Reducing customer churn by 28% with predictive analytics
Telecommunications provider | Telecommunications | 3 months
The challenge
The telecommunications provider was experiencing high customer churn rates but couldn't identify at-risk customers until they had already decided to leave. Retention efforts were reactive and largely ineffective.
Our solution
We built a churn prediction model that identifies at-risk customers weeks in advance, enabling the retention team to take proactive measures with personalised offers and interventions.
Results
- ✓28% reduction in customer churn
- ✓3x improvement in retention campaign effectiveness
- ✓15% increase in customer lifetime value
- ✓Better targeting of retention resources
Technologies used
PythonScikit-learnGoogle Cloud
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