ML Pipeline
Telecom
Financial Services
Python
Customer Churn Prediction Pipeline
This project shows how a retention team can turn customer history into a practical churn-risk queue. Users can train the model, score customers, review the main risk drivers and export an outreach list that links model output to revenue at risk.
34%
Churn Reduction (pilot)
Problem Statement
Retention teams often know churn is happening, but not early enough to act. This build shows how customer behaviour, billing history and service patterns can be converted into a ranked list of customers who need attention before they cancel.
Business Impact: A 1% reduction in monthly churn for a 500,000-subscriber
base represents ~$2.4M in preserved ARR per year (at ARPU $40/month).
Pipeline Architecture
A practical scoring flow from customer data to features, model training, risk scoring and retention action.
Use Cases & Applicability
This methodology extends beyond telecoms. Sectors where this pipeline delivers value:
Financial Services
Bank Account Attrition
Predict which current account holders are likely to transfer to competitor banks within 60 days.
Insurance
Policy Non-Renewal
Identify policyholders unlikely to renew at end of term, enabling underwriter outreach before expiry.
NGO / Donor Management
Donor Lapse Prediction
Forecast which recurring donors are at risk of lapsing, informing targeted re-engagement campaigns.