Tracking Churn and Retention in Subscription Models: Lessons from Telco
In the subscription economy, retaining customers is everything. Whether you’re running a SaaS startup or a telecom company, knowing why customers leave—and more importantly, who is likely to leave next—can mean the difference between growth and stagnation.
This is exactly the kind of problem I tackled in my Telco churn prediction project, where I used machine learning and Python to identify the behaviors and traits of customers who were likely to churn. This is not only a story about telecoms but also a blueprint that any subscription business can follow.
Why Churn Matters
Even a small improvement in retention can make a major impact. According to Stripe, increasing retention by just 5% can lead to a more than 25% boost in profits [1].
Zuora emphasizes that subscriber churn is a vital health metric for subscription-based businesses. It impacts everything from product-market fit to the effectiveness of your onboarding and support [2]. Paddle goes a step further, noting how predictive churn models can proactively improve customer success and product direction [3].
What the Telco Dataset Taught Me
Using the Telco customer dataset, I built an end-to-end churn analysis workflow with Python, pandas, scikit-learn, and seaborn. A few takeaways stood out:
- Tenure is everything: New customers were significantly more likely to churn, while long-term users were far stickier.
- Product mix influences churn: Customers using certain service combinations had higher churn rates—indicating the need for more tailored offerings.
- Contract type matters: Month-to-month plans had much higher churn than yearly or two-year contracts.
These patterns echo what subscription experts already know: churn is more than a number. It reflects product experience over time [2], [3].
Predicting the Drop-Off
I experimented with several machine learning models, including logistic regression, decision trees, and random forests, to predict churn likelihood. These models helped identify customers at risk based on usage patterns and demographic data. With this insight, businesses can:
- Trigger retention-focused emails or interventions
- Offer custom discounts or incentives
- Adjust contract types or service bundling
Lessons for Subscription Businesses
Whether you’re in telecom or SaaS, the core lessons apply:
- Look beyond averages: Segment customers by behavior and lifecycle stage.
- Track the why: Pair quantitative churn data with qualitative feedback.
- Act early: Prediction is only useful if it informs timely, proactive action.
- Review your product offerings: Sometimes churn is a product problem, not a customer one. Are you solving real problems? Are customers quietly disengaging before they leave?
Low engagement was a leading indicator of churn in the Telco dataset, suggesting unmet expectations long before cancellation.
Final Thoughts
If you’re building a subscription business and not tracking churn, you’re flying blind. Predictive analytics can reveal who’s likely to leave, and give you the tools to intervene before it’s too late.
Check out the full project and source code on GitHub.