Customer Health Score Calculator
Calculate customer health score from product usage, support tickets, and NPS for churn prediction.
Formula
Health Score = (Engagement x 0.30 + Recency x 0.20 + Support x 0.15 + NPS x 0.20 + Growth x 0.15) x Tenure Factor
The health score combines five weighted dimensions: product engagement from login frequency and feature usage, recency of last activity, support ticket health, NPS satisfaction, and revenue growth trajectory. A tenure factor adjusts for account maturity, slightly boosting scores for long-term customers.
Worked Examples
Example 1: Healthy Enterprise Customer Assessment
Problem: An enterprise customer logs in 18 times/month, uses 75% of features, has 2 support tickets, NPS of 9, $48K contract, last login 1 day ago, 18-month tenure, and 20% expansion revenue growth.
Solution: Engagement: (min(18/20,1) x 100 x 0.4) + (75 x 0.6) = (90 x 0.4) + 45 = 81\nRecency: 100 (1 day)\nSupport: 90 (2 tickets)\nNPS: 90 (9/10 x 100)\nGrowth: 67 (20/30 x 100)\nRaw: (81x0.30 + 100x0.20 + 90x0.15 + 90x0.20 + 67x0.15) x 1.05 = 90.2\nHealth Score: 90
Result: Health Score: 90 | Risk Level: Healthy | Churn Probability: 5% | Revenue at Risk: $2,400
Example 2: At-Risk SaaS Customer Intervention
Problem: A customer logs in 4 times/month, uses 30% of features, has 8 support tickets, NPS of 5, $18K contract, last login 12 days ago, 8-month tenure, and 2% expansion.
Solution: Engagement: (min(4/20,1) x 100 x 0.4) + (30 x 0.6) = (20 x 0.4) + 18 = 26\nRecency: 50 (12 days)\nSupport: 40 (8 tickets)\nNPS: 50 (5/10 x 100)\nGrowth: 6.7 (2/30 x 100)\nRaw: (26x0.30 + 50x0.20 + 40x0.15 + 50x0.20 + 6.7x0.15) x 1.0 = 37.8\nHealth Score: 38
Result: Health Score: 38 | Risk Level: Critical | Churn Probability: 65% | Revenue at Risk: $11,700
Frequently Asked Questions
What is a customer health score and why is it important?
A customer health score is a composite metric that predicts the likelihood of a customer renewing, expanding, or churning based on their behavior and engagement patterns. It aggregates multiple signals including product usage, support interactions, satisfaction surveys, and business metrics into a single actionable number, typically on a 0-100 scale. Health scores are critical for SaaS businesses because they enable proactive intervention before customers churn. Companies using health scoring report 10-20% improvement in net revenue retention. Without health scores, customer success teams react to cancellation requests rather than preventing them, missing the window where intervention is most effective.
What factors should be included in a customer health score?
An effective customer health score should incorporate both leading and lagging indicators across multiple dimensions. Product engagement metrics like login frequency, feature adoption breadth, and time spent in the product reveal whether customers are getting value. Support metrics including ticket volume, sentiment, and resolution satisfaction indicate frustration levels. Relationship metrics such as NPS scores, executive sponsor engagement, and responsiveness to outreach reflect relationship quality. Financial metrics like payment history, expansion revenue, and contract value signal commercial commitment. The specific weights for each factor should be calibrated using historical churn data from your customer base to identify which signals are most predictive for your product.
How do I interpret different health score ranges?
Health scores typically fall into four actionable categories that drive different customer success strategies. Scores of 80-100 indicate healthy customers who are engaged, satisfied, and likely to renew. These customers are candidates for expansion conversations and case study requests. Scores of 60-79 represent neutral customers who are using the product but may not be fully realizing its value. Proactive check-ins and enablement sessions work well here. Scores of 40-59 flag at-risk customers showing warning signs like declining usage or increasing support tickets, requiring immediate outreach and remediation plans. Scores below 40 indicate critical accounts with high churn probability, demanding executive-level intervention and potential rescue offers.
How does NPS relate to customer health and churn prediction?
Net Promoter Score correlates strongly with retention outcomes but should never be the sole health indicator. Promoters scoring 9-10 have renewal rates 15-25% higher than detractors scoring 0-6. However, NPS captures a point-in-time sentiment that may not reflect actual product usage. A customer might give a high NPS during a honeymoon period but churn later due to poor adoption. Conversely, a low NPS from a power user experiencing a temporary frustration does not necessarily indicate churn risk. The most effective approach weights NPS alongside behavioral data, giving NPS approximately 15-25% of the total health score weight. Track NPS trends over time rather than single responses for more reliable predictions.
How should support ticket volume be weighted in health scoring?
Support ticket interpretation requires nuance because both extremes can indicate problems. Zero tickets might mean the customer is self-sufficient and happy, or it might mean they have disengaged and stopped trying to make the product work. Very high ticket volumes clearly indicate frustration and friction. The sweet spot of 1-3 tickets per month often indicates active engagement with the product. More important than volume is ticket sentiment and resolution satisfaction. Unresolved tickets, repeated issues, and escalations are much stronger churn signals than ticket count alone. Weight support metrics at 10-20% of total health score, and consider tracking the ratio of feature requests to bug reports as a proxy for customer investment in the product.
How often should customer health scores be recalculated?
Health scores should be recalculated at least weekly for meaningful trend detection, though daily updates are ideal for SaaS products with granular usage data. Real-time scoring enables immediate alerts when a customer crosses critical thresholds, allowing same-day intervention. However, avoid over-reacting to daily fluctuations by implementing smoothing algorithms that weight recent behavior more heavily while maintaining historical context. Use 30-day rolling averages for stable metrics like feature adoption and 7-day windows for volatile metrics like login frequency. Establish alert triggers for significant score drops of 15 or more points within a week, and generate automated reports for customer success managers showing score trends for their portfolio at the start of each week.