Most gym schedules are built on tradition and intuition. "We've always had yoga at 6pm on Tuesdays." But your members' lives aren't static—and neither should your schedule be.
Before optimizing, you need to understand what's actually happening:
AI aggregates this data and reveals patterns invisible to manual tracking.
"One studio discovered their 5:30pm class was 90% full, while their 6pm class was 40% full. They moved the instructor—not the time—and filled both."
// Sometimes the problem isn't the slot. It's the pairing. Data shows you what's really driving attendance.
Here's a practical framework for schedule optimization:
Which classes are consistently full and drive membership value? These are non-negotiable. Build around them.
Which classes consistently underperform? Don't just cut them—understand why. Wrong time? Wrong instructor? Wrong format?
Run 4-week tests with clear metrics. Don't change multiple variables at once. Measure one thing at a time.
Member patterns shift with seasons, work schedules, and life changes. Review and adjust every quarter.
Studios that implement data-driven scheduling typically see:
The goal isn't more classes. It's fuller classes that members actually want.
Let's analyze your schedule and find the opportunities.