Analyze customer behavior and predict churn risk
| Name | LTV | Purchases | Avg Order | Churn Risk | Segment | |
|---|---|---|---|---|---|---|
| Sarah Chen | [email protected] | $1.0K | 24 | $625 | 5% | high value |
| Marcus Johnson | [email protected] | $0.1K | 12 | $708 | 8% | high value |
| Emma Rodriguez | [email protected] | $0.2K | 5 | $640 | 72% | at risk |
| James Wilson | [email protected] | $0.0K | 2 | $70 | 35% | new |
| Lisa Anderson | [email protected] | $0.0K | 1 | $0 | 95% | inactive |
2 customer(s) have a churn risk above 70%. Consider reaching out with personalized offers or loyalty incentives.
1 new customer(s) detected. Send onboarding emails and educational content to increase engagement.
2 high-value customer(s) are your top performers. Create VIP programs and exclusive offers to retain them.
1 inactive customer(s) haven't purchased recently. Send win-back campaigns with special discounts.