Predictive Churn Analysis
Telecom Provider · Telecommunications
Created predictive ML models identifying at-risk customers before churn occurs for proactive retention.
! The Challenge
The telecom provider was losing 8–12% of customers per quarter with no early-warning system, making timely retention interventions impossible.
→ Our Solution
Built a churn prediction model analyzing 200+ features from usage, billing, and support interactions, paired with automated retention workflows.
✓ Results
- 40% of at-risk customers successfully retained
- ฿15M annual cost savings
- 87% model accuracy (AUC-ROC)
- 850% ROI within 12 months
Tech Stack
Duration
4 months
Key Outcome
40% churn prevention, saved ฿15M/year
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