Key Takeaways

• Surveys are good, but only a minority of members will respond
• Attrition risk is higher among non-responders than even the most negative responders
• Member behavior data can give important insights about 100% of members, regardless of what they say or don’t say
• Affordable AI-powered behavior analytics can identify member experience issues and attrition risk months before they are verbalized

Surveying your members with Net Promoter or other types of Customer Satisfaction surveys is a powerful tool in monitoring/managing member experience and member retention. The aggregate analysis of scores and comments can highlight strengths and weaknesses, but unfortunately, at the individual member level, those ratings aren’t always predictive of which members are or aren’t at risk of leaving.

For one thing, there is sometimes a gap between what people say and what they do. An even bigger issue is that the survey submissions are a single snapshot in time and—speaking of gaps—the frequency of submissions from individual members can leave wide time gaps where many things can change.

There’s also the obvious issue of not everyone responding to your surveys. If you’re getting a 20% response rate on your surveys—congratulations! You’re doing better than most. But what about the other 80%? A disturbing reality is that many health clubs are finding higher attrition rates among survey non-responders than among responders who are giving the lowest ratings. They’re the majority of your members and you have no idea what they’re thinking. Scary.

Fortunately, one thing most fitness businesses have that many others don’t is an abundance of member behavior data. You might not know what members are thinking, but you definitely know a lot about what they’re doing and not doing. With the right kind of analysis, the old adage “actions speak louder than words” can really come to life in a way that can help you eliminate the gaps and blind spots inherent in reliance on subjective self-reporting.

Machine Learning is an application of AI that excels at this kind of analysis. It can easily find complex relationships among a host of current and historical member variables such as check-in patterns, ancillary spend, service utilization, demographics, club location, seasonality (and more!) and identify an individual member’s likelihood to cancel or to purchase products like Personal Training. It can also identify emerging micro-trends that can be the early warning signs of member experience issues—such as an abnormal decline in check-in frequencies for females age 36+ for a specific membership type in a specific location—long before they show up in your surveys.

Keep doing those surveys and other Voice of Customer measurements, but just remember that the vast majority of that voice will be in the form of behavior, not words. AI-powered member behavior analysis is a powerful tool for seeing the behavioral “voice” of 100% of your members, and it is now easily accessible to fitness businesses of all sizes.