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Analytics in Sport Marketing

Ceyda Mumcu and Gil Fried University of New Haven

The use of analytics has been growing throughout the sport industry. Although the concepts of analytics and big data are frequently used in the sport industry and highlighted in numerous media outlets, sport management students often do not have a strong understanding of why and how analytics are important for their future career, especially as it relates to sport marketing. This case study describes a fictitious student’s desire to be an intern in the analytics department at Major League Soccer and the student’s interaction with an industry professional who is an expert on customer relationship management and marketing analytics in the sport industry. The study provides information on how and why analytics are used in sport marketing and how data can be used to make decisions.

Keywords: analytics, segmentation, sport marketing

Hello, my name is Beth and I am a junior at “University of K,” studying sport management. I want to take you through my journey of learning about sport analytics and what analytics means to me now. This semester, I am enrolled in a junior-level sport marketing class. I just completed an assignment for the class, along with several classmates in my group. My assignment was to distribute a survey at a basketball game and then use that data to find what the students in attendance wanted in a game. We administered a 10-question survey at both a men’s and a women’s basketball game. We had 40 students complete the survey with the majority of students indicating that they wanted free items to entice them to come to the games, such as free food. The demographics showed that men were the primary attendees at both games and that most attendees were freshmen or juniors. This was the first survey I had ever undertaken, but I felt I learned a lot from the exercise. The assignment also gave me the chance to apply some of the statistics knowledge I learned in a statistics class I took last semester.

As I reviewed my notes for the next class, I remem- bered my professor saying that Charlie Shin, the senior director of Customer Relationship Management (CRM) and Analytics at Major League Soccer (MLS), was going to be in class as a guest speaker to discuss how MLS uses CRM and analytics to make marketing decisions. I reviewed the information my professor shared on CRM and market research that night before going to bed because I am interested in an internship in sport

marketing. In the morning, I went to the sport marketing class with excitement, but without much insight in what to expect. The readings shed light on big data and how important data were, but I did not really know how to apply that concept to making marketing decisions. The only thing I thought about on the way to class was that our small survey sample size of 40 students really was not anywhere close to being big data.

Class began with the professor introducing Mr. Shin. After the brief introduction, Mr. Shin started talking about his background and how he got into the field. Although we were expecting to hear what he does at MLS, he started asking the students questions:

Mr. S: “Have you taken any statistics classes?” I raised my hand to respond and said: B: “We are sport management majors and our

degree requires a math class and one stats class.”

As I answered the question, I thought he was trying to understand if we had background knowledge to under- stand what he was about to share with us about his job. I knew that market research required understanding statis- tics. As I was expecting him to get into CRM and analytics at MLS, Mr. Shin asked a follow-up question:

Mr. S: “What type of statistical analyses did you learn in those classes?”

He was looking at us and wondering who was going to answer. The question was not necessarily directed to me, but I felt like I needed to answer. I remembered that the classes were not easy, but I could not really remem- ber anything. I also remembered several terms (mode, mean, and median) and learning about relationships and comparing groups, but I could not remember the names

Ceyda Mumcu and Gil Fried are with the Department of Sport Management, College of Business, University of New Haven, West Haven, CT. Address author correspondence to Ceyda Mumcu at [email protected].

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CASE STUDIES

of the analyses or any detail. I was hesitant to speak up because it seemed like he was going to continue asking questions, and I was not sure if I had the right answers. With no one responding and Mr. Shin getting impatient, I responded again to make sure the guest speaker did not think we did not know anything.

B: “I learned about mean, median, and mode and I also remember comparing groups and looking at relationships.”

I looked around the room and saw the sigh of relief on the faces of my classmates. I was hoping that he would not ask any more questions, but he did.

Mr. S: “How could those statistical analyses be used in sport marketing?”

And this time, I really did not know what to say. The classes I took were taught by the quantitative analyses department and they were not taught in the context of sport industry. While I was thinking of an answer to his question, I remembered the lecture from last class session and quickly flipped through the pages in my notebook. I pretended to look busy so he would not pick on me. Luckily, Jack finally raised his hand to answer the question.

J: “We can identify if our fans are male or female, their age and income etc.”

Mr. S: “Yes, that is right. You are talking about basic fan profiling, which is very important for us and for any sport organization. Marketing begins with knowing your customers, and without understanding who your customers are and what they want from your product and organiza- tion, it is impossible to be relevant and satisfy customers.”

He went on and started lecturing on CRM and how to use marketing analytics in the sport industry and specifi- cally at MLS. I was busy taking notes on what he was saying. He said that the reason a team drafts a certain player is typically obvious: to fill a need (or needs). Similarly, the reason that teams pick certain logos, colors, fonts, advertising campaigns, and promotions is the same exact reason—to fill a (marketing) need. These decisions are not made randomly, but rather rely on insights derived from market research, analyses, and CRM system.

Mr. Shin elaborated that CRM and analytics have grown dramatically across all sport leagues. In 2014, more than 75% of all National Basketball Association (NBA) teams were using a CRM system, and every major league sport team was expected to have a CRM system by 2016 (Zeppenfeld, 2014). CRM systems gather information on customers at every possible touch point, and the databases store information on customer demographics, psychographics, and product usage including all transactions, inquiries, and interactions between the customer and the organization. Various analyses are performed based on the availability of data and the sport organization’s goals. For example, a sport team can perform cluster analysis to segment their fans, estimate customer lifetime values, generate leads via

look-alike models, predict retention of existing fans, and measure performance of marketing activities. Insights derived from data analyses allow sport teams to engage with fans effectively by sending the right message to the right person at the right time via the right platform (Green, 2015), build a fan base and solidify fan relation- ships, increase sales and, therefore, revenues, and make informed business decisions (Sutton, 2013).

According to Mr. Shin, MLS started to build its CRM system in 2007. MLS’ primary goals are to drive the growth of MLS fan base through introducing new fans to MLS, moving noncommitted and casual fans to greater avidity, and converting aspirational fans to avid fans. In other words, MLS aims to move their spectators and fans up on the sport fan escalator, build a larger highly identified fan base, and ultimately have a loyal (often called raving) fan base (Klie, 2012). In addition to developing a fan base, MLS uses analytics to segment the market, develop different tactics for each segment, and evaluate the performance and effectiveness of these tactics and marketing activities. In essence, the goal is to be relevant to the fans and provide the right information and product via the right tools.

For instance, MLS performs value-based segmenta- tion via cluster analysis. Fans are grouped into segments based on their customer lifetime value, and then a profile for each segment is developed by adding demographic, geographic, and behavioral variables. The league takes this information one step further and creates look-alike models to use in lead generations. In the process of new customer acquisition, MLS approaches potential custo- mers who look like their valuable fans with the hope that new customers will become valuable fans in time.

Mr. Shin continued with another example on how information drawn from their CRM system is used in designing personalized and relevant e-mail campaigns. The CRM system stores information on fans’ affinity with teams and their favorite players. Instead of sending fans general information about the league, upcoming games, or league-wide merchandise sales, they decided to send personalized e-mails to each fan with informa- tion and promotions about the respective team the fan identifies with. Following the e-mail campaign, they measured the performance of personalized e-mail cam- paign as opposed to the generic e-mail campaigns. They saw that the personalized e-mail campaign had 39% higher unique click rate comparing with static e-mail (Alford, n.d.). In other words, personalized e-mails resonated with their current fans and made them act on it. By clicking through the link provided in the person- alized e-mails, 39% more fans reached to the relevant landing web pages.

The examples Mr. Shin shared with us were easy to follow and made sense. However, there was a disconnect in my mind in regard to what kind of data were collected and how, and how the data were analyzed and inter- preted, which led into the design of various tactics and marketing activities. I raised my hand and asked Mr. Shin about the process of CRM.

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“I understand the need to design different tactics for different avidity levels to attract new customers who are expected to become valuable fans, but I can’t put the pieces together. Could you tell us how the system works? It must be very complicated.”

Mr. Shin laid out the process on the whiteboard. He drew three columns to show the process (see Figure 1). Data acquisition and the centralized database were de- scribed in the first column, customer data analyses in the second column, and the application of analyses in the third column. MLS collects data from ticket and mer- chandise sales during games via sweepstakes and other activities organized to engage fans, and online from the league and team websites. He also gave examples from the sport industry pertaining to various data acquisition methods. I was fascinated with the Tampa Bay Lightning example he gave. In 2011, Tampa Bay Lightning new management decided to give discounts at concession stands and merchandise stores to encourage season ticket sales. Instead of giving discount cards, management designed jerseys with radio frequency identification chips embedded in a sleeve (Swedberg, 2011). Season ticket holders swiped their jersey sleeve at the checkout register to obtain discounts at concession stands and the merchandise store. In return, management was able to create a “sea of blue” in the bleachers and track their season ticket holders’ behaviors at the arena.

Mr. Shin continued with the analyses aspect of the CRM system and explained how data were used for segmentation and modeling. Various analyses could be performed, such as descriptive and predictive analyses. Descriptive analyses are used to segment customers and develop a fan profile (Mullin, Hardy, & Sutton, 2014) using basic demographic information. With this analy- sis, teams try to profile fans using the best available information. Some CRM systems are capable of storing data at every touch point just like in the Tampa Bay Lightning example, which, in return, provides a very detailed description of customers. Based on the type of data collected, segments could be determined based on a fan’s state of being (demographics), state of mind (psy- chographics), product benefits, and product usage (Mullin et al., 2014), or nested segments could be identified by integrating couple or multiple bases. For

example, a fan’s gender, age, ethnicity, and residential zip code could be used to divide a market into state-of- being segments, and a fan’s loyalty and identification could be used to create state-of-mind segments. In addition, sport organizations divide a market into seg- ments based on product benefits and usage, and benefits and usage segmentations are closely related with state- of-mind segmentation. For instance, season ticket holders who are loyal fans of a team make up the heavy user segment of the market, and their expectation from the sport organization is different than light users and casual fans. Season ticket holders might expect exclusive benefits as a return for their loyalty to the sport organi- zation, while a casual fan might only be looking for entertainment and convenience.

In addition to descriptive analyses, predictive anal- yses are performed to determine the likelihood of future events occurring. For example, retention models are developed using demographics, transaction history, at- tendance data, and other fan behavior with the goal of identifying who is likely to renew their season tickets and who is not, and who has the potential to be upsold (to purchase more or better tickets). Knowing a fan’s likeli- hood of renewal may increase a sales department’s productivity and lead to a more effective use of re- sources. Retention models also detect reasons for churn, which is defined as “the percentage of customers that are lost in a given period” (Laursen, 2011, p. 148). Knowing why a team’s fans are not renewing might help the team develop strategies to keep their fans. Moreover, predic- tive analyses can be used to pinpoint what drives atten- dance at sporting events, such as day of week, weather conditions, opponent, rivalry, type of promotions, and other variables. This information has led to the growth of variable ticket pricing programs where ticket prices can be set based on various anticipated conditions (Ulam & Armas, 2014). Data can be plugged into some algorithms to help determine the optimum price point for a ticket based on close to 50 variables and the weight given to those factors.

As the last piece of the process, Mr. Shin touched on designing different marketing tactics based on the infor- mation derived from the analytics. For instance, results may suggest that fans are most likely to attend games on Fridays and Saturdays. With this information, a team can try to drive ticket sales and revenue growth for other games by having family-oriented events at Sunday games, giveaways for weekday games, and not under- taking any additional promotions for Friday games (Levey, 2012). Last, he indicated that the effectiveness of marketing campaigns needs to be measured with appropriate metrics. Mr. Shin ended his speech with the progression of the process:

(a) Identifying a problem,

(b) Conducting background research to develop a hypothesis or a research question,

(c) Collecting data,

(d) Analyzing the data, and

Figure 1 — MLS CRM infrastructure.

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(e) Presenting the results in a way that actions can be taken.

The information was overwhelming, but defi- nitely very interesting. He drew a connection between statistics and marketing decisions that showed me that my fear of statistics was unwarranted. I was telling myself how cool an internship at MLS’ Marketing Analytics department would be!

At the end of the class, I approached Mr. Shin to thank him for his presentation and ask if they had an internship program. He said that they have a very strong internship program. I was very interested in the oppor- tunity and I think it showed. He gave me his business card and indicated that if I was really interested, I should send him an e-mail and he would get back to me. I immediately sent him a thank you e-mail and then attached my resume with a brief blurb that I was interested in the internship. I was shocked when he responded the next day. His e-mail was brief and thanked me for my information. His last couple lines threw me for a loop.

“If you really are interested in this internship please review the attached data and let me know what your conclusions are, and how we should proceed knowing this information.” Mr. Shin concluded by indicating this is the standard “test” that he undertakes to see if someone is really the right data person to work with him. Please see Supplemental Material (available on- line) to work on the assignments provided by Mr. Shin.

Acknowledgments

The views and opinions expressed herein are solely the authors. These views and opinions do not represent those of MLS or Charlie Shin. The data and information provided herein including the interaction between the student and Charlie Shin are for educational purposes only and are fictitious. MLS makes no representations or guarantees as to accuracy, completeness, cur- rentness, suitability, or validity of any information included herein and will not be liable for any errors, omissions, or delays in this

information or any losses, injuries, or damages arising from its display or use.

References

Alford, J. (n.d.). What Major League Soccer knows about predictive analytics marketing? SAS white paper.

Green, F. (2015, November 5). Social CRM: Debunking the myth. Fan engagement 2015: Data-driven marketing best practices. Presented at Sport Analytics Europe Conference. Retrieved from http://www.sportsanalyticseurope.com/ presentations-fan-engagement/

Klie, L. (2012, September). The numbers game. Retrieved from http://www.destinationcrm.com/Articles/Columns- Departments/Insights/The-Numbers-Game-84463.aspx

Laursen, G.H.N. (2011). Business analytics for sales and marketing managers: How to compete in the information age. Hoboken, NJ: John Wiley & Sons, Inc.

Levey, J. (2012, February 29). Sports marketing and predictive analytics: A match made in heaven or (Iowa). Retrieved from http://www.fathomdelivers.com/blog/analytics-and- big-data/sports-marketing-predictive-analytics-a-match- made-in-heaven/

Mullin, B.J., Hardy, S., & Sutton, W.A. (2014). Sport market- ing (4th ed.). Champaign, IL: Human Kinetics.

Sutton, B. (2013). Industry standouts lead sports into new era of decision-making. Sports Business Journal, 16(17), 12.

Swedberg, C. (2011, December 19). Tampa Bay Lightning strikes gold with RFID. Retrieved from http://www. rfidjournal.com/articles/view?9063

Ulam, H., & Armas, G.C. (2014, September 6). Variable ticket pricing catching on across NFL. Retrieved from http:// www.wisconsinrapidstribune.com/story/sports/nfl/2014/ 09/06/nfl-tickets-prices-packers-wisconsin-green-bay/ 15190269/

Zeppenfeld, C. (2014, June 1). 10 Things newbies need to know about the sports CRM world. Retrieved from http:// baylors3.com/10-things-newbies-need-to-know-about-the- sports-crm-world/

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