Identifying cancellation risk weeks before it materialises — enabling targeted retention interventions that protect revenue without blanket discounting.
Energy companies operating in liberalised markets face high churn driven by price comparison and switching incentives. Reactive retention — responding to cancellation notices — is both expensive and too late. ML churn prediction enables proactive intervention weeks earlier, at lower cost, when retention is still possible without margin-destroying discounts.
Managing churn in energy, telecoms, or a subscription business? We'd be happy to discuss your churn economics before this case study is published.
Discuss Your Challenge →Reactive retention after cancellation notice — too late, too expensive, and not targeted
ML churn scoring with 60–90 day horizon, integrated with CRM-triggered retention campaigns
Measurable reduction in contract cancellations and improved retention cost-per-saved-customer