Data Analytics and Member Engagement

It’s common knowledge that keeping a gym member costs less than retaining a new one. In fact, most studies put that figure between three and five times cheaper. By the numbers, then, let’s consider how far $1,200 goes. If it’s $150 to acquire a new member but only $30-$50 to keep an existing member, why wouldn’t you focus some resources on the members you’ve got?

An obvious answer to this is that you don’t have the tools to do this. There are myriad platforms and tools to simply pay for ad space in front of your target demographic. What’s severely lacking in today’s common toolset is an effective way to identify and reach out to members who are at risk of churning. This is where data analytics shines.

Consider the following example showing the daily attendance of three members, Alex, Bob, and Claire, over a two month period:

Looking at this, you probably see that:

● Alex started with a strong routine and is tapering off,

● Bob has reduced his attendance rates,

● Claire, though attending least frequently, is the most regular.

Let’s consider the use case of determining when to engage with a member.

We’ve done this for Alex in the graph by changing to a weekly view. A clear schedule emerges, so try during their normal workout hours on a Monday, Wednesday, or Friday or the following day to check that they’re okay.

For Claire, shown below, that single Thursday is a great moment to find out why she doesn’t attend more often, or you can use it as an opportunity to present a class that might appeal to her at that time.

We can take it a step further, though.

Let’s identify when to flag a member as “at-risk” of churning. These members can be flagged in your management software so that any desk staff, trainers, or other team members on the floor can make sure to give them special recognition or speak with them about their goals. Making your at-risk members feel seen and welcome in your fitness community is the number one way to retain them.

Using a few configurable rules, we can use a data analytics tool to see that Alex crossed our risk threshold shortly after February 18th. He’s missed three workouts in the past two weeks, so he’s fallen far enough outside his usual patterns that we know he’s at risk of churning.

Applying the same concepts but with a reversed threshold, we can see that Bob has shown greater attendance than usual near the end of January, so that same rolling window approach tells us that that time is a great upselling opportunity - be it for classes, coaching, or supplements.

Simple rule-based approaches like this are better than nothing, but there’s an entire field of anomaly detection you could be making use of today with the help of advanced data analytics. Imagine knowing not just how many, but exactly which customers of yours are most likely to churn before they’ve even made the decision to cancel their subscription. With GroeFit’s help, you could improve your member database with predictive analytics far stronger than the simple techniques shown above in order to lower your churn and maximize your revenue. Consider your cost of acquisition compared to your cost of retention. Is focusing your efforts on the customers who need it something you can afford not to do? Sign up today to get help managing your data and keeping your members engaged when it matters most.