Case study
Anticipating churn and targeting retention efforts
Previous caseClientTelecom operator, consumer and business markets.
Also relevant toEnergy, insurance, financial services, media, mobility, real estate services and SaaS.
Lower churn in at-risk segments.
Moving from reactive retention to predictive churn management, tailored to customer value.
The situation
Customers leave for many reasons: acquisition of short-lived customers, pain points in customer journeys, competing offers or changing needs. Retention actions often come late, and their cost can erode the value they preserve.
The challenge: act earlier and tailor efforts to each customer’s situation and profitability.
My role
With my teams and the business intelligence function, I reshaped the approach to churn across the whole customer lifecycle, from acquisition to retention.
What we did
- Review acquisition criteria to factor in their impact on future loyalty.
- Use behavioural signals and predictive models to detect churn risk.
- Automate the triggering of targeted actions.
- Combine pain point resolution, tailored offers and multichannel retention, according to segment value and profitability.