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previously wrote about some of the theory behind using predictive analysis in a member-based organization. This post is intended to take the next step, looking at some specific variables that might be relevant in a membership renewal business case. Here are some quick examples that may prove interesting in a predictive model. The value of these data points may be greater in some organizations than others and in some cases these attributes may have no bearing at all on renewal probability. These are just some common data points that we've seen in some way correlate to renewal probability.
# of Years as a Member Is there a correlation between the # of years someone is a member and their likelihood to renew. For example, is an individual/organization that has been a member for one year, less likely to renew than a 10 year member? Depending on the organization and the nature of the membership structure, there could be different answers. For your group, do you know the answer?
# of Non-Dues Related Transactions Members that are active in purchasing books, certifications, or other non-dues items from an organization may be getting more value for their membership, and therefore more likely to renew than others. Is this true for your group?
# of Years of Uninterrupted Attendance at Annual Event Attendance at Annual Meetings, as well as other events may be a factor in someone’s willingness to renew membership. A committed member that attends annual gatherings, year after year, is more likely to renew, or are they?
# of Committee Assignments The involvement level of an individual or organization in volunteer activities, such as committee positions, may have a bearing in their overall perception of value from membership. Is this the case for your constituents?
These factors are simply a handful of dozens or hundreds that can be evaluated quite easily with modern predictive analytics tools. Additionally, a key goal of predictive analytics is to find factors in the data that you didn't necessarily think would impact your intended business goal.
Once you have started the process of running these types of analytical tools,
you must have an action plan, otherwise there is little value in the analytics. For example, if a predictive model suggests that your most at-risk members are those that have been with the organization between 3 and 7 years and have not attended at least one event in the least 2 years, what do you do? The action element is just as important as the analysis. Remember, no predictive model is perfect, but they get better as you use them more and more. Don't be afraid to act on good, yet imperfect ideas that come from these tools. Make sure you foster an inquisitive culture organizationally and constantly go back to tune the modeling approach.