Predictive analytics · Python
Turning churn predictions into business decisions.
Can a churn model produce a useful retention strategy instead of another probability score?
01 / The problem
A churn score only matters if it changes an action.
I analyzed 10,000 retail-banking customers with a churn rate of roughly 20%. The project connected model performance to specific retention actions instead of stopping at a ranked model table.
The business needed enough recall to find at-risk customers, but low precision would waste outreach. No single metric could decide that trade-off without the campaign context.
02 / The approach
Compare models, then connect signals to retention.
- 01
Explored churn patterns across age, geography, balance, activity, and product use.
- 02
Compared six classification models with five-fold cross-validation.
- 03
Created behavioural features such as product usage, engagement, and balance-to-salary ratio.
- 04
Used PyCaret as a second check on the manually built pipeline.
03 / The result
The output was a ranked intervention plan.
Age, number of products, activity, and balance were the strongest signals. The practical output was a set of targeted retention ideas and a plan for ranking customers by intervention priority.