Customer Churn: How to Spot At-Risk Customers Before They Leave
Your best customers are quietly leaving. RFM scoring identifies who's at risk — and personalized win-back offers bring them home.
The Silent Exodus You Can't See
Maria used to come in every Tuesday and Friday. Her average basket was $47. Then she stopped coming on Fridays. Then Tuesdays became every other week. Then she disappeared entirely. Customers don't announce they're leaving. They just gradually stop coming.
What Is RFM Scoring?
RFM stands for Recency, Frequency, Monetary:
- Recency: How many days since their last purchase?
- Frequency: How often do they shop?
- Monetary: How much do they spend per visit?
When a customer's recency suddenly increases, that's your early warning signal.
Win-Back Offers That Work
Generic "Come back! 10% off!" doesn't work. Personalized offers based on purchase history work 3x better:
- Maria always bought carne asada → "Maria, premium Angus carne — $2 off your next visit"
- Carlos was a craft beer regular → "New IPA just landed, Carlos — 20% off first 6-pack"
The Numbers
- Acquiring a new customer costs 5x more than retaining an existing one
- Win-back campaigns achieve 10–25% recovery rates
- A retained high-value customer is worth $2,400–$5,000/year
KairosPal's Churn Prevention engine scores every customer using RFM analysis and generates personalized win-back offers automatically. Start your free demo.