Retaining association members can feel a little like shooting in the dark, especially if terms like “metrics” and “predictive analytics” seem like a foreign language. For many association leaders, member retention is about in-person connection, surveys, and generalized marketing that is hard to really know how well it’s working. 

One of the things that we talk about a lot at Tasio is the need for both person-to-person and data-driven strategies for keeping membership numbers high.

By developing the right metrics and using them to develop member profiles, you can better determine who in your current association membership is getting ready to leave—and target them with specific marketing messages to keep them happier. 

What are Metrics for Association Member Retention? 

The big picture idea is that members take action (or don’t take action) based on how invested they are in your association. They take action through things like: 

  • Taking CEUs
  • Logging into the member portal of your website
  • Paying for non-dues items from your store

And they take inaction through things like: 

  • Not posting or making comments on the website
  • Not updating their credit card for renewal
  • Not opening emails

The series of behaviors that members do or don’t take contribute to whether they are going to renew or not, which is the basic idea of predictive analytics. 

There are behaviors that members take that can help us identify whether or not they will renew. These behaviors are called metrics. 

Developing these metrics is one of the most important things you and your association leadership team can do to help identify who is going to renew. 

5 Steps for Creating Association Metrics that Work

The most important thing to remember is that developing metrics should rely mostly on your own intuition and experience. You know your members the most, and there are some areas of your association that will be specific to you and no one else. 

That being said, here are the key steps for creating metrics that really work to predict member loss.

Step 1: Gather Your Team

It’s always a good idea to do this part with a team that you trust. It should include leaders as well as current members and former members (if possible). You’ll want to choose people who have connections to a lot of people in your association to get a good overview of the kinds of services and roadblocks that they are interacting with on a daily basis. 

Step 2: Know Your Data Limitations

In order to make predictive analytics work, you need data points for all your metrics for as many members as you can. That means you need to be able to choose metrics for which you have solid data. This data might include: 

  • Purchasing data
  • Email data
  • Course completion data

You don’t have to have all this data now, but you must make metrics that are concrete enough that you can get that data in the future. 

Step 3: Brainstorm Metrics (Recency, Frequency, and Monetary)

The goal for your brainstorm team is going to be developing 5-10 metrics that give a good overview of the behaviors your members do or don’t do before they leave your association. We always suggest that you should have at least a few in three key areas: recency, frequency, and monetary. 

Recency. How recently did this behavior occur? 

Examples: 

  • Days since last login
  • Tenure (years/months of continuous membership)
  • Days since last purchase
  • Days since profile update
  • Days since last contact with association
  • Renewal for first year members

Frequency. How often does this behavior occur? 

Examples: 

  • Total number of logins
  • Number of non-dues purchases over the last (3,6,12) months
  • Number of webinars or events attended over the last (3,6,12) months
  • Number of CEUs/CPEs awarded
  • Ratio of number of unused seats to membership cost

Monetary. How much is this behavior worth? 

Examples:

  • Amount spent in the store
  • Amount of non-dues or dues purchases over the last (3,6,12) months
  • Change in number of purchases last month compared to this month
  • Number of cancelled/returned purchases last (3,6,12) months
  • Ratio of usage/membership price

Step 4: Create a Historical Sample of Members with KNOWN OUTCOMES

This is a crucial part of the process of using metrics to develop member profiles. In order to make sure that your team has chosen metrics that are predictive, you really need to gather a sample size where the outcome of the member behavior is known. We suggest that this is a year-long period that is pretty recent—ending within a few months of the time you create the data set. 

Remember, the more recent the data, the better it will be to help you predict future member behavior. 

The most important part of this step is that the outcome of the member is known. You have to be able to see the kinds of things a member did before they left your organization so that you can use that data to accurately predict how your current members will act. 

Step 5: Analyze Metrics and Remove Bad Ones

It’s unlikely that every metric you choose is going to be a winner, and that’s pretty normal. Once you have gathered your data, you’ll want to put it into a program that helps you analyze how effective that metric is for predicting member loss. You can do this in Excel yourself, or you can borrow our workbook for predicting member loss below. 

If you choose to use ours, all you have to do is add your membership data into the “Data” tab and the Excel spreadsheet does the rest of the work for you. You’ll be able to easily see which metrics are accurately helping you predict member loss and which ones should be thrown out. 

Questions? 

If you have any questions on how to use the system or would like more information on the Tasio predictive analytics model for retaining members, download our free ebook here.