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.
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.