Churn signal detection

Risk identified 3 months before departure

A customer's departure is usually noticed at cancellation, once the decision has been made. Yet Bain & Company's work shows that a five-point rise in retention translates into a 25 to 95% increase in profit, and the warning signs, falling usage, repeated complaints, disengagement, are already in your data.

The automated workflow

A structured process for fast, reliable results.

Signal collection

Behavioural data is aggregated: usage frequency, purchase history, support tickets, marketing engagement, NPS.

Predictive modelling

Behaviour is analysed and warning signs identified by comparison with the profiles of customers who have already left.

Proactive alerts

Sales teams are notified as soon as a customer enters the risk zone, with details of the signals detected.

Retention actions

Actions suited to the customer's profile and the nature of the risk are suggested: a call, an offer, a commercial decision.

Tangible results

3 months

Average advance warning before departure

Score per customer

Portfolio ranked by risk level

Value quantified

Amount of the portfolio at risk, tracked over time

Up and running in 4 to 6 weeks

From specification to deployment, with visible results from the first few weeks.

Rapid scoping (1 week)

Working prototype at the halfway point

Deployment and training included

Frequently asked questions: Churn signal detection

Dozens of indicators: falling purchase frequency, declining engagement, rising complaints, prolonged silence in response to communications, a deteriorating NPS score.

The model is trained on your actual history of departures, and the trigger threshold is set to keep alert volumes manageable for teams. Accuracy improves as feedback from the field qualifies the predictions.

The gain depends more on the quality of the actions taken after the alert than on the model itself. The system's measurable contribution lies in the advance warning, around three months, and in prioritising the portfolio by value at risk. The rest is up to your teams, with tracking of how retention actions convert.

Alerts arrive in the CRM, by email or through your collaboration tools (Teams, Slack), each with the risk score, the signals detected and the suggested actions.

More workflows: Customer Service

Every purchase triggers a follow-up

Automated after-sales follow-up

The weeks after a purchase decide the relationship, and that is precisely when follow-up slips for lack of time. Messages stay generic, reminders arrive at the wrong moment, and opportunities for loyalty or cross-selling are missed.

3 to 4 weeks

Discover

-50% response time

Customer support assistant

Repetitive questions about products, orders and services take up most of support teams' time. Response times lengthen, answer quality depends on the agent handling it, and customers compare with the responsiveness they find elsewhere.

4 to 6 weeks

Discover

-40% handling time

Complaint categorisation and routing

Complaints arrive through several channels and are sorted by hand. The time spent qualifying and directing each one delays the response, approximate assignments add a transfer, and urgent cases move at the same pace as the rest of the flow.

3 to 5 weeks

Discover

See all workflows: Customer Service