2.9%
Monthly churn
From 4.6%
Case Study
Customer Retention Intelligence
Anticipate the resignations before the letter arrives.
Case Study
Anticipate the resignations before the letter arrives.
A predictive layer that watches behavioral patterns, transactional history, and sentiment for the early signals of disengagement. Your team gets a weekly list of members drifting away, ranked by risk, with the specific signal that triggered each flag and a recommended outreach action. By the time a cancellation conversation would normally happen, the relationship has already been re-engaged.
The system separates seasonal quiet from real risk, so the team does not waste attention on accounts that are simply on holiday or already responding to outreach. Risk scores are explained in plain language, never as black-box numbers, and every intervention is tracked so you learn which approaches actually save which kinds of relationships.
Capabilities
Visit cadence, app usage, attendance, and channel engagement patterns.
Spend trends, billing disputes, payment friction, recovery attempts.
Sentiment signals from email, support conversations, and survey responses.
Risk scores with plain-English reasoning (“declining dining visits + recent billing escalation”).
Outreach playbooks tailored to the type of disengagement, with outcome tracking per intervention.
From Our Work
First quarter, twenty-two studios.
The operator was losing 4-5% of paying customers a month with no early warning. Some looked healthy in billing reports while their actual engagement had already dropped; others kept paying but had logged repeated frustration in support conversations. Cancellations arrived as a one-line tap in the app, leaving no chance to intervene. We delivered a churn intelligence layer that pulled check-in cadence, class booking patterns, billing events, and support sentiment into a single risk score per customer. Studio managers now get a Monday morning list of disengaging customers with the triggering signal and a recommended action. Within the first quarter, monthly churn dropped from 4.6% to 2.9% and the save rate on at-risk outreach climbed to 38%.
2.9%
Monthly churn
From 4.6%
81%
Customers contacted before cancellation
From under 10%
38%
Save rate on at-risk outreach
From none tracked
7–10d
Time from disengagement to flag
From 60–90 days
<1h/wk
Manual list-building per studio
From 6 hrs/week
Key Insight
Members do not disappear suddenly — they fade. Catching the fade gives the team weeks of room the cancel button does not.
