Debbie King - Sep 11, 2015

Case Study: Understanding and Optimizing Membership Dues Collection

Association Challenge: Which members are late paying their dues and which members are ‘at risk’?

Our client, a trade association, was struggling with dues collections. They were unable to identify those members who were late paying their dues due to the complexity of their dues structure.

They also could not tell if paying late was normal for those individuals or not. Not knowing the members' typical payment scheduled made it impossible to determine which members were just lapsed and which were at risk of dropping membership altogether.

The association was not sure which members they need to push to pay for renewal or whether this was their normal behavior.

Association Analytics Solution: Create a data visualization to help understand and optimize dues collection

We collected and cleaned the membership data and created an anticipated payment schedule for current renewal dues based on members’ payment history. We created a simple and effective visualization, classifying each unpaid member into one of 5 categories:

•    60 days+ past due

•    30 days+ past due

•    Keep Eye On (less than 30 days past due)

•    In good standing (their expected payment date is 30 days or less)

•    Did not pay in previous year

The Results:

The association now has a much better understanding of their dues collection trends and patterns. They are now able to quickly identify those members at greatest risk of not paying for their membership dues for the year. They can proactively contact members as they approach their anticipated pay date to ensure they renew. Now they develop targeted messages aimed at changing the behavior of those members who regularly pay late.

Written by Debbie King