How to Calculate Customer Health Scores That Actually Mean Something

Customer health scores are a powerful tool for predicting churn, identifying upsell opportunities, and proactively engaging accounts before issues escalate. But too often, these scores are either vague or oversimplified — reducing rich customer experiences to a single number with little context.

In this post, we’ll break down what actually goes into a meaningful customer health score, how to calculate it, and why each element matters. Whether you're in Customer Success, Sales, or Product, this guide will help you turn scattered support data into strategic insight.

What Is a Customer Health Score?

A customer health score is a composite metric that reflects how well a customer is doing with your product or service. It aims to predict the likelihood of retention, expansion, or churn by combining various signals into a single index or tier (e.g., “Healthy,” “At Risk,” “Critical”).

But unlike a Net Promoter Score (NPS) or a CSAT survey, a good health score isn’t just a snapshot — it’s a moving picture that incorporates behavior, satisfaction, and support history over time.

Key Metrics to Include in Your Customer Health Score

Let’s break down the most impactful components of a health score and how they can be quantified.

1. CSAT Scores (Customer Satisfaction)

What it tells you: Immediate feedback after interactions, usually support-related.
How to use it:
Aggregate CSAT over the past 30/60/90 days. A low average or a recent drop may signal dissatisfaction.

📊 Example: If a customer averages below 3.5 out of 5 over their last 5 support cases, mark this as a negative indicator.

2. Number of Times Contacting Support

What it tells you: Friction or confusion with the product — or high engagement.
How to use it:
Track the volume of inbound support touches over a set period (e.g., last 30 or 90 days). High volume paired with low CSAT = trouble. High volume and high CSAT? They may just be power users.

📊 Example: More than 10 contacts in a month with mixed CSAT may indicate product usability concerns.

3. Support Case Topics

What it tells you: Are they asking about bugs, feature gaps, or billing issues?
How to use it:
Group topics into categories: critical product blockers, feature education, or low-impact issues. Critical blockers should weigh more heavily.

📊 Example: 3+ tickets flagged “integration failure” in 60 days = high-risk signal.

4. Number of Support Cases Over Time

What it tells you: Ongoing strain or increasing need for help.
How to use it:
Trend case volumes monthly. An increase could indicate rising friction or onboarding struggles.

📊 Example: A 2x spike in monthly case volume from a previously quiet customer is worth investigating.

5. Time to Resolve / Callbacks

What it tells you: Customer perception of support quality and internal efficiency.
How to use it:
Average your resolution times. Long times (or frequent callback delays) reduce trust. Flag accounts where more than 30% of issues take longer than SLA targets.

📊 Example: Average resolution >48 hours for the last 5 tickets = health score penalty.

6. Renewal Window Approaching

What it tells you: Immediacy of risk or opportunity.
How to use it:
Add weighted urgency if the customer is within a 90-day renewal window. Combine this with other data points to prioritize interventions.

📊 Example: Customer is 45 days from renewal, had 3 recent critical support tickets = high-touch needed.

7. Product Utilization

What it tells you: Whether the customer is getting value from your product.
How to use it:
Track logins, usage of key features, or integrations. Low utilization — especially for paying tiers — is a strong churn predictor.

📊 Example: Customer has not logged in for 2 weeks and hasn't used key modules in 30 days = high risk.

How to Calculate It All Together

You don’t need to build a PhD-level algorithm, but you do need a weighted formula that reflects business priorities. Here's a sample scoring framework:

Sample Scoring Framework

• Avg. CSAT (20%): Less than 3.5 stars = -20

• Support Contact Volume (15%): More than 8 contacts/month = -15

• Support Topics (15%): Includes critical bugs = -20

• Time to Resolve (10%): Average resolution time > 48 hours = -10

• Renewal Window (10%): Within 60 days of renewal = +15 or -15

• Product Utilization (30%): Using less than 30% of key features = -30

Then, bucket your totals:

• 70–100 = Healthy

• 40–69 = At Risk

• <40 = Critical

Final Tips

• Keep it dynamic: Update weekly or monthly, not quarterly.

• Include qualitative notes: Add CSM observations to validate or override scores.

• Make it actionable: Tie thresholds to playbooks — don’t just monitor, intervene.

The Takeaway

A good customer health score should tell you more than whether someone’s happy — it should guide your next move. By combining support behavior, product usage, and key business timing (like renewals), you can build a score that doesn’t just describe the customer experience — it predicts what happens next.

Want help designing a customer health scoring system that fits your support stack? Let’s talk.

👉 Reach out to Advising.LA to talk through your goals — no sales pressure, just smart strategy.

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