Computing churn rate
Churn rate = customers lost in the period / customers at the start × 100%
An example: 10,000 active customers at the start of a quarter, 1,200 of whom made no purchase within
90 days → churn rate = 12%.
Choosing the churn window
The window depends on the category — on the typical gap between purchases:
| Category | Typical purchase frequency | Churn window |
|---|---|---|
| FMCG, groceries | Weekly to monthly | 30–60 days |
| Fashion, apparel | Two to four times a year | 90–120 days |
| Electronics | Once or twice a year | 180–365 days |
| Furniture, renovation | Less than once a year | 365+ days |
Churn prediction: how the model works
The model studies the features that precede churn:
- Falling visit frequency: three times a week before, once a month now
- Shallower sessions: fewer pages per visit
- RFM movement: recency rising, frequency falling
- No reaction to campaigns: open rate declining
From those features it assigns each customer a churn score and enrols the high-risk ones into a
retention segment.
Retention scenarios at high churn risk
Churn score above the threshold → retention trigger:
├── Email with a personal selection
├── Push notification with a personal offer
├── An onsite banner on the next visit
└── Personalized recommendations on the homepage
Reactivating in time works several times better than working with customers who have already gone.