Indian Neobank Churn Intelligence Dashboard
Case study for a real-time churn prediction dashboard built around UPI behavior, inactivity windows, and early balance signals.
Indian neobanks lose a significant share of customers in the first six months, and most teams realize it only after the user has already left. The churn is not the surprise. Missing the warning signs is.
I built a live churn intelligence dashboard that surfaces which customer segments are at risk and why, using three behavioral signals that matter in the Indian fintech context: UPI transaction frequency, inactivity windows, and early balance patterns combined with CIBIL scores.
The dashboard is interactive, filterable by state, and designed so every chart points to a business decision instead of being decorative.
What the dashboard reveals:
- 17.3% of customers are predicted to churn, representing 865 customers at risk right now.
- Customers making fewer than 5 UPI transactions per month churn at 29.3%, nearly 3x higher than active users.
- New customers under 6 months with balances below Rs. 10,000 churn at 57.1%, making them the most urgent cohort.
- Tamil Nadu shows the highest churn rate at 19.7% among the major states in the dataset.
- 74 customers already show early warning signals but still have CIBIL scores above 650, making them strong intervention candidates.
Want this running on your actual customer data? I can have a working first version live in 48 hours.
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