Harnessing the Power of Membership Predictive Analytics for Strategic Association Growth

Membership predictive analytics is a new approach that employs data and machine learning to forecast member behavior, particularly in terms of renewal likelihood, according to an article by Association Analytics. 

This method simplifies the complex task by spotlighting factors strongly correlated with membership renewal, allowing organizations to focus marketing and engagement efforts effectively. Members are then categorized into prediction buckets, ranging from “Very Likely to Renew” to “Very Unlikely to Renew,” enabling targeted strategies for quick renewal or personalized approaches as needed. The model ties revenue to prediction buckets, aiding in setting realistic dues revenue expectations for the current year.

These predictive insights act as a crystal ball for associations, informing mid- and end-of-year strategies. Mid-year, organizations can tailor engagement activities based on members’ prediction categories, adjusting approaches for those in the “May or May Not Renew” category. As the year concludes, end-of-year strategies can be customized for different renewal groups, ensuring standard strategies for highly likely renewals and a more personal touch for others.

Getting started with predictive analytics can be approached in various ways. One can opt for a do-it-yourself (DIY) approach using manual methods or leverage traditional judgment and expertise to identify key renewal factors. However, using a specialized platform is recommended, as it automates predictive analytics based on member data points, allowing organizations to focus on strategic work rather than delving into the intricacies of data science and mathematics.