareness of the game.

“That’s been running for nearly two years, and has given us a real grounding because we can now report not just year-on-year but roll all the data up and do some deep cuts in all the different areas to understand where the latent demand is and what the barriers are.”Paul Burditt, LTA

The LTA supplements that work with an additional set of ongoing qualitative research to look beyond the tracking data and pinpoint the factors influencing the trends it describes.

“Tracking surveys are very descriptive and are fantastic for the ‘what’, but they don’t do the deep dive into the ‘why’. That is why we also have a rolling qualitative survey to unpick all the different strands and help us with the business strategy.

“For example, one of our key groups is females playing in the park in summer. We know from the quantitative when they play and how often they play, and we know their age and what they say the barriers are.

“However, that begs a million other questions, and it’s only by getting them to complete diaries – making them go out and play, describing their experiences stage-by-stage – that you can really unpick the journey. You couldn’t do that through quantitative alone.”Paul Burditt

The research has already had a direct influence on a change in direction of participation strategy to put a greater emphasis on public parks over private clubs. It has also given the organisation a firmer base from which to take decisions on a day-to-day basis.

Deliverables now include a monthly KPI report to the executive and more localised versions for the individual heads of regions.

“It’s helped us do an education job for the organisation – ‘everything you ever wanted to know about tennis but were afraid to ask’ – be that the board, the executive or the council, or colleagues in the regional teams.”Paul Burditt

Facebook posts and 28.4 million tweets

• The 2014 FIFA World Cup broke records on all the major social media networks (see left).

The sheer volume of sports content being produced by social media users – not to mention the pace of its growth – has sparked a scramble for properties and brands to understand more about these consumers and how they can develop their commercial strategies to tap into the market effectively.

6.2 Social media dataSocial media has emerged as a platform for sports consumption with a global reach and significance arguably beyond that of any other:

• Manchester United claims to be in direct touch with 60 million fans through Facebook alone, and engaging with 35 million of those every month

• Usain Bolt’s victory in the 2012 Olympic 200-metre final generated 80,000 tweets per minute on Twitter

• The 2015 Super Bowl was the subject of 65 million



“It’s absolutely pivotal to understand why you are pulling the data through. Are you doing it just to get another attitudinal lens on how your fans think, see and behave? Or are you doing it to create an opportunity to do more programmatic advertising?

“In our experience, there are all sorts of opportunities to start understanding and driving growth from fans who might not be on your database. That, for me, is the principal opportunity [in social media research]. As a club, you can start understanding the 400 million people who follow you, as well as the three million you have on your database.

“If you are able to start to understand the 397 million people who have a propensity to follow your brand but are not on your database, you will start having different conversations with the brands that sponsor you and begin to think about other ways to monetise your digital content.

“For example, if I can find another three million people who are prepared to pay £5 a month to access my content, that can present a challenge to some of the rights-fees I might be getting in the second-tier market.”Matt Rogan, Chief Executive, Two Circles

The challenge of social media, however, springs from the same source – its youth and pace of development means connections are harder to make and verify given the lack of historical data to track against, and the huge variety of the unstructured information being produced.

For organisations looking to harness the undoubted power of social, the analysis stage of the process can be far more difficult than the data capture phase, and is likely to remain so for some time while methodologies mature and evolve.

“There are definitely some complexities around social and the main thing is that it’s all new.

“You can gain an understanding of the audience and of the digital services you have, but the trickiest thing is to understand how they integrate, because the analytics systems around all the digital services are far from perfect.

“An awful lot of people rely on analytics provided

“There’s more conversation happening on social media around sports than people attending or watching games on TV. Therefore, the conversation on social will become more valuable than the real estate being sold around games on TV and in-stadium.

“What will emerge is an absolute demand on social media analytics to decipher best practices and to discern the actual value of engagement with a fan. Social media provides the most real-time return on sponsor spend versus a billboard or TV commercial. We’re all fighting to show where it impacts at the cash register.” Kyle Nelson, Co-Founder and CMO, MVPindex

Beyond simple scale, the appeal of social media data lies in the alternative insight it can provide into the passions and mindsets of sports fans.

Where the ideal controlled survey is a lab-test experiment staged in sterile conditions, social media monitoring is research in the wild: tracking its subjects in their natural habitat and observing how they interact with the stimuli of that environment, unprompted and free to roam.

“Traditionally in sport, brands, teams, leagues, and agencies have relied on survey-based data as their chief insight into the needs and desires of their customers. The problem with this method is that it’s time-consuming, not up to date, and subjective.

“Social media is real-time, and people are sharing openly what they’re passionate about. Therefore, with comprehensive social media data we can see an audience’s true passion points, brand interests and loyalty alignment with brands, teams, and athletes among other things. These things represent the audience’s real interests.”Kyle Nelson, MVPindex

Other key points of appeal for social media are its ability to open up new sources of data that may not be captured through other channels and methodologies, and the opportunities this creates to look at new angles – not merely using social to achieve the same objectives that established research formats are already covering.



particular type of engagement only or a particular way in which your website is working.

“You cannot take those numbers at face value; you have to interrogate them, compare and contrast them. You need to move from just capturing information towards taking knowledge and hopefully wisdom from it.”Richard Ayers, CEO, Seven League

by the service provider – so Facebook, Twitter or even Google Analytics. In a way, these are quite enticing for sports bodies because they are free and official, and can be trusted.

“But as time has gone on, people have begun to realise that maybe it’s not such a good idea, because maybe they all have their own agendas of what they want to tell you. Maybe they are focusing on a

Case Study 8: Targeting millennials through social

In 2015, NASCAR’s Chip Ganassi Racing Team (CGRT) turned to social media for the data it needed to help build its fanbase among the millennial demographic.

The team conceived an exclusive concert at its North Carolina base – the ‘Ganassi Sound Garage: a Digi Bash’ – and worked with sports social media intelligence platform MVPindex to identify the right group of online influencers to invite.

MVPindex used its social ranking system to identify and filter Twitter and Instagram users aligned with CGRT’s target group: 15-24-year-olds living in the Charlotte area and with similar interests to the team’s younger drivers.

“Our analysts compared the followers of the influencers with those of the team and the driver groups, looking for an overlap across them.

“Once a target list of top influencers was finalised, a second set of filters was applied for age, location, and certain keywords. This then generated the desired invitee list of the 175-300 millennials needed to fill the event.”Kyle Nelson, Co-Founder and CMO, MVPindex

More than 200 millennials attended the Sound Garage event, meeting the drivers, viewing the cars and

Figure 12: Twitter impressions, race week v Sound Garage: a Digi Bash event

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Typical race week (m) Sound Garage event (m) Source: MVPindex/CGRT, 2015

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engaging with team sponsors as well as enjoying the music. The occasion also produced a large amount of user-generated content and positive online coverage from the attendees, meaning the team achieved significantly higher levels of social media reach and exposure than it would expect in a typical race week.



The Numbers Game Chapter 7: Privacy and data acquisition

The age of privacy was declared over as long ago as 2010, when Facebook founder Mark Zuckerberg asserted that online confidentiality was so no longer a “social norm”.

A month after switching the network’s default setting from private to public, Zuckerberg said that if he was starting the business anew, the idea of hard-wired confidentiality would not have been on the radar, arguing that the shift in status was merely reflecting wider trends in society at large.

“People have really got comfortable not only sharing more information of different kinds, but more openly and with more people. That social norm is just something that has evolved over time.

“Our role in the system is to be constantly innovating and updating what our system is to reflect the current social norms.”Mark Zuckerberg, Founder, Facebook

In the five years since, the direction of travel in attitudes to sharing personal information has remained the same, and if anything has gathered pace. However, while we are certainly sharing more information about more and more aspects of our lives, there are three distinct strands to the phenomenon that sports organisations need to be aware of:

• Controlled sharing: Where the consumer makes a proactive or even spontaneous decision to share data of their choice, such as an image, an opinion or news

• Coerced sharing: Where the consumer feels obligated to provide data to complete a purchase or obtain a service or information, usually prefaced by the phrase ‘fields marked * are mandatory’

• Concealed sharing: When data is collected and used either without express permission or through allowance via the small print that very few people actually read. This is exemplified by the catch-all of

online pop-ups that advise ‘by using this service, you are accepting its terms and conditions of use’.

While controlled sharing may be achievable on social media, commercial transactions – especially online – are dominated by the other two modes.

Consumers are providing data but they are not doing so with any great enthusiasm, and nor are they grateful for brands and websites kindly pre-selecting the ‘yes, please e-mail me information about your products and partners’ option.

A March 2015 study by the University of Pennsylvania’s Annenberg School for Communication found very limited approval for this kind of data capture (see next page).

“Companies are saying that people give up their data because they understand they are getting something for that data. But what’s really going on is a sense of resignation. Americans feel that they have no control over what companies do with their information or how they collect it.”Dr. Joseph Turow, Professor of Communication, University of Pennsylvania

There are also signs that consumers are moving from resignation to indignation over companies’ collection, storage and securing of their data, and not just over its use in marketing communications – major breaches such as the hacking of UK telecoms provider TalkTalk in October 2015 is one of many recent high-profile incidents causing growing alarm.

Sport itself does not have to look far for similar situations. In April 2014, for instance, the Buffalo Bills settled a class-action lawsuit for a total of $2.5 million – paid in team store gift vouchers – after exceeding the maximum weekly number of text messages it had pledged to send to fans signing up for an SMS news service.

Chapter 7: Privacy and data acquisition

https://www.asc.upenn.edu/sites/default/files/TradeoffFallacy_1.pdf

https://www.asc.upenn.edu/sites/default/files/TradeoffFallacy_1.pdf



companies – can be classified: loss of trust, loss of self-determination, discrimination and economic loss.

For sports organisations, the last two of these are arguably the most common and significant, though it could be argued that fans lose a degree of self-determination when the information held about them is used by teams or venues to show them only some of a potential range of ticketing or purchase options available, for example. Loss of trust is also a risk, but more to the organisation than the individual, with fans potentially losing faith as a result of a bad data privacy experience.

Discrimination harms are already front and centre of the dynamic pricing models in use by many high-profile teams and venues, with ticket costs varying according to a range of statistical factors, potentially including some specific to an individual.

If that information leads them to be offered a higher price than another individual, the possibility of discrimination potentially exists – with the result of that discrimination being economic loss.

So what should the data-driven organisation do to limit potential privacy harms and minimise the risk of them occurring? In the UK, the Information Commissioner’s Office has produced specific advice for users of big data, and warns: “The complexity of big data analytics is not an excuse for failing to obtain consent where it is required.”

In October 2015, meanwhile, players of daily fantasy sports games raised concerns over the appearance of employees from the two leading operators, DraftKings and FanDuel, in the leading performers on each other’s platforms. It was revealed that one of them accidentally revealed their access to potentially advantageous information on player selection percentages.

So consumers may now be used to sharing, but they are not always happy about it. Sports organisations looking to capture more customer data and make greater use of it commercially therefore need to be aware of the main issues concerning people over data privacy, and consider how they can foster greater willingness among fans to hand over the information they need.

7.1 Privacy issuesEver since the early years of the postal system, when letters were routinely opened in transit, the information we communicate to others – and the way in which the recipients handle it – has posed a threat to individual privacy. The introduction of successive technologies, from the telegram and the telephone to e-mail and social media, has only created a whole new set of risks each time.

The National Institute of Standards and Technology in America identifies four main headings under which ‘privacy harms’ – negative potential impacts for individuals associated with sharing personal information with

Figure 13: Americans’ attitudes towards data trade-off statements

Strongly agree Agree Disagree Strongly disagree

Source: Annenberg School for Communication, March 2015

It’s OK if a store where I shop uses information it has about me to create a picture of me that

improves the services they provide for me

It’s fair for an online or physical store to monitor what I’m doing online when I’m there, in

exchange for letting me use the store’s wireless internet, or Wi-Fi, without charge

If companies give me a discount, it is a fair exchange for them to collect information about

me without my knowing

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0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%



Figure 14: Privacy considerations around big data

Transparency Organisations need to be transparent when they collect data, and explaining how it will be used is an important element in complying with data protection principles

Variance If an organisation has collected personal data for one purpose and then decides to start analysing it for completely different purposes – or to make it available for others to do so – then it needs to make its users aware of this, particularly if the additional purpose is not obviously connected with the organisation’s primary service

Excessiveness The propensity of ‘big data’ operations to use ‘all’ the data rather than only the relevant data raises questions over whether it breaches data protection principles of excessiveness. Organisations need to consider whether they need all the data they are using

Personal nature If an organisation is processing personal data, in the UK it needs to be certain that processing is compliant with the terms of the Data Protection Act

Impact Organisations holding personal information should carry out a privacy impact assessment looking at how the processing might affect the individuals involved, and what risks it might subject them to

Subject access People have a right to see the data an organisation holds on them, so processing systems should be designed to make this easily accessible to them, ideally online

Source: Information Commissioner’s Office (UK)

“We should only ask for information that only the person you’re asking can give you. You want to save your bullets for when you really need them. We don’t want to be bothersome to our partners and if we are constantly asking them for information we are not being a good partner.”Brandon Doll, Director of Special Projects, Oakland Raiders

Transparency and variance are bound up together, in that a team or venue should tell its fans exactly what it intends to do with their data, and particularly if it will be shared with other parties.

If it won’t, then communicating that fact can be an important means of not just adding to its reputation for transparency but of building trust that the organisation has its fans’ best interests at heart.

“Be up front. Tell fans that you have a data policy and that the more fan data you can get, the better sponsors you can get and the more competitive you will be on the pitch as a result of the increased revenue you will be generating.

“Every club is different, but our recommendation to all of them would be to promise never to sell data to third parties unless they have expressly agreed to that – so opt in, rather than opt out. Some clubs will collect data because they can then sell it to a Chinese tyre manufacturer.

“When you’re trying to build CRM rather than sell sponsorships, you have to be really open with your fans. You have to show trust and be sure you won’t weaken the first time a big sponsor asks to have a look at your data.”Garry Adamson, Managing Director, 4Sight Sport & Leisure

7.2 Willingness to shareSport has one major advantage over other businesses when it comes to customers’ willingness to share – the connection fans feel with their club is considered to make them more favourably disposed towards helping their favourite team if they ask them for opinion or information.

For sports organisations already using data to steer their commercial strategy, excessiveness, transparency and variance are the most important of those elements.

Smart data-driven organisations focus on relevant data as much as big data (see Chapter 2), primarily to help achieve the best outcomes from their analytics. However, this is also done to avoid irritating fans who are either fed up with continually being asked for information or being asked for details that are most likely readily available elsewhere, either in the organisation itself or outside it.

“We try to limit the number of times we are asking our partners and fans to give us information. If we can find information on our own we shouldn’t be asking for it.

http://www.legislation.gov.uk/ukpga/1998/29/contents



with other companies or organisations they buy products or services from – such as banks or shops – but only the smallest possible majority, 51 per cent (see below).

The other 49 per cent, we can infer, were either no more – or even less – willing to do so. That suggests sports spectators increasingly see data exchange as a commercial transaction rather than an expression of fan loyalty, a possibility underlined by the fact that cash discounts were the most popular in-stadium benefit for which information was willing to be exchanged, well ahead of access to premium services or exclusive mobile content.

Willingness to share also varies by age, with the influence of social media having a positive influence on younger users of these platforms.

“We did a study with the WTA (Women’s Tennis Association) and fans across the age groups were pretty much the same, except when it came to social media. The younger generation are ok with what we might call data intrusion because they have grown their whole lives with it.”Dr. Michelle Harrolle, University of South Florida

“I am consistently surprised by the number of people willing to give us information about themselves – survey response rates are consistently above those you see in other industries. It’s because people are passionate about their team, whereas they are not passionate about 3M Scotch tape – they don’t wake up thinking about that the way they do their team.”Chris Zeppenfeld, Director of Database Marketing, Charlotte Hornets

“Fans talk about ‘our’ team and how ‘we’ did last week – they feel part of the group, and there is a personal self-interest and social identity in there. Because of that personal connection they feel more willing to provide personal data.”Dr. Michelle Harrolle, Assistant Professor, Sport & Entertainment Management, University of South Florida

There are limits to that willingness, however, as data fatigue encountered in other areas of life spills over into consumers’ sporting interests, potentially making what might otherwise be another act of engagement or support become just another data coercion chore.

“If you are doing face-to-face research, fans will tell you whatever you want to know. If sports organisations listen, people want to talk.

“When you go online, it’s a bit different. Right now the consumer is inundated with surveys. If you click on a website, you’re asked to give feedback or a review; consumers are getting tired of it.

“Consumers want you to be more like Amazon – they don’t ask questions, they know who you are and help you find what you want.”Dr. Michelle Harrolle, University of South Florida

The increasingly commercial outlook of sports organisations may also be wearing away some of the advantages of loyalty they enjoy. Consumer research carried out by the Mintel market intelligence agency during August 2015 found that a majority of sports spectators said they were more willing to share personal information with a club, team or venue they support than

Figure 15: Venue benefits sports fans are willing to share personal information for

Source: Lightspeed GMI/Mintel, August 2015

Discount on future purchases at the venue

(tickets, merchandise, food and drink)

Access to premium services at the venue (ordering of food and drink, fast-track

queuing, Wi-Fi access)

Access to exclusive event information on a smartphone (player

interviews, travel updates, video clips)

Other benefits at the venue

I would never share personal information with a

sports venue

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%

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Case Study 9: The Jockey Club – rewarding racegoers

The most popular incentive for information sharing identified by the CAB was the loyalty scheme, a method that has been helping UK horseracing venue operator Jockey Club Racecourses (JCR) understand more about their customers since 2011.

JCR is a shareholder in Rewards4Racing, a loyalty programme that allows racegoers to either register directly or when they first buy an advance ticket online to any meeting at one of its 15 courses. They can then accumulate points when they go racing and when they make further purchases with a range of retailers and partner brands, including:

• 4,000 online retailers linked through an affiliate platform

• 30 high street retailers

• three bookmakers

• five racing partners: Racing UK (the subscription TV channel), the Racing Post newspaper, Crowd Racing (a crowdfunding racehorse ownership platform), The Racegoers Club and the Racehorse Owners Association.

“Wherever a horse racing fan is touched by racing with The Jockey Club or York Racecourse, there is an opportunity for them to join the programme and to collect or spend points.

“Every time they transact, there is some form of data to be collected from their activity that enhances our partners’ single customer view.” Josh Apiafi, Co-Founder, Rewards4Racing

Accumulated points can be spent on anything from tickets and memberships to tours and experiences across The Jockey Club’s 15 racecourses – rewards that tick the box identified by Mintel of reducing the cost of attendance. This data also provides The Jockey Club with another layer of customer insight around programme members’ spending patterns and preferences.

The main theme, however, is that points are accumulated as well as spent in racing.

“In terms of points generated, 40 per cent come from racing tickets, 40 per cent through betting and 20 per cent through our retail and racing partners. On a betting front, members can collect points quickly as they receive one per cent of their betting stake back in points.” Josh Apiafi

Alongside money-saving incentives, the programme also encourages members to share more data with JCR through opportunities to go behind the scenes at Jockey Club Estates and get up close to its star personalities, both equine and human.

The incentives, points and otherwise, appear effective: Rewards4Racing has over 900,000 members on its database, having added 340,000 in 2015 alone.

Rewards4Racing points can be used at courses including Aintree

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“It can be something simple like entry into a competition or something more sophisticated like a full-blown loyalty programme, but there should always be a return.”Garry Adamson, 4Sight Sport & Leisure

While Mintel’s research suggests that cost-based incentives work best with fans in the stadium who are likely to be paying most for their passion, for the rising numbers of armchair fans who follow sport via various media platforms, the favoured approach is increasingly based around exclusivity and behind-the-scenes access that gets them closer to their favourite teams and athletes.

Some recent research has indicated that content has only limited value as an incentive for data sharing – but also suggests its potential is far stronger within key sports fan demographics.

The Customer Acquisition Barometer (CAB) 2015, produced by the UK’s Direct Marketing Association, indicated that only 10 per cent of consumers had exchanged information with a brand they knew for exclusive content during the past year. However, among 18-24-year-olds, the figure rose to 30 per cent.

Given the higher levels of engagement enjoyed by sports properties over brands more widely, it is likely that teams and venues can improve on these levels of interest. In 2014, for example, the Indiana Pacers of the National Basketball Association were able to collect data on more than 139,000 fans across Facebook, Twitter, Google+ and its website using a range of content incentives ranging from sweepstakes to competitions and polls.

The influence of social media not only impacts the willingness to share, but also the performance of the channels that can be used for data capture.

So while social media is a happy hunting ground, the technologies that control access to these platforms within the stadium – i.e. the Wi-Fi network – also have particular potential to harvest key data from millennials eager to remain connected to their online social circles during a game.

“There has been a reticence [to register for stadium Wi-Fi access and use the associated matchday app] among hardcore fans who think the club is just trying to get more money out of them, and the reluctance curve follows the age curve.

“Younger people do not seem to have any issues signing up at all – their key concern is having access to Wi-Fi for social media traffic; they will be on-board but won’t download the app.”Pearse Connolly, Head of Business Development, FotballMedia AS

7.3 Incentives to shareWith sports teams becoming less able to rely on blind loyalty to procure data from fans, the question is shifting from whether customers should be offered an incentive to provide personal information to what form that incentive should take.

“Data is like a currency: if a fan gives you some data, whether it is their contact details or filling out a survey, there should be something given in return.

http://www.dma.org.uk/uploads/ckeditor/Customer_Aquisition_Barometer_2015_FINAL.pdf


The Numbers Game Chapter 8: Data acquisition strategies

Nowhere do sports organisations get closer to their customers than in the stadium on a matchday – yet, arguably, nowhere do they know less about them.

Online, fan locations, purchases and opinions can all be monitored and tracked; in the stadium, it can be hard to know even the smallest element of who the spectators are.

“Traditionally people have bought a lot of tickets on the day of the game, which doesn’t encourage data capture.”Peter Oliver, Director, Green 4 Solutions

Advance ticketing solutions are now capturing more and more information about ticket-buyers and their purchasing habits, but that can represent only a small number of those actually attending.

“Our average purchaser buys four tickets, so if [Norwegian football team] Rosenborg has a crowd of 20,000 we only know who 5,000 of them are.”Jon Ola Bergaplass, Head of Technical Projects, Norwegian Professional Football League

All-day events that have regular breaks in play – such as cricket matches, golf or tennis tournaments – can use these

periods to capture data and opinion through face-to-face surveying, but for almost all sports it is new technologies that are the prime enabler of access to information about spectators and their behaviours in the stadium or arena.

Principal among these are connected stadia; Wi-Fi and DAS networks; NFC, RFID Bluetooth and beacons; mobile payment systems and loyalty cards; and smartphone apps. These technologies do not come cheap, however, and it is only in the United States that the fully-connected stadium is becoming the norm.

It is also significant that the adoption of this level of connectivity has been driven not just by its potential for data capture, but rather the need to provide a new generation of spectators with access to web-based media and social networks they take for granted in leisure venues elsewhere; deliver a stadium experience that can compete with the increasingly sophisticated offer of TV viewing in-home; and grow secondary expenditure beyond the gate.

So data analysts again need to work with allies in other areas of the organisation to make a case for the network investments that can unlock the value of the match-going fan.

And in preparing their element of that argument, they need to consider the type and value of the data they want to access, the technologies they need to capture it, and the ways in which they incentivise spectators to share this information with them.

8.1 Target dataThe information data-driven sports organisations typically attempt to capture from fans at an event can broadly be characterised as one of four types: transactional, locational, behavioural and attitudinal.

• Transactional data: typically information about the purchases spectators make within the stadium, whether that be buying merchandise, a match programme or food and drink, or placing a bet

• Locational data: information relating to the movement

Chapter 8: Data acquisition strategies

Norwegian clubs like Rosenburg will benefit from connected stadia

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Wi-FiThe connected stadium model is typically based on a high-density Wi-Fi solution powerful enough to enable a large number of spectators to access the venue’s internet connection. In this setting, mobile phone networks will be unable to offer similar service due to the pressure placed on these by the volume and concentrated nature of demand.

The principal barriers to Wi-Fi enablement are installation costs – particularly in retro-fitting older venues – and concerns over the future-proofed nature of the investment: tomorrow’s mobile network standards will likely prove better able to handle stadium traffic densities and thereby allow spectators to cut out the middle man of the arena operator.

Patterns of investment in Wi-Fi networks indicate that the strength of these concerns vary greatly on either side of the Atlantic.

“Cost seems to be a barrier in Europe; it’s not a barrier in the States. In the States, they see Wi-Fi at an event as being as integral as electricity – it’s a cost of running the event. They don’t even look at it as a capital expense, it’s an operating expense – an incremental cost to make your event go ahead.

“In Europe, it appears the thing that is preventing us moving forward here is the capital expense because they need to see an immediate return on investment.

“I think that, largely speaking, sports rights holders in Europe think very short term, and if the CEO or FD can’t see a return within X they aren’t going to buy into it. It’s really only those who are able to strike deals with value-in-kind sponsors that are able to roll out the red carpet and get it done.”Fiona Green, Director and Co-Founder, Winners FDD

DASThe current available solution to mobile phone service access issues in stadiums is the Distributed Antenna System (DAS), a network of multiple signal receivers sited in areas of maximum demand. It is also used in high-footfall venues more widely, such as airports, hotels and conference centres.

DAS boosts mobile network coverage and capacity and improves reliability of connection in these settings. It is

of spectators around the stadium, in everything from time of arrival and departure to crowd dynamics and dwell times at concessions and museum exhibits

• Behavioural data: information about the activity of the spectator within the venue. There is some crossover with transactional and locational data here in terms of where they go and what they buy, however this strand is focusing increasingly on fans’ online habits within the stadium, looking at the information they access, how they engage with digital services within the arena and the content they create and share themselves

• Attitudinal data: the deeper layer of opinion and motivation that helps explain the patterns identified via the other three streams, offers feedback on quality of experience and helps draw out the strengths and weaknesses of the event offer.

8.2 Venue technologiesIn 2013, the NFL (National Football League) produced a set of minimum standards for stadium connectivity across the league. This was in response to four successive years of attendance decline between 2008 and 2011 and in an effort to offer spectators access to the new media content that significantly upgraded the in-home viewing experience over the same period.

NBA (National Basketball Arena) arenas, meanwhile, have found the same installations a more cost-effective investment to make on the basis of the multi-use nature of these venues, as well as the significantly longer fixture lists of the basketball season.

The NFL invested in Wi-Fi to tackle dwindling attendances

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other Bluetooth-enabled devices to perform specified actions when in close proximity. The beacon’s ability to determine the exact location of an individual device can be used to monitor spectator flows through a stadium and to send a location-based message or offer to their handset.

“Mobile can tell us where you are in the arena, who you are, when you arrived, and through that we can give you better offers and discounts that are relevant to where you are and what you like.”Chris Zeppenfeld, Charlotte Hornets

The principal difference between beacon and NFC/RFID technologies is that beacons can only transmit – rather than additionally receive – information. They also require the installation of a specific app on the device for the transmission to be received. However, they do have a longer-range than NFC/RFID.

SmartphonesWhile many current NFC/RFID solutions are based on smartcards, it is the smartphone that is emerging as the device with the greatest longer-term potential for data capture in the stadium.

Growing familiarity with mobile ticketing and contactless payment puts transactional activity on the handset alongside established habits of media consumption, creation and sharing. The smartphone is also home to the mobile app, which teams and venues are increasingly viewing as the principal digital touchpoint within the stadium, and the enabler of the data transaction between property and fan (see Chapter 7).

“I’m really pushing that we put more effort into making our mobile app better because the smartphone is the best scenario for data capture within the stadium.

“Not everyone will have a fan or membership card but pretty much everyone in the arena will have a smartphone, and the advantage of the app is that not everyone in the arena will have bought their ticket themselves but they can all download the app.”Chris Zeppenfeld, Charlotte Hornets

increasingly being used in tandem with Wi-Fi installations to cope with the exponential growth being seen in data traffic.

In 2014, telecommunications company AT&T said it had 90 per cent of NFL stadiums and 75 per cent of NBA, MLB (Major League Baseball) and National Hockey League venues covered by DAS, and had a target of 99 per cent of access attempts – voice and data – connecting to the network successfully first time.

NFC, RFID, Bluetooth and beaconsThe other key technology for data capture in sports stadiums is Near-Field Communication (NFC), which describes a set of protocols that enable electronic devices to establish radio contact and exchange data by being in proximity of typically 10-centimetres or less.

NFC is the next generation of RFID (Radio Frequency Identification) technologies and is now best known for its enabling of contactless payment systems.

“The more people who pay through their phone the better. That means the purchase is no longer anonymous – you can actually identify that it was Joe Bloggs who bought the pretzel.”Chris Zeppenfeld, Director of Database Marketing, Charlotte Hornets

However, NFC also has a range of other established uses in sports venues, not just through mobile phone connectivity but also through enabled smartcards that are increasingly replacing season tickets.

In the UK, Manchester City’s Etihad Stadium is equipped with RFID tag readers that communicate with the club’s ‘Clever Card’ membership cards. The system streamlines entry to the stadium, allowing the admission of 1,200 fans per minute, and enables fans to buy match tickets and make cashless purchases within the stadium, all of which are tracked in real-time and fed directly into the club’s CRM system.

While NFC technologies are predominantly collecting transactional data, new networks of beacons built on Bluetooth connectivity are creating fresh opportunities to capture behavioural and interest data within the venue.

These transmitters enable smartphones, tablets and



sports organisations is then the extent to which they can get in the way of the spectator’s route to their online destination without annoying them or, at worst, causing them to abandon the journey entirely.

“One of the biggest challenges that we and a lot of other teams face is how to capture data at the point of sale without messing with line waiting times? We can’t ask people to spend five minutes giving us their e-mail address before they can get a pretzel.”Chris Zeppenfeld, Charlotte Hornets

For many venues, the team or stadium app is effectively a walled garden offering access to a pre-defined set of content and services, a model ISP providers abandoned almost a decade ago but which still broadly describes many people’s experience of Facebook.

Some are already questioning that model, and using the data they capture from spectators’ use – or avoidance – of their stadium apps to determine whether they should stick with it or offer unfettered connectivity that may, in the long run, tell them more about their customers’ wider interests and preferences.

“Some people don’t use our app at all; they just give us their data and then go online to do what they have always done. So the next question is, for these people, do you shape the traffic to push them into your service or do we just leave them alone? If fans use [digital football platform] Squawka as their results service, do we just let them carry on doing that over Wi-Fi or do we invite the service into our walled garden?

“We even have some people using [live video streaming platforms] Periscope and Meerkat. Do we ban them, or do we say that is free marketing for us? The more we understand about behaviour the better decisions we can make about the kinds of content we want to give to fans.”Pearse Connolly, Head of Business Development, FotballMedia AS

The potential issue for mobile apps centres on the data transaction element of the proposition – specifically whether the content, services and incentives it offers persuade spectators to provide the information the property seeks in exchange.

While transactional services such as ticketing are accepted as being an obligatory condition of entry to a venue, downloading an app – whether to access event-related content or enable beacon communication – is very much an opt-in opportunity, and as such begs the question for fans: “What’s in it for me?”

8.3 Spectator buy-inThe single biggest carrot for teams and venues to dangle in front of spectators when it comes to data sharing is fast, reliable access to the mobile internet. For example:

• According to analytics from AT&T Park, home of MLB’s San Francisco Giants, the amount of data transmitted by fans during the team’s final play-off series increased by 302 per cent between 2012 and 2014, with 2.1TB pushed upstream during game four of the 2014 World Series

• According to AT&T, in basketball’s NCAA (National Collegiate Athletic Association) Final Four, data usage on the network alone grew from 376GB in 2012 to 667GB in 2013, 885GB in 2014 and 1.52TB in 2015. That represents another increase of 300 per cent over the period – the peak figure being the equivalent of 4.35 million image posts to social media

• In the NFL, the San Francisco 49ers’ new Levi’s Stadium is the most connected in the United States, offering 40 times more bandwidth than any other stadium in the country when it opened in 2014.

It is not just the availability of internet access that the new generation of spectators now expects, but speed of access also – hence the levels of connectivity built in the Levi’s Stadium.

However, speed of access is influenced as much by the controls and protocols that stand at the physical gateway to the network as by the technologies behind it. With Wi-Fi or DAS networks in place, the big question for data-driven

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Case Study 10: Norway’s Connected League

The Norwegian Professional Football League (NPFL) has spent the last three years working with technology company Cisco to develop a collaborative model of fan data capture and analysis based on a countrywide network of Wi-Fi-enabled stadia.

As part of the ‘Connected League’, clubs still need to fund their own Wi-Fi installations but the mechanics of gathering, storing, interrogating and utilising the data produced are all managed from a technology hub in Oslo run by local partner Datametrix. This set-up significantly reduces the overall cost.

In 2015, Viking Stavanger became the first club to trial the system; a target is to get four-to-eight more clubs involved by the start of 2017.

“The individual clubs here don’t have the attendances to make a good case for building out Wi-Fi-connected stadia, so having all the information generated come into a single data hub means the clubs can make a better business case because that side of the cost is centralised.”Thomas Torjusen, Head of Media, NPFL

While enhancing the event experience is an important element of the initiative, it is the desire to acquire and learn from spectator data that is its principal driver.

“Cisco’s initial approach was based on their experience in US arenas and stadiums and so very media and sales driven. That doesn’t work for us; the model for us is data capture and utilisation – that will be the main selling point to the clubs.

“The stadium is one of the best locations for capturing fan data and also for interacting with them over the course of a season once we have them in our database.”Thomas Torjusen

The data capture model is based around sign-in Wi-Fi access and a matchday app, and has been enabled by the near-ubiquity of the mobile phone as the principal touchpoint between fans and their clubs.

“How many people in the stadium have Wi-Fi-enabled devices? We can see from our system and over the season about 90 per cent of people have one with them.

“There has been a huge pivot towards mobile – 40 per cent of traffic to our clubs’ websites is now from mobile devices. It is somewhere we have to be.

“The NPFL has integrated six different ticketing systems into the CRM system as well. However, when enabling Wi-Fi, we also are able to tell who the ticket-holder is, as well as the ticket-buyer.”Jon Ola Bergaplass, Head of Technical Projects, NPFL

The mandatory Wi-Fi access requirement is mobile phone and e-mail address, with an option to supply further information on address, age and gender.

“We use the MAC address from the device for the primary technology service aspects. However, the mobile number is really important because we can do a huge amount with that, and not just in communication – we can

Daniel Arnefjord of Norwegian Premier League team Aalesunds

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get a lot of detail in usage statistics and traffic patterns.”Jon Ola Bergaplass

That data can then be matched up with location-tracking

and behavioural information gleaned from users’ interaction with other technologies installed across the stadium.

“We are trialling beacons in the National Stadium so we can build out people’s passage through the arena, track footfall around concession stands and do some activations around the museum.

“We have been able to do that because our telecoms partner is very keen on looking at beacons for other installations such as shopping centres, so they have partnered with us to look at

our VIP guests and conference centre. It has given us some fantastic granular detail: everything from time of arrival to dwell time in the restaurant, VIP suite and the boxes; whether they looked at our augmented reality videos, or whether they didn’t.”Pearse Connolly, Head of Business Development, FotballMedia AS

The results of the Viking Stavanger trial have been positive, with the club increasing the size of its CRM database by 35 per cent over the season through additions from Wi-Fi access registration alone.

Sign-up rates began at around 10 per cent of spectators but have grown to 20 per cent over the season. The long-term ambition is for 80 per cent sign-in and 40 per cent downloading the stadium app.


The Numbers Game Chapter 9: Data segmentation

Knowledge they say is power, and the intelligence of big data is already strengthening the arm of businesses as they wrestles with consumers to extract the maximum value possible from each and every transaction.

The bigger the data, the stronger the seller’s position is in that negotiation. A 2013 paper published by Brandeis University found that using demographic information to value customers individually and vary prices charged in response could increase the profits of online streaming service Netflix by 0.14 per cent. By including web browsing data in the model, researchers found an increased simulated profit of 1.4 per cent, with some customers paying twice as much as others.

Price discrimination enabled by knowledge of purchasing histories and preferences is already a reality in dynamic ticket pricing active in various areas of the sports industry. However, the customer profiling and segmentation behind these models can play a much wider role in data-driven strategy.

Successful organisations run segmentation exercises in parallel with their big data analysis-insight processes to ensure the actions emerging have maximum impact by being directed towards the customer groups who will be most receptive to them.

The ‘market of one’ is still a long way off, but the market of 10,000 – or even 1,000 is better – than the market of one million, and thanks to data and customer profiling is a reality within reach of almost all.

“We try to create a 360-degree view of fans. That is never going to happen because there will always be more information than you can capture, but we can at least get a panoramic view of the fan.”Jim Tobin, National Sales Executive, SAS Sports Industry Practice

9.1 Breaking down segmentationAdvice around approaches to segmentation typically

follows the playbook of big data analysis more widely: start small, scale up and focus on the markers and differentiators that are most relevant to your objectives.

The first of those – starting small – reflects the increasing accessibility of customer information to sports organisations of all sizes.

“You don’t need to know much to start segmenting. If you have a fanbase of 100,000, the first step would be to look for the key differentiators that would allow you start looking at four groups of 25,000 or even 10 of 10,000.

“You could start doing that as simply as differentiating along lines of age or family make-up – that is an easy thing to do and could let you start customising some of your messaging straight off the bat.”Haynes Hendrickson, President, Turnkey Intelligence

“You can start customer profiling with some very basic information – just knowing who is interested in hospitality could be very valuable for the organisation, for example.”Garry Adamson, Managing Director, 4Sight Sport & Leisure

Chapter 9: Data segmentation

Segmenting by age or family make-up is a good starting point - Haynes Hendrickson, Turnkey Intelligence

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strategy we have considered so far, and underlines the importance of collecting only things that matter rather than everything available.

“We once really impressed ourselves by finding 38 different customer segments, all genuinely different and based on attitudes, lifestyles and demographics. But once we had patted ourselves on the back, we remembered that the point of segmentation is to develop a different strategy for each group and you just can’t have 38 different strategies.

“It’s very easy to overcomplicate things so you often have to compromise; six-to-eight segments can often be plenty.”Garry Adamson, 4Sight Sport & Leisure

9.2 Putting segmentation to workOnce the analysis process has produced some relevant customer segments to work with, the second phase of the process is to integrate that information with the insights and actions being developed alongside.

In its simplest form that means targeting the groups and individuals most likely to respond positively to the offer

The second – scaling up – is a reminder of the range of levels on which data strategy can be formulated and implemented, working up from purchase history and demographics through to behaviours, opinion and sentiment.

“When you get into an advanced state of CRM you are combining things like your e-mail data, ticketing data, social data and match attendance data with your website data to get to what we call the single customer view. This is where we draw all the information we have about an individual into one environment.”Fiona Green, Director and Co-Founder, Winners FDD

“Attitudinal data is the final piece of the jigsaw – once you know the motivation for attending a match or making a particular purchase, that’s when you can start to build some really nice profiles and propositions.”Garry Adamson, 4Sight Sport & Leisure

The third element – relevance – returns to the theme of ‘right data’ that runs through all aspects of big data

Case Study 11: Social completes the Champions League picture

The 2015 UEFA Champions League semi-finals presented an opportunity to enhance customer datasets by adding social media activity generated around the matches.

Sports CRM and business intelligence agency Winners FDD used Twitter cards – which create tweets that allow you to create a media-rich experience for your followers that stand out from the rest of the Twitter stream – to identify the handles of known customers who were following their client’s Twitter accounts. It then used a third party to produce data on their interactions over a one-week period, focusing on relevant hashtags including brands, athletes and clubs.

“Our existing customer profiles became richer and deeper with the addition of their Twitter data:

who they interacted with during a pre-determined period, what they were feeling about our client and their competition brand, and how much reach and influence they had with their tweets.

“Of course, Twitter provides data to users about their followers – but with Twitter cards this happens on an individual level. The end result is that when it comes to communicating with a fan, we now know what content he or she is interested in, and what platform to use; this enables our client to continue to engage with him or her in a way that’s relevant, and that will generate the greatest ROI, and will help ensure his or her loyalty.”Fiona Green, Winners FDD



around which influencers to focus on to best achieve the desired penetration of information.

“The brands that are [using social media] best are realising that the story and medium needs to be different based on the brand ambassador they’re leveraging.

“Rickie Fowler is best when he’s activated on Instagram, and his top performing content is based around humour. Tom Brady, on the other hand, is partnered with Facebook and is the star NFL (National Football League) performer on the platform. Knowing key subtleties about an ambassador and how their audience engages is critical to winning over an audience.

“Too often brands select an ambassador in social based on the number of fans and followers they have. What they should select on is the amount of authority the ambassador has over his or her fans and followers. This means choosing the right ambassador for the brand over the one with the biggest audience.”Kyle Nelson, Co-Founder and CMO, MVPindex

However, segmentation is only half the answer at most. Without a data-driven view of the products, offers or marketing messages that are best able to meet the organisation’s commercial objectives, there is nothing to put to the fan groups identified.

Profiling alone will not tell an organisation what to do, only where best to do it.

“Segmentation is a tricky beast – it’s in the realm of description rather than telling you that you could do X, Y or Z. It’s something on the journey to that but doesn’t have instant answers.”Paul Burditt, Head of Business Insight and Planning, Lawn Tennis Association

being presented, or tailoring that offer to their specific interests.Elements of the offer most likely to be varied by segment include:

• Product: one group could be a primary target for season ticket sales, for example, while another could be targeted for hospitality business

• Content: personalising a website landing page for particular user groups can aid favourability by offering a shortcut to news or views that chime with their interests

• Medium: do they respond best to e-mail, SMS or social media?

• Message: tone of voice, subject matter and presentation can all affect how a communication is received

• Timing: knowing the buying habits of a particular fan segment makes it easier to predict when they will be most open to the idea of another purchase.

“Once you have the single customer view, you know whether you should be sending someone an e-mail, a push message through a mobile app, or interacting with them on social.

“If they have previously bought tickets, how soon before kick-off did they buy them? If we know that, when should we send a message to remind them?

“Did they buy the last replica shirt? If so, we’ve got a new one coming out so send them a prompt. All those types of actionable insights you get from data can inform your strategy, inform your tactics and inform your next e-mail campaign.”Fiona Green, Winners FDD

The more sophisticated the profiling, the more possible it is to drill this targeting down further – to segment the communication channels being used, for example.

That could mean following up a decision to communicate an offer to a particular fan group using social media with another decision on which social network to use. That could then lead to another decision to be made

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Case Study 12: Segmenting the Challenge Cup final

The Rugby Football League (RFL), the governing body of the sport in England, has been developing a data-driven approach to its commercial and marketing activities over the past five years and focuses much of its CRM attention on building a single view of its customers from a wide range of touchpoints. These include ticket buying, volunteering, local club play and coaching.

In 2015, the RFL worked with 4Sight Sport & Leisure to increase ticket sales for the 2015 Challenge Cup final by segmenting its potential spectator base into a number of groups that could then be targeted more effectively through knowledge of their demographic character, interests and past purchasing habits.

The segmentation used both an RFM (recency, frequency and monetary value) approach and analysis of important characteristics such as:

• Preferred time of buying tickets

• Method of buying tickets

• Geography

• Number of big games attended

• Team supported

• Type of games attended in the past

• Age

• Gender

• Number of tickets typically purchased.

The exercise identified three key segments based on three different buying phases across the event’s 11-month ticket sale window: early (well in advance of the final), middle (before the quarter-finals) and late (after the quarter-finals).

Within these, 14 further sub-segments were identified to create a targeted set of potential customer groups again, based on the drivers of their purchasing decisions. These included:

• Group bookers: those with a higher propensity to purchase more than six tickets

• Regular Challenge Cup final bookers: those that attend every year no matter which teams are playing

• Love Wembley: those that have identified in previous surveys that they enjoy a day out at Wembley

• Fanatics: those that attend only if their team is in the final

• Big game attenders: those that appear to enjoy big rugby league games

• Sports fans nearby: those that appear to enjoy major sports events on their doorstep.

The segmentation enabled the RFL and 4Sight to create personalised marketing campaigns using content and delivery channels to which the recipient was considered most likely to respond. For example, promotions during the ‘early’ ticket buying phase were aimed specifically at fans living in the south of England, focused on group offers and played on the prestige of Wembley Stadium, as these were identified as key drivers of interest among people buying at this point.

As a result of the more targeted approach that the segmentation enabled, response rates to email campaigns doubled year on year, with unsubscribes down 30 per cent. Overall ticket sales, meanwhile, were up by 2,000 on 2014.

Leeds Rhinos thrashed Hull KR in the 2015 Challenge Cup final

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The Numbers Game Chapter 10: Shaping communications

Over the past 20 years, communicating with sports fans has become easier through multi-channel TV, the internet, social media and smartphone technologies. These platforms have given clubs and properties an always-on connection to their supporter base, and extended their reach to global scale.

But communicating with fans has become harder too. Media proliferation has also brought media fragmentation – more channels, more platforms, more choice – meaning competition for consumers’ time and money is intensifying almost by the hour.

As long ago as 2001, Thomas Davenport and John C Beck warned – in their ground-breaking book The Attention Economy – of the limited “human bandwidth” available to process all the competing forms of media vying for our consideration. This was, of course, before the world of Twitter, Facebook and youTube existed.

Attention, they argued, has become “a more valuable currency than the kind you store in bank accounts”.

“The problems for business people lie on both sides of the attention equation: how to get and hold the attention of consumers, stockholders, potential employees and the like, and how to parcel out their own attention in the face of overwhelming options. People and companies that do this succeed.

"The rest fail. Understanding and managing attention is now the single most important determinant of business success. Welcome to the attention economy.”Thomas Davenport and John C Beck (The Attention Economy, May 2011)

One key response to this challenge has been the rise of content marketing, in which brands have started to recognise that they must provide the consumer with something of value to command their attention. In the age of fast-forwarding commercial breaks

and ad-blocking online, just firing out messages is no longer enough.

Nor, for that matter, is spewing out ‘content’ on a scattergun basis. To offer the value needed to engage the consumer, content needs to be appropriate to their needs, interests and preferences.

In short, communication needs to be targeted, which is where data comes in – using knowledge of a brand or property’s audience segments to determine the subject matter, messages and delivery mechanisms that will attract their attention and persuade them to read, watch, click or buy.

In content marketing terms, sports organisations are already significantly advantaged in that fans are hungry for information and insights about their favourite athletes, teams, venues and properties.

However, they are not immune to fan cynicism and face competition from a vast array of well-resourced media

Chapter 10: Shaping communications

Figure 16: Positive responses to personalised online content among millennials

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Tailored to their location

Tailored to their cultural

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• A static version promoting three nationally-broadcast matches, sent to subscribers on whom data is most limited

• A personalised version based on the league’s knowledge of the recipient’s club affinity and geolocation, which features imagery and information tailored according to these variables.

E-mail response rates, MLS says, increase by 39 per cent when the personalised e-mail is sent ahead of the generic one.

This campaign plays on one of the most important themes of content marketing in that it uses responses to past communications to inform the focus of future ones; it constantly monitors fans’ interest in the content it is feeding them, and uses that data to create a better offer next time.

“When you send an e-mail campaign, you can analyse the results to inform the strategy of your next e-mail campaign. When you put content on your website, you should be constantly tracking Google Analytics to identify which content is working and which content isn’t, what the user journey is like, where they come on and where they drop off.”Fiona Green, Director and Co-Founder, Winners FDD

“We will analyse the hell out of how many things were published, when, by whom and on what subject; how

outlets all fighting to reach the same audience. This means they need every tool at their disposal to get their content marketing and communications right.

Knowledge of fans – which a sports organisation can extract from the data it holds on them – should give it a head start in identifying the two key elements of its communication strategy: the right content and the right channels.

10.1 The right contentThe process of identifying the type of communication or media content that will attract the attention and enable the engagement of sports fans is another manifestation of the segmentation approach discussed in Chapter 9. Demographics and transactional data will, again, be a starting point, but content segmentation is as much about what consumers like as about what they do or who they are. This means attitudinal data, social media metrics and web browsing become much more important identifiers of preference.

This is arguably the field in which digital data has greatest potential, given the direct relevance of the information being collected to the task in hand – finding out what people like and then giving them more of it – and its comparative ease of collection.

For example, using a cookie and pixel tag strategy, Mississippi State University was able to profile and segment more than 500,000 fans during a single five-week period of the 2014 college football season. It then began tailoring the school’s interactions with them accordingly.

As with other segmentations, targeting content can work on a comparatively simple level as well as through more detailed analysis.

Major League Soccer (MLS) reports success in e-mail marketing through segmenting its recipient base with just two dimensions and serving up one of only two variations of its This Week in MLS newsletter as a result.

The factors that determine the version of the email newsletter a fan receives are how their information was acquired – for example through web registration or a competition entry – and the ways in which they respond to the MLS communications they receive.

The two variations of newsletter then e-mailed out to them are:

Figure 17: Visiting professional and collegiate sports team sites, by platform

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Case Study 13: Tackling season ticket churn at Leicester CityEnglish football club Leicester City took a data-driven approach to season ticket sales for the 2015/16 Premier League season, with a specific focus on improving communication with groups identified as at risk of not renewing.

The club worked with CRM agency 4Sight Sport & Leisure to identify members of this group based on common characteristics associated with non-renewal by season-ticket holders in previous years.

‘At-risk’ supporters were considered to include:

• Those who had held a season ticket for one year only

• Fans whose date of birth showed they were due to move into a higher price category

• 18-24-year-olds

• Residents of particular postcodes

• Supporters living outside the club’s primary catchment area.

E-communications activities were then tailored to have specific relevance to these groups, using a range of branded campaign templates optimised for the mobile devices the data indicated would be heavily used by recipients. For example, fans moving price band were informed about direct debit payment options and allocated a personal follow-up phone call if they did not renew within a specific period.

The focus on relevancy helped achieve some important results, including:

• E-mail open rates of more than 60 per cent in the initial campaign

• Mobile e-mail open rates of more than 50 per cent

• Season tickets selling out inside two months and without having to go on general sale

• Sixty per cent of season ticket sales coming as a direct result of engagement with one of the club’s renewal messages.

most appropriate, according to the target group and the objectives of the organisation.

The idea of the right channel, however, extends beyond the delivery pipe – it is the best content environment within the chosen platform also.

So if the best route to a particular target group is via mobile, the data-driven organisation next needs to consider whether its content will be best served via its own responsive website or app, or via third-party networks such as social media.

In many cases, the right option will be to tap into the existing ecosystem rather than try to reinvent the wheel.

“We often see people spending a lot of money building their own apps and then finding that fans don’t look at them because the same information is available from a million other sources. Unless you can find some really exclusive content there is no reason for them to go somewhere new.” Garry Adamson, Managing Director, 4Sight Sport & Leisure

the audience reacted and on which platforms they did so. We then take all those filters and mush it all together to come up with a very clean view of what the output is and what is actually working, and therefore as a result what an organisation ought to be doing.”Richard Ayers, CEO, Seven League

10.2 The right channels“Fish where the fish are” is the motto of many data-driven marketers, and one that has particular relevance for content marketing. While segmentation tells the fisherman where to find their prey, content is the bait that gets it onto the hook.

To mangle the metaphor slightly further still, the successful fishermen also require the right equipment for their catch, and ideally to have that on board before leaving shore to avoid any nasty surprises out in the ocean.

For data-driven engagement with sports fans, the right equipment means the right communication channel, which is increasingly online and increasingly mobile (see left).

Once again, data will also identify which channel is



possibilities for push messaging in stadiums, the more decisions sports organisations will have to take about when and how often they seek to communicate with fans.

“If a fan with a mobile app approaches a concession stand, it would be great to extend an offer or a message that is very personalised to them. You don’t want to be creepy but if you can extend a relevant offer the impact is much better than sending out messages blind.”Jim Tobin, National Sales Executive, SAS Sports Industry Practice

“The best way to reach fans on social media is to have other fans tell them.”Richard Ayers, Seven League

10.3 The right timeThe final piece in the jigsaw of data-driven communications is timing – sending the right message to the right person, in the right way, and at the right time.

Timing, however, is arguably the hardest part to get right: what is opportune to one person is an interruption to the next.

And with technologies such as beacons creating new


The Numbers Game Chapter 11: Big data’s future

The big data of today will be even bigger tomorrow: the importance of data analytics in commercial sport strategy is set to keep on growing as the volume, variety and velocity of information available continues to expand at pace.

Advocates of the practice believe data will eventually create a whole new model for the way sports organisations operate commercially and interact with both their fans and their business partners.

“So far, data has been seen as a means to put bums on seats in stadiums – ultimately it will become the enabler of a new breed of sports property that can take more control over its commercial, broadcast and matchday revenue streams.”Matt Rogan, Chief Executive, Two Circles

That future will not arrive overnight, however, given that the majority of sports properties working with data are still only scratching the surface of its possibilities.

Many of the barriers to becoming a fully data-driven organisation discussed in Chapter 2 will persist, with historic cultural resistance to change and the continuing prioritisation of investment into on-field operations widely seen as the most difficult to overcome.

“The Moneyball effect is pervasive throughout the playing department now, with almost all clubs using really sophisticated data analytics across everything from injury risks to formations and transfer policy.

“That level of rigour will become standard in commercial areas of sport too. It’s starting to happen, but only slowly. In our industry, investment goes into the playing side first because people think the biggest difference is made by buying a new striker, not a new data analyst.”Garry Adamson, Managing Director, 4Sight Sport & Leisure

Technology will remain the principal enabler of the expansion of data analytics in terms of the range of information to which sports organisations will have access, but the pace of meaningful change will be largely determined by the speed with which we are able to make sense of the numbers mounting in front of us.

“Analytics take time to catch up. We’re getting social, but what about chat and messaging? Snapchat was doing deals with media partners, but it was literally six guys in a room and they hadn’t built an analytics system.

“If you are a very young company – which a lot of these are – building a full-blown analytics system is very difficult.”Richard Ayers, CEO, Seven League

Over the next five years, those working in the field expect to see significant developments in a number of areas, but particularly:

• the opening up of more data sources

• the rise of the data warehouse

• real-time analytics

• a new focus on sponsorship

• the democratisation of big data.

Chapter 11: Big data’s future

Big data will become much more than about putting bums on seats – Matt Rogan, Two Circles

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experience, information that can offer a whole host of new insights into how that should develop in the future.

“Technology around facial recognition is advancing to the point of being able to see how a person’s expression changes in response to an advert.

“In sport, you can ask someone what motivates them to come to a game, but if you can actually be with them and watch them in the stadium – see when they are happy and when they are not, look at their body language and visual cues – that opens up a whole new range of possibilities.”Dr. Michelle Harrolle, Assistant Professor, Sport & Entertainment Management, University of South Florida

11.2 Data warehousingOne of the major challenges currently facing data-driven sports organisations, and particularly those who are new to the game, is pulling together the disparate sets of information to which they have access.

The principal issue here is the need to create a combined pool of data in a consistent format that enables analysis tasks to be performed across multiple sources and systems on a comparable basis.

While many organisations have attempted to achieve this agglomeration of data within their existing CRM systems, the leaders in the field are concluding that larger, purpose-built solutions are necessary, with the data warehouse primary among them.

A data warehouse is essentially a relational database designed for query and analysis rather than transaction processing, and which has specific capability to consolidate information from multiple sources.

Analytics professionals identify the building of a data warehouse as the fundamental next step in the evolution of the practice for sports organisations, as all further processes hinge on the availability of the consistent, comparable and verified information it provides.

“The immediate future is data warehousing coming online and un-siloing the data. That’s the one problem everyone has – one thing doesn’t talk to another; we need to get 12 silos down to one. All

One thing that will not change, however, is that the human factor – in everything from providing the data, to interpreting and acting upon it – will remain central to successful strategy and growth.

11.1 Data sourcesThe new data sources that have opened up for sports organisations in recent years have been primarily digital, as it is online where the pace of ‘datafication’ has been fastest, and costs of capture lowest.

Stadium-based technologies (see Chapter 8) will continue to catch up, but some of the potentially most important developments of the next period of analytics will be applicable both online and off.

Possibly the most significant of these will be visual recognition technologies, which will have a number of applications that can capture both behavioural and, for the first time, sentiment data.

Online, cookie and pixel tag systems are already tracking what users do, but now teams and properties are also able to identify what they see when they look at a webpage.

“When we have a press conference, we may have a sponsor who will have its logo on the backdrop. We have used visual attention software to determine the likelihood that in the first three-to-five seconds of the conference a fan seeing that backdrop will recognise and identify the logo, as well as how it is going to stand out on the page of our website they are viewing it on.

“Then we can take the video views, and from that determine we can get significant traction even if someone views for only a few seconds. It shows we are thinking about things in a much more scientific way to drive value.”Brandon Doll, Director of Special Projects, Oakland Raiders

In the stadium, visual recognition technologies can add to the existing bank of information being captured around spectator flow, but the next frontier is tracking how individuals react to specific elements of the game



For the data-driven sports organisation, real-time analytics are enabling push notifications to spectators according to where they are in the stadium – as identified by beacons and GPS data – or using RFID to personalise their greeting on reaching the turnstile.

This is primarily an enabler of the segmentation/personalisation benefits looked at in Chapters 8 and 9, and has major additional potential online, where the real-time technologies behind programmatic advertising are ready to be applied to web content delivery more widely.

“Programmatic advertising is already with us, but in the future there will be programmatic serving of content, allowing computers to make decisions about what content as well as advertising is shown, and in the process do a lot of heavy-duty profiling of users.

“This is a really interesting area because with programmatic you can start to start to identify audiences, and you can start to identify behavioural patterns across not only your site but other sites and other parts of the internet.

“Then it starts to become a really important tool, and one of the reasons for that is because sponsors, advertisers and people selling tickets really want to understand the details on audiences. Programmatic can deliver quite a high degree of that.

“Where once you might have had an e-mail database understanding, you can now have a programmatic understanding of a user’s behaviour online. That’s very powerful. It is an expensive thing at the moment but in time it will become standard practice.”Richard Ayers, CEO, Seven League

11.4 A focus on sponsorshipThe focus of data-driven commercial sports strategy has so far been firmly on the fan side of the revenue mix – looking primarily at ticket sales, merchandising and secondary expenditure beyond the stadium gate.

However, the leaders in the field are increasingly looking at how data can help them raise the value of other income streams.

While many believe data has the long-term potential

teams at the forefront of analytics are working on that now. That is a one-to-three year thing but after that we will start working out what to do with the profiles it unlocks.”Chris Zeppenfeld, Director of Database Marketing, Charlotte Hornets

11.3 Real-time analyticsVelocity is one of the key identifiers of big datam and relates to the speed with which the information in question is generated and the speed with which it can be captured.

Both of these are accelerating rapidly as new technologies enable faster performance by the systems and services generating the data, and by the software solutions being built to process and analyse the data.

Already we are at the point of real-time data capture and processing, and moving swiftly towards real-time reaction to the opportunities being created.

“Today the really successful companies understand where their customers are and, perhaps more importantly, what they are doing and where they are going. They know what is happening as it is happening, and they allow that information to guide their strategy and decision-making.”Bernard Marr, Chief Executive, Advanced Performance Institute

“We are thinking in a much more scientific way to drive value” – Brandon Doll, Oakland Raiders

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https://www.thinkwithgoogle.com/programmatic-guide/



use of analytics to enable the teams to answer that question for them.”Haynes Hendrickson, President, Turnkey Intelligence

11.5 Democratisation of dataOne of the major barriers to the wider utilisation of data throughout the commercial departments of sports organisations is the complex nature of the – often raw – information being presented to them.

The ownership of data by the IT and analytics functions can mean it exists in formats that make sense only to those with the advanced skills found within these specialisms. This often means sales and marketing teams must go through time-consuming and attention-intensive processes to access the information they need to inform their own strategising.

Chapter 4.4 has already identified the need for analytics systems to produce insights that are not just actionable but also comprehensible to a wider audience of non-specialists throughout the organisation.

On one hand, that is essentially seen as requiring data specialists to add new proficiency in translation, interpretation and client service to their existing skillsets.

“It’s like IT was 20 years ago, when the IT guys used to be about computers alone. Then they had to interact with people, they needed to become customer-friendly and start delivering some customer service – there was a shift.

“You will see that in sport, too – you are going to have data analytics people who are going to have the technical background, but are going to understand research methodologies too – the art of asking the right questions.

“You are always going to have specialised data analytics, where people are using very mathematical, scientific models, but day-to-day you need someone in there who can do both of those things.”Dr. Michelle Harrolle, University of South Florida

On the other hand, however, analytics systems will become more user-friendly, enabling more people to ask specific questions and extract relevant insights without the assistance of the current gatekeepers to that information.

to radically alter the established media rights models, the immediate opportunity is seen as being around sponsorship.

There are still privacy issues to consider around sharing customer data with commercial partners, but more and more organisations are using data to support their sponsorship offer, both in terms of quantifying the audience their partners are able to reach and helping them develop the messaging and activations that will resonate most strongly with their key target groups.

“I think there is a great opportunity for continued growth in sponsorship analytics, not only from the property side to justify an elevated spend from a partner, but also to let the brand hone in on what they are getting by doing one particular activation versus doing another. They need to know what is going to resonate best with that fanbase.

“The top 10 spenders in sports advertising are doing that already, but there is such a wide swath of mid-size companies who like the appeal of partnering with a sports team, but at the end of the year probably aren’t honing in on exactly what they get for that spend. So there’s a lot of room for the

Data has the immediate potential to revolutionise the structure of sponsorship deals

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and personal bests. In the playing arena, it is the numbers that really count, but on the commercial side of the industry, sport remains a people business and its USPs are built around the human qualities of passion, emotion, pride and community.

So the challenge for the data analytics world of the future is how to balance the human and the machine to ensure the numbers of the sporting experience feed its uniqueness rather than leave it constrained, homogenised and plasticised.

The winners will be those who combine the quantitative with the qualitative to strengthen the personal connections that make sport special.

“Predicting the behaviour of fans will become much more accurate, but analytics will only become more effective if it retains a human edge – they can become much more sophisticated but it mustn’t become too automated in the programmes they produce.

“Fans are human and you have stay authentic to reach them.”Garry Adamson, 4Sight Sport & Leisure

Data visualisation and benchmarking are two methods by which analytics specialists are helping their generalist colleagues understand the numbers they produce, but soon the focus will switch to direct access to these tools for sales and marketing teams themselves.

In the same way that the content management system has opened web content creation to almost anyone, so too will new analytics systems democratise their use in the future.

“The next big thing in sport will be allowing more people to use analytics, so any business user or coach can walk up, access the system and create their own predictive models without needing a degree in statistics.”Jim Tobin, National Sales Executive, SAS Sports Industry Practice

11.6 The human factorWhen people think of big data, they tend to think in numbers: how many, how much, how often. And the tendency to do so is potentially stronger in sport than in many other businesses: results, records, league positions

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