The Challenge
No visibility into churn risk across 795 schools serving 6,500+ UK primary schools. Reactive customer success approach led to revenue leakage. Manual reporting took hours with limited actionable insights.
Our Solution
Engineered ML churn prediction system with 38-feature pipeline analyzing activity momentum, RFM health scores, and engagement patterns. Built renewal risk assessment enabling proactive interventions 120 days before renewal. Developed AI feedback categorization using LangChain and OpenAI.
The Results
92% model confidence identified 63 at-risk accounts representing £42k revenue exposure. Enabled proactive customer success with automated priority scoring. MySQL to BigQuery migration reduced report generation time by 80%.
Quick Facts
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- EdTech
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Get StartedWhat Our Client Says
"The ML churn prediction system gave us a 120-day head start on at-risk accounts. We went from reactive firefighting to proactive customer success with clear prioritization."
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