Teams often improperly invest their limited renewal resources and end up spending more to achieve less than optimal results. Here are two common mistakes that teams make when trying to maximize their renewals.
The “One-Size-Fits-All” Trap
The first step in effectively leveraging your data to drive renewals is to acknowledge that there are different kinds of buyers, sellers, products, price points. Sports leagues have acknowledged that there are different sales skills involved in selling new tickets versus renewals – as most teams have separated sales (new business) and service (retention & upsell).
However, there are also significant differences in buyers, products and prices. Renewal efforts should acknowledge that the renewal habits of a business buyer who has 4 premium seats are fundamentally different than the renewal patterns of a personal account with 2 tickets in the cheap seats. Those differences should be incorporated in targeted sales and marketing approaches.
But even if you have acknowledged these differences, it is not efficient to apply the same level of renewal resources to each account. Certain buyers are inherently less likely to repurchase. Identifying those buyers early in the process positions you to devote more attention to risky accounts and save the renewal.
The Silo Trap
Many teams that understand the need to identify and target their riskiest renewal opportunities and avoid the “one-size-fits-all” trap make a costly mistake in leveraging siloed fields of data to make targeting decisions. Consider the following renewal examples:
- A season ticket holder who only used tickets for 40% of games.
- A premium season ticket member filled out a customer service survey and said all kinds of terrible things about the team, their experience, etc.
- A person who has never returned a service rep’s phone call, answered an email, or had any other interactions with your service staff?
Are these people renewal risks? Most teams would say “yes” and would formulate renewal strategies for these customers and others who exhibit similar characteristics.
Before we invest a lot of resources in attempting to save these accounts, we should attempt to better understand these risk factors – and any other factors that would quantitatively mitigate some of the risk. For example, lets say the person who only went to 35 out of 81 games has been a season ticket holder for 35 years and has never been to more than 50 games in all that time. Or, maybe it’s a business that only uses the tickets to entertain their best clients and is perfectly happy with the value that they receive from their less-than-perfect attendance record. Equally important mitigating factors might be in place for the person who responded negatively to the survey or wasn’t available to speak with the team’s service rep.
Sports data will yield potentially dozens of fields of negative stories. The above factors could prove to be strong predictors of member attrition. However, it is important to understand the whole story before making assumptions and implementing expensive sales and marketing approaches that might be solving a problem that doesn’t exist (or more appropriately, exists somewhere else).