See why members leave —
before they do.

This is the connective layer made tangible. Attendance and form-engagement data combine into a churn-risk score per member — the metric that turns a nice-to-have into a can't-live-without.

Churn-Risk Board
JR
Jordan Reyes
14 sessions · form ↑
Healthy
MP
Mei Park
Attendance ↓ 3 wks · depth ↓
At Risk
DS
Devin Shah
No check-in 21 days
Churning
CO
Chen Ortiz
Consistent · tempo strong
Healthy
Projected 90-day retention lift+18%
Not record-keeping — prediction

Two signals, one score

Attendance data alone tells you someone stopped showing up. Add form-engagement — are their sessions getting better or worse? — and you can predict disengagement early, and explain why it's happening.

  • Attendance patterns and check-in frequency
  • Form-quality and coaching-engagement trends
  • Combined into a per-member churn-risk score
Why it's the moat

Hard to rip out, by design

A gym using this to prevent cancellations won't downgrade to a cheaper billing-only tool. That's what makes retention intelligence the stickiest part of the platform.

Early warning

At-risk members surface weeks before they cancel — while there's still time to act.

Trainer prompts

A flagged member auto-routes a check-in to the right trainer, tied to the reason for the risk.

A sellable number

"Gyms using the coach retained X% more members" beats any feature list on your sales page.

"
Knowing that a member is leaving is hindsight. Knowing why — early enough to act — is retention.
— The AviTechnoFit connective layer, in one line

Turn floor data into members who stay