Predictive Customer Churn Probability & Health Score Calculator

Customer Success: Moving from Reactive to Predictive Churn Prevention

Traditional subscription companies discover churn reactively when an account hits the cancellation button. High-performing Customer Success teams track predictive leading indicators:

• Telemetry & Logins: A 30%+ drop in weekly active user logins is the earliest symptom of impending churn.

• Unused Licenses: If an account paid for 50 seats but only 12 are active, the account will downgrade or cancel at renewal.

• Support Escalations: Unresolved tickets create executive dissatisfaction.

How to Use This Calculator

Enter your Product Usage: Active Days in Last Month (0 to 30 Days) (e.g. 8 (Days account was logged into)), Core Feature Adoption Depth (%) (e.g. 35.0 (% of core product features actively used)), Open / Escalated Support Issues (Last 60 Days) (e.g. 3 (Unresolved or high-priority tickets)), NPS / CSAT Satisfaction Rating (0 to 10) (e.g. 5 (0-6 Detractor, 7-8 Passive, 9-10 Promoter)), Account Monthly Recurring Revenue ( / month) (e.g. 450.0), and Days Remaining Until Contract Renewal (e.g. 45 (Days to renewal deadline)). Results update instantly as you type — adjust any value to compare scenarios.

The Formula

Customer Health Score & logistic churn probability equations: Health Score (0-100) = w_1((Active Days) / (30)) + w_2(Feature Adoption \%) + w_3(Support Score) + w_4((NPS) / (10)) Churn Probability (\%) = (1) / (1 + (Health Score - 45)10) × 100\% At-Risk Monthly Revenue (MRR) = Account MRR × Churn Probability (Fraction) At-Risk ARR Drain = At-Risk MRR × 12

Worked Example

For example, evaluating a 450/month SaaS customer with 8 active login days in the last month, 35% core feature adoption, 3 escalated support tickets, an NPS score of 5/10, and 45 days until renewal: Health Score evaluates to . The logistic churn probability is . Expected at-risk revenue equals 359.10/month, requiring immediate executive intervention.

Understanding Your Result

Marketing metrics are most useful as trends over time rather than isolated snapshots. Benchmark your result against your own historical performance first, then against industry averages. Attribution models (last-click vs. multi-touch) can produce dramatically different channel ROI figures — ensure consistent attribution methodology before making channel budget decisions. Statistical significance requires sufficient sample size: most A/B test decisions need a minimum of 1,000 conversions per variant for reliable results.