Analytics & RFM
Understand your customers with RFM (Recency, Frequency, Monetary) analysis, Customer Lifetime Value predictions, and churn risk assessment.
What is RFM Analysis?
RFM analysis is a customer segmentation technique that uses three key metrics to evaluate and categorize customers:
Recency
How recently did the customer make a purchase? More recent = higher score.
Frequency
How often does the customer purchase? More orders = higher score.
Monetary
How much does the customer spend? Higher total = higher score.
RFM Scores
Each customer receives a score from 1 to 5 for each RFM dimension, where 5 is the best. Scores are calculated relative to your store's customer base using quintile-based distribution.
Customer Segments
Based on RFM scores, customers are automatically classified into 12 segments:
| Segment | R | F | M | Description |
|---|---|---|---|---|
| Champions | 5 | 5 | 5 | Best customers. Bought recently, buy often, spend the most. |
| Loyal Customers | 3-5 | 4-5 | 4-5 | Buy regularly with high spending. |
| Potential Loyalists | 4-5 | 2-3 | 2-3 | Recent buyers with potential to become loyal. |
| Recent Customers | 5 | 1 | 1-2 | Just made their first purchase. |
| Promising | 3-4 | 1-2 | 1-2 | Showing early signs of engagement. |
| Needs Attention | 2-3 | 2-3 | 2-3 | Above average but haven't purchased lately. |
| About to Sleep | 2-3 | 1-2 | 1-2 | Below average, at risk of becoming inactive. |
| At Risk | 1-2 | 3-5 | 3-5 | Spent big but haven't returned in a while. |
| Cannot Lose | 1-2 | 4-5 | 4-5 | Used to be champions, now slipping away. |
| Hibernating | 1-2 | 1-2 | 1-3 | Low activity across all metrics. |
| Lost | 1 | 1 | 1 | Lowest engagement, likely churned. |
| New | 5 | 1 | 1 | Very recent first-time buyers. |
Additional Metrics
Customer Lifetime Value (CLTV)
Predicted total revenue a customer will generate over their relationship with your store. Calculated using purchase history and behavior patterns.
Churn Risk Score
Probability (0-1) that a customer will become inactive. Based on recency, purchase patterns, and engagement decline.
Average Order Value (AOV)
The average amount spent per order. Useful for identifying high-value vs. frequent small-purchase customers.
Purchase Interval
Average days between purchases. Helps predict when a customer is likely to buy again.
Using Analytics Data
Analytics data can be used in several ways:
- Customer Segmentation: Filter segments by RFM scores
- n8n Workflows: Access metrics via Customer > Get Metrics
- Customer Profiles: View individual customer analytics
- Dashboard: See segment distribution and trends