Modeled Fitness scenario
Catching cancellations six weeks before they happen
Starting assumptions
- 6.8%Monthly churn
- 22%Trial-to-member conversion
- 31%Failed payments recovered
Illustrative modeled outcome
- 5.2%Monthly churn
- 61%Trial-to-member conversion
- 71%Failed payments recovered
The challenge
the modeled business ran four locations and treated churn as a marketing problem, buying more trials to replace the members leaving out the back. Their check-in system already knew exactly who was about to cancel, because members stop attending roughly six weeks before they call. Nobody was reading that data. The other leak was billing: a failed card triggered a form letter three weeks later, by which point the member had already mentally left.
The approach
Check-in events wired into GoHighLevel
We connected their access control system so scan-ins arrive as events. That one integration turned a dormant dataset into a trigger, which is the basis of everything that followed.
Drop-off measured against personal baseline
A member who normally attends three times a week and has not scanned in ten days is at risk. Someone who attends fortnightly is not. Thresholds are set per member against their own pattern rather than one global rule.
A coach message, not a marketing message
Outreach comes from their actual coach by name and asks how things are going. It captures a reason, and injury, schedule, cost and motivation each route somewhere different.
Same-day payment recovery
A failed card now triggers a friendly same-day text that assumes the card simply expired. Recovery rose from under a third to over seventy percent, almost entirely on speed and tone.
The outcome
Monthly churn fell from 6.8% to 5.2% across all four locations, which at their membership base is worth more annually than their entire paid acquisition budget. Trial conversion nearly tripled once the same drop-off logic was applied to trial members.
Other builds
Modeled Home Services scenario
Cutting speed-to-lead from 40 minutes to 48 seconds
Illustrative: +42% booked estimates
Modeled Roofing scenario
Surviving a 400-lead storm week without hiring
Illustrative: 2.4x storm capacity
Modeled Med Spas scenario
An AI agent that books consultations at 11pm
Illustrative: 3.1x rebooking rate