Customer Churn & Retention: A Predictive Risk Model

A predictive churn model on 7,000+ telecom subscriber records, quantifying revenue at risk and identifying the highest-leverage retention actions.

26.5%Baseline churn rate

$139K/moRecurring revenue at risk

0.845Random forest AUC

~50%Of churners caught in top 20% risk

Overview

Built a predictive churn model on a 7,043-customer telecom subscriber base to flag at-risk accounts before they cancel, then translated the model's output into a monthly revenue-at-risk figure and a prioritized retention list — the kind of deliverable a subscription or membership business would hand straight to its retention team.

Method

Python-based data pipeline: cleaning, encoding, and a stratified 75/25 train/test split
Logistic regression (interpretable baseline) and random forest (nonlinear comparison), both evaluated on accuracy, precision, recall, and AUC
Risk-scored the full customer base and ranked by predicted churn probability to test how much revenue a targeted campaign could realistically protect

Results

Bar charts showing churn rate by contract type and by customer tenure — Churn concentrates heavily in month-to-month contracts and in the first 6 months of a customer's tenure.

Horizontal bar chart of the top 10 churn drivers by random forest feature importance — Top 10 churn drivers by random forest feature importance — tenure and contract length dominate.

Model comparison on the held-out test set (1,761 customers)
Model	Accuracy	Precision	Recall	F1	AUC
Logistic Regression	75.0%	51.9%	79.7%	0.628	0.846
Random Forest	75.5%	52.4%	80.7%	0.636	0.845

Key Findings

Baseline churn rate of 26.5% represents roughly $139K/month ($1.67M annualized) in recurring revenue at risk
Contract type is the dominant lever: month-to-month customers churn at 42.7% vs. 11.3% (one-year) and 2.8% (two-year contracts)
New customers are the highest-risk group: 52.9% churn in the first 6 months, falling to 9.5% after 4+ years of tenure
Targeting just the riskiest 20% of customers captures about half of all churners — roughly $18K/month in revenue a retention team could focus on instead of blanket outreach
Fiber-optic subscribers churn at more than double the rate of DSL subscribers (41.9% vs. 19.0%), pointing to a service-quality or pricing friction point worth investigating independent of the model

Deliverable

Full analysis delivered as a Jupyter notebook — data pipeline, model comparison, driver analysis, and a prioritized customer risk list — with embedded outputs and charts.