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Understanding sport data analytics

This is an excerpt from Sport Marketing 5th Edition With HKPropel Access by Windy Dees,Patrick Walsh,Chad D. McEvoy,Steve McKelvey,Bernard J. Mullin,Stephen Hardy & William A. Sutton.

Data Analytics

With all of the sources of market research methodologies just described, front office staff have the potential to be left with an enormous wealth of data to organize and sift through. In order to really make informed decisions from the data, front office staff must analyze it. In essence, data analytics requires reducing large quantities of data into information that is actionable in the workplace. The availability and improvements in technology have certainly aided in data collection and management for sport organizations.

CRM is one of the most widely used tools to support analytics within professional sport organizations, but many sport organizations use some form of this analysis as well from college through minor leagues as well as the professionals. Basically, CRM involves documenting information about consumers in order to acquire, maintain, and develop relationships with these consumers over time. CRM platforms are essentially customer profiles that include as much information about customers (fans) as possible. This includes documenting

• demographics (e.g., age, gender, household income),
• buying history (e.g., how tickets were purchased, when they were purchased, how many tickets, which seats),
• attendance (e.g., how long they’ve been attending, what games they prefer), and
• client engagement (e.g., notes section from any conversations with sales staff).

With the documentation of all of this information, team staff can be prepared to effectively interact with the consumer and provide a personalized ticket package, send out marketing materials that are most applicable to this consumer, or recommend the appropriate upgrades based on anticipated capacity and interest. Understanding current consumer interests can also help sports executives position themselves to recruit new customers in the future.

Once sports executives have the documentation about individual fans, they can conduct a cluster analysis, which groups fans into segments based on their similar characteristics. To help prioritize engagement with fans within the CRM, teams utilize what’s called an RFM model, which stands for recency, frequency, and monetary (table 4.6). By using these metrics to segment fans, ticket associates can generate a grading system that’s used for better prioritizing the consumers they should target their attention toward in order to maximize sales.

Several programs exist to help front office staff keep track of all of this data and produce meaningful analyses and output to help inform next steps. Microsoft Dynamics is one of the more popular software at the professional level. Dynamics has the capabilities of both documenting customer information and then sending customized messaging or specific promotions to groups of fans who may be best suited to these promotions based on the data collected about them. Without sophisticated CRM platforms, sport organizations can start with a simple spreadsheet to build out the things they do know about their fans.

Teams can also utilize data analytics to make a variety of other decisions, beyond trying to manage relationships with fans. Collecting and analyzing data can help teams make decisions about promotions, sponsorships, concessions, and more.

Descriptive statistics help to identify visible trends, outline frequencies, and essentially describe the data in a way that summarizes what has been collected. This would look like the average number of fans in attendance or the percentage breakdown of age groups attending a given game. This sort of analysis isn’t necessarily trying to make predictions yet, but it is a way of identifying important findings within available data. Common descriptive statistics include looking at the mean, median, mode, and frequencies of occurrences in a data set. How many times did a fan purchase tickets to a game last season? What was the average number of beers sold between the first and second period? What was the highest attended game last season?

Inferential statistics refers to when a researcher takes an observation from a smaller group of people and tries to generalize it to a larger population. In this case, a researcher could interview several focus groups made up of four to six fans at a time and try to draw conclusions about the sentiments of all fans. Similarly, a researcher could survey 300 fans and try to apply the findings to the thousands of fans in the company’s fan base.

Predictive analytics focuses on analyzing previous sets of data or behaviors to understand and predict future behaviors. This kind of analysis can be particularly useful for generating retention models for season-ticket holders or predicting attendance to help hire the right number of concession staff for a given game. With ample access to data to input into the predictive algorithms, sport managers are becoming more savvy and capable of optimizing across all areas of operations.

A common statistical method used to make predictions is called linear regression, also often referred to as “regression.” What regression does is allow a person to insert one or more independent variables into a model and try to understand how each variable contributes to a dependent variable. Say, for example, you work in a marketing department for an MiLB team and you want to know what day of the week (independent variable) and type of promotion (independent variable) combination would help you attract the largest audience (dependent variable). You could take data from the last four years and code the days of the week and promotion types into categorical variables to identify their respective contribution to attendance size.

Many other statistical modeling techniques exist that make strong predictions about future behaviors, but in any event, these predictions are only as good as the data involved, so care and attention should be given before throwing variables in a model and making real business decisions from the findings.

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