It’s that time of year for many teams and entertainment organizations to lock in their ticket plan renewals. The use of retention modeling is a great way to determine who to target and expected results. Year over year, the process can be improved to provide greater organizational insight into your fan base and ticketing plan options. 

With so much data available to feed into your retention model from all aspects of the fan experience, you need to narrow in on what data points correlate to renewal likelihood.

Here are 5 key data categories to collect and consider working into your retention model: 

1. Ticket Activity

It probably won’t be a surprise that ticket activity will be a key indicator of likeliness to renew and the following points should be added to your data set for renewal modeling: 

• Attendance Percentage
• Tickets Forward
• Ticket Resale Percentage 
• Donated Tickets 

Pro-Tip: Be sure to pay special attention to resale percentage. Plan holders looking to resell their tickets may be less enticed to renew their package when they have a low ticket resale percentage. 

We recommend looking at this resale statistic throughout the season. It may be advantageous to proactively call your customers that have difficulty selling and suggest a competitive price point if applicable. This may save your customer during renewal time. 

2. Proximity to Venue

Add Zip Code to your data set. With this data in your model, you can view which season ticket holders have moved away from the venue and are therefore less likely to renew. 

Address data can also be correlated with wealth. Within your market, this may be a great indication of those likely to purchase certain ticketing plans. 

3. Ticket Holder Tenure

Length of a ticket holder’s tenure at an organization is a key attribute to gather and is a great indicator for those likely to renew. While long-term ticket holders are a likely bet for renewal, rookie season ticket holders tend to be more likely to churn.

In the early-mid season, be sure to remind rookies of all the added benefits of being a season ticket holder and encourage them to take advantage to increase their engagement with your organization.

4. Digital Interactions

The more digitally engaged, the more likely to renew. It is common that if an individual consistently opens emails, likes Instagram posts, and completes surveys, they are more likely to renew than someone that has unsubscribed from all communications. 

• Email interactions
• Form submissions
• Website visits
• Social media engagements 

Do not look too closely at only one source without consider others as it can skew your view of the individual. For example, consider a person that does not have social media accounts but may still open emails. 

5. Survey Data

Prior to starting your retention planning process, you may consider sending a survey to all season ticket holders to develop your organizations Internal Renewal Score (IRS). We recommend launching a “Likelihood to Renew” digital survey or call-campaign 2 months prior to the start of renewals to allow enough time to understand responses.

Pro-Tip: When surveying, instead of simply asking “yes” or “no” if they are going to renew, ask their likelihood to renew in a scale rating in order to gauge interest.

In addition to the survey, have your account managers to do a “gut check” for their accounts, scoring each on the likelihood to renew using the same scoring as your IRS survey. This is a good check-in to see if season ticket members and their account managers are on the same page, and if not, determine what can the account manager do to help.

Closing

While this may be seen as a daunting list for those just beginning to develop retention scoring, understand the most important thing to do is simply start! If you do not have all these data points readily available, work within the parameters of what you do have and continue building your data set during the next season.

For those that have developed retention models already, we encourage creativity in the data collected and the types of models built. For example, maybe try adding Credit Card Declines to your data set to see if that adjusts the results of your model. Year over year, continue to refine the model’s accuracy while finding ways to automate the process. 

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