“Our member research told us that 94% of our members are ‘satisfied’ but last year we only retained 87%. That 7% difference translated into an unexpected loss of more than $250,000 in membership dues…how do we keep this from happening in the future?”
This statement paraphrases a recent exchange with an association membership director on an industry message board. Unfortunately, as renewal season came to a close for this organization in March, the “unpredictability” of member defection had struck again with significant implications on a 2016 budget that had been set months ago. Fortunately, the fact is that there is more predictability to member defection than you might think.
Every year, associations go through their own process of forecasting the percentage of members that they expect to retain in order to project a budget in advance of the next fiscal year. Some use historical averages: “We know that over the past 10 years, we’ve retained 90% of our members.” Some use surveys: “80% of our members are ‘extremely likely’ or ‘very likely’ to renew their membership next year.” Undoubtedly for others, it is more like throwing a dart at a dartboard, or picking a number out of a hat.
With as much data as associations have collected (or are beginning to collect) there is no longer any excuse for not having a more informed perspective on member retention. It involves a simple spreadsheet of members who have lapsed in the past 1 to 3 years, a few hours of time and a basic understanding of data mining. Here are three relatively quick and easy ways in which you can better predict high-probability defectors:
- What percentage of your lapsed members was in the first 12 months of membership?
- How does that percentage compare to the second year, third year, fifth year, etc.?
- If it is considerably higher, it is likely you need to focus retention efforts inside of the first year (or factor this higher defection rate into your forecast).
- Our research shows that in many associations, there is one of two scenarios that play out in terms of a member engagement life cycle:
- “Honeymoon period” – members join and for a period of time (months or years), they are energetic, actively engaged, and proud to be a member. Over time, lacking a definitive value and/or opportunities to get or stay involved, the early excitement wears off. While retention is relatively high early on, it tapers off with time.
- “Prove it period” – members join but are not convinced of the value of membership. The association has a period of time (again, months or years) to effectively communicate and show the value in terms of a return on investment, in essence securing the members’ loyalty and engagement. Lacking this “proof,” defection is more likely.
- What percentage of members was late in paying his or her dues the year before?
- Admittedly, some members simply forget or lose track of the renewal notice; however, for others, this is a sign that he or she is struggling with the decision.
- Over the years, our analysis has shown that members who have paid dues late have a significantly higher probability of lapsing in the next 3 years. The more times dues payments are late, the higher the probability.
- What percentage of lapsed members joined because of a membership campaign or form of incentive?
- Many associations offer incentives and make it financially compelling to join the association in order to take advantage of a significantly discounted annual conference.
- Others discount their membership by 10%, 25%, sometimes as much as 50% if dues are not paid within a certain number of days beyond the renewal date.
- Dues are rarely the reason why members leave associations; instead, it is because what they pay is not at least balanced by what they receive in terms of tangible benefits. Are you offering more “stuff” rather than benefits with specific descriptions on how they are helping members advance their careers and/or be more successful?
- If you have tracked these members who have been incentivized in some way to join, what is their retention rate relative to those who have not been incentivized? We frequently find that defection is higher among the former (especially in the year or two following the incentive).
As you might expect, while each of these three can be examined independently, a combination has a compounding effect on a member’s likelihood to lapse. If you have not tracked this information, we recommend that you start. It does not require a sophisticated CRM or AMS system. It can be started in something as simple as an Excel spreadsheet of members. If it means taking the unpredictability out of your dues revenue, any type of analysis will more than pay for itself.