Where Member Retention Fails and What To Do About It

The largest failure point in the fitness industry occurs at the beginning. Nearly half of new exercisers discontinue within the first few months, and more than a third of participants stop attending within a few months.¹ This is a major issue because it defines whether a member ever achieves consistent facility visits.

Uncertainty shapes early behavior

Many new members arrive without a clear plan. They are expected to choose exercises, set intensity, and structure a session on their own. These decisions are made in an unfamiliar environment, often under perceived social pressure.

Intimidation in gym settings is widely reported, and fear-related barriers continue to influence participation.^2,3 These conditions affect how long someone stays in their first few sessions and whether they return at all. When the early experience lacks structure, hesitation follows.

Programming determines whether members stay engaged

Programming is one of the most controllable variables in the early member experience.

Generic exercise guidelines are a solid foundation, yet they do not account for differences in readiness, preferences, or context.⁴ When a program does not reflect the individual, adherence declines.

Observed differences between novice, experienced and dropout populations reinforce this pattern.⁵ Individuals respond differently based on their starting point, yet many programs are still delivered as if those differences do not exist.

Adaptive systems allow programming to evolve with the member

Recent advances in artificial intelligence (AI) have made it possible to adjust programming in response to user behavior. For instance, reinforcement learning-based exercise prescription can improve both exercise intensity and user satisfaction compared to static programming.⁶ These dynamic systems update recommendations based on member performance, allowing training to change as the individual progresses.

This type of adjustment is especially relevant early, when confidence, tolerance and familiarity are still developing, which is exactly what is occurring for new members.

Not all AI delivers the same level of guidance

The effectiveness of AI-based programming depends on how well it reflects the individual. Evaluations of AI-generated exercise programs show that systems relying on generalized inputs may lack sufficient variability and specificity.⁷ Programs that incorporate detailed user data and ongoing performance feedback produce more relevant guidance across sessions.

This distinction matters when evaluating solution effectiveness.

What this means for operators

Early adherence is shaped by how clearly members understand what to do and how confident they feel doing it. PACE was developed to support this stage. It uses individual inputs and Matrix proprietary, generative AI to guide exercise selection, intensity, and progression from the first session. Members do not need to independently determine how to begin or how to progress.

Providing structured, individualized guidance early supports competence, reduces uncertainty, and increases the likelihood of repeat visits.

Facilities that address the first few months with this level of precision are better positioned to retain members over time.