Detecting customer churn early
Falling usage, less frequent orders, a cooling tone: the system spots at-risk customers before the cancellation arrives.
Why a cancellation never comes out of nowhere
The problem: When a customer cancels, everything is obvious in hindsight: usage had been declining for months, orders became less frequent, the tone in support turned cooler, the main contact changed. It is just that nobody saw it beforehand, because these signals sit in four different systems and nobody can keep an eye on two hundred customers on the side.
The solution: Our system keeps that eye on them. It collects the signals from your systems, usage data, order frequency, support threads, contact history, and detects when the pattern changes for a customer. Then there is an alert with context: which customer, which signals, since when. It comes with the fitting next step, from a check-in call to an adjusted offer.
What happens next: You talk to at-risk customers while it is still a conversation and not a cancellation confirmation. Customer value sets the order: where a lot of revenue or potential is at stake, the system triggers earlier. And the signals get better when you also include what customers write: automatically analyzed customer feedback often shows dissatisfaction first.
Typical scenarios
From subscription businesses to repeat-customer commerce. These are the most common scenarios from our projects.
- Early warning with context: The alert does not just say who, but why: which signals changed and since when.
- Prioritized by value: Not every customer is the same. With high revenue or potential, the system speaks up earlier and more clearly.
- The next step included: Every alert comes with a suggestion: call, adjust the offer, propose a meeting. No guessing what to do now.
- Win-back: Even after a cancellation: the system recognizes when a new approach is worth it, instead of leaving it to chance.
