Rethinking Customer Health Scores - Why Your Churn Prediction Model is Failing
David Pinto | Principal Consultant | RenewalsHub
December 2024
Customer health scores are meant to help teams spot risk early, intervene proactively and prevent churn. But for many organizations, these scores fail to deliver on that promise - and churn remains high.
Yet, despite good intentions, many companies still experience high churn rates, even with customer health scoring systems in place.
The problem isn’t the concept of health scores, it’s that many health scores rely on outdated, incomplete, or irrelevant metrics, making them ineffective at predicting and preventing churn.
Using insights from the RenewalsHub Renewals Maturity Model, part of the broader RenewalsHub Renewals Transformation Framework™, we examine why traditional health scoring models often fail and how to develop a more predictive and accurate approach to improve renewal outcomes. Not sure how advanced your current model is? Take our free 2-minute Renewals Maturity Self-Assessment to benchmark your starting point.
Why Most Health Scores Fail to Predict Churn
Many customer health scores use a combination of these factors:
Net Promoter Score (NPS)
Customer satisfaction surveys (CSAT)
Product usage (logins, time spent)
Customer support ticket volume
Account manager or CSM sentiment
While these metrics aren’t necessarily wrong, they’re often too generic, lagging, or superficial, which makes them unreliable indicators of actual customer health.
Common pitfalls include:
Over-reliance on NPS scores: High NPS doesn’t always correlate with renewal likelihood; a customer may love your product - and still churn for reasons your score isn’t tracking.
Usage metrics alone are misleading: Just because a customer logs in frequently doesn’t mean they're seeing tangible business value. Conversely, infrequent logins might indicate efficient adoption rather than dissatisfaction.
Delayed feedback loops: Health scores based on quarterly surveys or delayed usage reports may miss critical churn signals that appear quickly.
Bottom Line: If your health scores don’t reflect renewal behavior, they’re not helping—they’re hurting.
The New Approach: Predictive Customer Health Scoring
To effectively prevent churn, your customer health scores must evolve from reactive and subjective measures to predictive, actionable analytics.
A predictive health score accurately forecasts churn by capturing:
Real business outcomes
Multi-threaded stakeholder engagement
Quality (not just frequency) of product adoption
Early signs of risk, like declining engagement or organizational changes
Here are 5 steps for how to build a robust predictive customer health score:
1. Measure Value Realization, Not Just Sentiment
Focus on real outcomes tied to customer success milestones.
Move beyond satisfaction metrics (like NPS) to measure value realization, including:
Has the customer achieved measurable outcomes?
Are they meeting their business objectives?
Do they attribute their success to your product or service?
Example Metrics:
Productivity or efficiency improvements
Cost savings or ROI directly attributed to your solution
Achievement of customer-specific success milestones
Customers who see and acknowledge clear value rarely churn.
2. Track Stakeholder Depth & Breadth
Single-threaded accounts = higher risk.
Customers with relationships across multiple stakeholders consistently renew at higher rates. Single-threaded accounts (those depending on one champion) pose significant renewal risk — especially when there’s no visibility or planning across the People & Roles component of the Renewals Engine.
Include engagement metrics such as:
Number of active relationships within the customer’s organization
Frequency and quality of executive-level engagement
Breadth of adoption across different departments or teams
Multi-threaded relationships significantly reduce churn by anchoring your value broadly within the customer's business.
3. Prioritize High-Value Feature Usage
Deep adoption is a better predictor than daily logins.
Most health scores measure how often customers use the product, rather than how deeply they engage with critical features. Quantity alone isn’t predictive — it’s the quality of adoption, tied to Data & Insights from the Renewals Engine, that matters most.
Key indicators of quality feature adoption:
Are customers using high-impact features tied to success?
Do users explore advanced features or only basic functionalities?
Are key users continually leveraging the solution in critical workflows?
If customers aren't unlocking strategic value, frequent usage alone won’t guarantee renewal.
4. Don’t Ignore External Risk Signals
Budget cuts, leadership changes and payment issues matter.
Many churn signals come from factors outside basic usage or satisfaction scores. Predictive models must integrate these additional signals:
Changes in customer’s executive leadership or decision-makers
Customer financial health or budget changes
Recent support issues, escalations, or unresolved tickets
Changes in contract terms, late payments, or requests for shorter contracts
These subtle churn indicators often predict churn more accurately than traditional metrics.
5. Test, Tune and Recalibrate Health Scoring Constantly
If it didn’t catch the last churns, it’s time to adjust.
A customer health score shouldn’t be static, it must evolve. Routinely test its predictive accuracy by asking:
Did our health scores accurately predict our last churned accounts?
What common indicators did our recent churns share?
How can we better weight indicators to predict future churn more reliably?
Periodic recalibration ensures your model remains predictive, relevant and actionable - a key practice in organizations advancing through the Renewals Strategy Lifecycle phase of the RenewalsHub Renewals Transformation Framework™.
Predictive Health Scores Drive Measurable Renewals Impact
Companies struggling with ineffective churn prediction models built around basic usage metrics and quarterly NPS surveys can significantly improve renewal performance outcomes by adopting the disciplined predictive customer health scoring approach outlined above. Typical results achieved through this disciplined approach include:
35% improvement in churn prediction accuracy
30% reduction in churn rate over 12 months
Improved renewal forecasting and revenue predictability
Stronger alignment across Customer Success, Sales and Renewals roles through clearly defined People & Roles and Coverage Strategy
Final Thoughts: Make Your Customer Health Scores Work for You
Traditional customer health scores give false confidence because they’re based on reactive, limited and superficial indicators. A predictive, value-driven model is the best way to protect your recurring revenue and secure long-term customer loyalty.
Renewals are predictable - if you measure the right things.
Ready to Build a Predictive Health Model That Actually Works?
RenewalsHub helps companies build predictive health scoring systems, automate renewals and improve retention through the RenewalsHub Renewals Transformation Framework™ - including the Maturity Model, Strategy Lifecycle and Renewals Engine.
Want a fast pulse check before going deeper? Take our free Renewals Maturity Self-Assessment to get an instant snapshot of where you stand.