Health Scoring Framework

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Health Scoring Framework

Complete methodology for multi-dimensional customer health scoring in SaaS customer success.

Overview

Customer health scoring is the foundation of proactive customer success management. A well-calibrated health score enables CSMs to prioritise their portfolio, identify emerging risks before they become churn events, and allocate resources where they will have the greatest impact.

This framework uses a weighted, multi-dimensional approach that scores customers across four key areas: usage, engagement, support, and relationship. Each dimension contributes to an overall health score (0-100) that classifies accounts as Green (healthy), Yellow (needs attention), or Red (at risk).

Scoring Dimensions

1. Usage (Weight: 30%)

Usage metrics are the strongest leading indicator of customer health. Customers who are not using the product are not deriving value and are at elevated churn risk.

Metric	Definition	Scoring Method
Login Frequency	Percentage of expected login days with actual logins	(actual / target) * 100, capped at 100
Feature Adoption	Percentage of available features actively used	(adopted / available) * 100, capped at 100
DAU/MAU Ratio	Daily active users divided by monthly active users	(actual / target) * 100, capped at 100

Sub-weights within Usage:

Login Frequency: 35%
Feature Adoption: 40%
DAU/MAU Ratio: 25%

Why 30% weight: Usage is the most objective, data-driven signal. Declining usage almost always precedes churn. However, some customers may have seasonal usage patterns, which is why it is not weighted even higher.

2. Engagement (Weight: 25%)

Engagement measures how actively the customer participates in the relationship beyond just product usage.

Metric	Definition	Scoring Method
Support Ticket Volume	Number of support tickets in the period	Inverse score: (1 - actual/max) * 100
Meeting Attendance	Percentage of scheduled meetings attended	(actual / target) * 100, capped at 100
NPS Score	Net Promoter Score response (0-10)	(actual / target) * 100, capped at 100
CSAT Score	Customer Satisfaction score (1-5)	(actual / target) * 100, capped at 100

Sub-weights within Engagement:

Support Ticket Volume: 20% (inverse — fewer tickets is better)
Meeting Attendance: 30%
NPS Score: 25%
CSAT Score: 25%

Why 25% weight: Engagement signals complement usage data. A customer who attends meetings but does not use the product may be in an evaluation phase. A customer who uses the product but skips meetings may be becoming self-sufficient — or disengaging.

3. Support (Weight: 20%)

Support health measures the quality of the customer’s support experience, which directly impacts satisfaction and renewal likelihood.

Metric	Definition	Scoring Method
Open Tickets	Number of currently unresolved tickets	Inverse score: (1 - actual/max) * 100
Escalation Rate	Percentage of tickets escalated	Inverse score: (1 - actual/max) * 100
Avg Resolution Time	Average hours to resolve tickets	Inverse score: (1 - actual/max) * 100

Sub-weights within Support:

Open Tickets: 35%
Escalation Rate: 35%
Resolution Time: 30%

Why 20% weight: Support issues are lagging indicators — they tell you there is already a problem. However, unresolved support issues are a strong predictor of churn, especially when combined with declining engagement.

4. Relationship (Weight: 25%)

Relationship health measures the strength and depth of the personal connection between the customer and your organisation.

Metric	Definition	Scoring Method
Executive Sponsor Engagement	Engagement level of exec sponsor (0-100)	(actual / target) * 100, capped at 100
Multi-Threading Depth	Number of stakeholder contacts	(actual / target) * 100, capped at 100
Renewal Sentiment	Qualitative sentiment assessment	Mapped to score: positive=100, neutral=60, negative=20, unknown=50

Sub-weights within Relationship:

Executive Sponsor Engagement: 35%
Multi-Threading Depth: 30%
Renewal Sentiment: 35%

Why 25% weight: Relationship strength is the most important defence against competitive displacement. A customer with strong relationships will give you more chances to fix problems. A customer with weak relationships may leave without warning.

Classification Thresholds

Standard Thresholds

Classification	Score Range	Meaning	Action
Green	75-100	Customer is healthy and achieving value	Standard cadence, focus on expansion
Yellow	50-74	Customer needs attention	Increase touch frequency, investigate root causes
Red	0-49	Customer is at risk	Immediate intervention, create save plan

Segment-Adjusted Thresholds

Enterprise customers typically have higher expectations and more complex deployments, which means a higher bar for “healthy.” SMB customers may have simpler use cases and lower engagement expectations.

Segment	Green Threshold	Yellow Threshold	Red Threshold
Enterprise	75-100	50-74	0-49
Mid-Market	70-100	45-69	0-44
SMB	65-100	40-64	0-39

Segment-Specific Benchmarks

Each metric target is calibrated per segment. Enterprise customers are expected to have higher login frequency, attendance, and sponsor engagement. SMB customers have lower targets but still meaningful thresholds.

Example Calibration:

Enterprise login frequency target: 90% (high-touch, deeply embedded)
Mid-Market login frequency target: 80% (balanced engagement)
SMB login frequency target: 70% (self-serve oriented)

Trend Analysis

A single health score snapshot is useful. A health score trend is actionable.

Trend Classification

Trend	Criteria	Implication
Improving	Current > Previous by 5+ points	Positive trajectory, reinforce what is working
Stable	Within +/- 5 points	Maintain current approach
Declining	Current < Previous by 5+ points	Investigate and intervene
No Data	No previous period available	Establish baseline

Trend Priority Matrix

Current Score	Trend	Priority
Green	Declining	HIGH — intervene before it drops further
Yellow	Declining	CRITICAL — trajectory leads to Red
Yellow	Improving	MEDIUM — reinforce positive momentum
Red	Improving	HIGH — support the recovery
Red	Stable	CRITICAL — needs new intervention approach

Calibration Guidelines

When to Recalibrate

After major product changes: New features may change what “good usage” looks like
Seasonal patterns: Some industries have cyclical usage (retail holiday season, fiscal year end)
Portfolio composition changes: If you add many SMB customers, the overall averages shift
After churn events: Review whether the health score predicted the churn

Calibration Process

Export health scores for all customers over the past 12 months
Identify all churn events in the same period
Calculate the average health score of churned customers 90, 60, and 30 days before churn
Adjust thresholds so that churned customers would have been classified as Yellow or Red at least 60 days before churn
Validate with a holdout set of recent data

Common Calibration Pitfalls

Threshold creep: Gradually lowering Green thresholds to make the portfolio look healthier
Over-weighting lagging indicators: Support metrics react after the damage is done
Ignoring segment differences: Using one threshold for all segments
Sentiment bias: Over-relying on subjective renewal sentiment

Implementation Checklist

Define data sources for each metric (CRM, product analytics, support system)
Establish data refresh frequency (daily for usage, weekly for engagement)
Configure segment benchmarks for your customer base
Set initial thresholds using industry defaults (provided above)
Run a 30-day pilot with manual review of edge cases
Calibrate thresholds based on pilot results
Automate scoring and alerting
Review and recalibrate quarterly

Last Updated: February 2026