Predictive analytics: the future of member retention for associations

The key to successful member retention lies within your member data.  

Read on to learn how modern predictive analytics tools can help your association uncover data insights and help you avoid member lapses.  

Predictive analytics might sound like something that only big for-profit businesses do. But the truth is that predictive analytics represents the future of member retention strategies for associations. 

What is predictive analytics? 

Predictive analytics is the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. To put predictive analytics in perspective for your association, consider the four main types of analytics: 

  • Descriptive – This examines decisions and outcomes after the fact to better understand what happened.
    • For example: that a member lapsed.
       
  • Diagnostic – This helps you understand why something happened in the past. 
    • For example: a member lapsed due to low engagement.
       
  • Prescriptive – This helps you understand actions you can take to affect the outcome.
    • For example: actions that could prevent a member from lapsing. 
  • Predictive – This forecasts what is most likely to happen in the future.
    • For example: member is at high risk for lapsing soon.  

Why care about predictive analytics for your association? 

Your association collects a vast amount of member data. This data can help your association anticipate member needs and behaviors, but it can be difficult to sort through – especially manually. In fact, in the 2021 Association Trends Study by Community Brands, 63 percent of those working at professional membership organizations (who responded to the study’s survey) say that processing/using the data they have is somewhat or very challenging.  

63 percent of those working at professional membership organizations say that processing/using the data they have is somewhat or very challenging.  

Predictive analytics tools automate the practice of analyzing and using data, making it much easier to process data and uncover useful insights. These tools are easy to use, so even non-techy staff members can analyze data on their own – without needing help from a data scientist. For example, staff in your membership department can use predictive analytics to understand why members are lapsing and (even better) which members are in danger of lapsing in the future (so they can attempt to avoid the lapse). 

Nimble AMS includes artificial intelligence (AI) and predictive analytics tools to help you do things like predict which members are at highest risk of non-renewal and then reach out to save them before they leave. 

How can predictive analytics improve member retention? 

Now let’s dig in a bit more to see how using predictive analytics can help you improve member retention for your association: 

  • Discover membership trends – The right predictive analytics tools allow you to automatically discover relevant membership trends (including those related to membership lapses) based on your organization’s data without having to build sophisticated data models.
     
  • Uncover useful insights – These tools allow you to surface and share relevant insights for your entire staff to see. For example, display predictive scores that signal how likely a member is to lapse soon as well as what factors are most and least likely to lead to member attrition. This opens the possibility for your entire staff to improve a member’s experience with your organization.
     
  • Alert staff – You can even offer up visual warnings to alert your staff that a member may be in danger of lapsing soon. This can be helpful, for example, if a staff member is on a phone call with a member and sees that the member might be at risk of lapsing. 
  • Suggest corrective actions – You can automatically present staff with suggested actions for them to take in case they encounter an at-risk member. Go a step further by proactively presenting the at-risk member with options, such as an ad that gives them a discount on membership renewal.   

How does predictive analytics for associations work in the real world? 

Here’s a case in point: Marine Corps Association & Foundation (MCA&F) was seeing about 1,000 lapsed members each month. Using Nimble AMS Prediction Builder – which uses the AI technology, Salesforce Einstein – the organization implemented a solution that automatically identifies members at high risk of lapsing and presents staff with suggested actions to take to prevent non-renewal. 

Once the association rolled-out the AI process and trained staff, it was then easy for MCA&F staff to see warning signals for members at risk of lapsing. Now, when an at-risk member is discovered, staff are automatically presented with actionable steps to take with the member.

The result: 25 percent of MCA&F members at high risk of non-renewal were saved. 

Learn more.

Read more about how predictive analytics helped one association increase member retention: Read the Marine Corps Association & Foundation case study. 

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