Reactive retention waits for the customer to click cancel and then tries to save them. Predictive retention identifies them weeks earlier — while there is still time and goodwill to intervene. For subscription businesses with meaningful customer counts, predicting churn is one of the highest-leverage analytical investments available.
The simplest way to predict churn
You do not need machine learning to start. A rule-based score using 4–6 signals catches the majority of at-risk customers:
- Recent skipped cycle — +20 points.
- Two skipped cycles in a row — +40 points.
- Recent failed payment — +30 points.
- Card expiring within 30 days — +25 points.
- No portal login in 60+ days — +15 points.
- Negative-sentiment support ticket — +30 points.
Sum across signals, anyone above a threshold (say, 50) gets flagged. Crude, fast, and surprisingly effective. Build it in a spreadsheet on a Monday and start using it Tuesday.
When to upgrade to machine learning
Move to ML-based prediction when:
- You have at least several thousand subscribers and a year of clean data.
- Your rule-based system is tuned and you want incremental lift, not a starting point.
- You have someone (in-house or consulting) who can maintain the model — not just build it once.
- You have measurable downstream interventions ready to consume the scores.
Don't skip the intervention design
The most common churn-prediction failure mode is a beautiful model with no follow-up. Before you build the prediction, design what happens to a high-risk customer: which save offer, which email, which manual outreach. The action is what generates the retention lift; the prediction just routes the customer to the action. See churn prediction model for the modeling side and win-back campaign for what happens after a customer does churn.