4 core ML models.
20 business verticals.

Members quietly stop coming weeks before they cancel.

Patients drift to other providers when follow-ups slip.

Loyal regulars become "we used to go there" without a single complaint.

Morning regulars break their commute habit and settle into a new routine at a competitor café.

Clients miss their rebooking window and drift to competitors offering first-time discounts.

Guests rebook through OTAs instead of direct, costing 15–25% commission per stay.

Past attendees drift away between seasons and are expensive to re-acquire via ads.

Past clients drift to OTAs or competitors when you are not top-of-mind during their planning window.

Clients miss their rebooking window and try a competitor offering a first-visit discount.

Students mentally quit weeks before they formally drop — and most institutions only notice at the refund deadline.

POS data shows a frequency drop only after the customer is gone.

Pre-approved leads go cold within days and at-risk borrowers shift to competitors with faster offers.

Customers try a new service once, develop a new habit, and never come back — all without a complaint.

Members reduce their visits gradually, and by the time they cancel, they've already found an alternative workspace.

Customers forget recurring service appointments and search for competitors when a problem appears.

Players close the app and may never return once they hit a friction point or boring cycle.

Workshop loyalty drops 60% once the free OEM service window ends and customers switch to independent garages.

Pet parents miss grooming and vaccination appointments, drifting to other local providers.

Policyholders shop for cheaper quotes on comparison sites during the renewal window when 90% of churn happens.

Retailers gradually shift orders to competitors without warning until your quarterly report shows a revenue drop.

Every business loses customers, but the patterns are unique to your industry and hard to detect manually.