customer churn
Customer churn prediction that warned a telecom provider weeks earlier
Customer churn prediction gave this telecom provider something it never had: a warning before customers cancelled. Combining usage, billing and support data into early-warning dashboards, and triggering retention offers in time, cut churn by 19% and flagged risk 3 weeks earlier.
Illustrative example. Client details are anonymised and representative of the work we do.
- Industry
- Telecom
- Services
- Marketing Analytics Services + more
- Platforms
- Power BI
- Duration
- 5 months
Where they started
The team only learned customers were unhappy when they cancelled.
The approachWhat we changed
We combined usage, billing and support data into early-warning dashboards and triggered retention offers before customers left.
What other businesses can take from this
Subscription businesses usually record the cancellation but not the warning signs before it. The signs are there: falling usage, a disputed bill, repeated support calls. They sit in different systems owned by different teams, so nobody sees them together until the customer has already decided to leave.
Other subscription businesses can take from this that churn prediction starts with joining data you already hold, rather than with a complex model. Once usage, billing and support data sit in one view, risk becomes visible weeks ahead. Offers then reach customers while they can still be persuaded, and acceptance of retention offers rose 14%.
Telecom, broadband and SaaS firms with plenty of customer data and no early warning system are the natural fit.
What moved
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