Customer Churn Prediction for Telecoms
The challenge
Keeping a customer costs less than winning a new one, yet many customers leave without warning.
What we built
We built an AI model that predicts which customers are likely to leave, using their history together with external data.
Retention teams see which customers are at risk and why, so offers go to the right people at the right time. The model has been demonstrated in a realistic setting.
Business value
- Earlier warning of customers at risk
- Better targeted retention offers
- A clearer view of why customers leave
- Retention budget spent where it counts
Where else it applies
The same approach transfers to banking, insurance, energy retail, SaaS, and media subscriptions. It belongs to our Prediction and Forecasting work: past data turned into estimates of what is likely to happen next, so teams plan ahead instead of reacting.
Task
Spot customers who are about to leave, before they do.
Let’s build together
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