Customer Health Intelligence

Churn Risk Calculator

A multi-signal Customer Health Index that turns product, relationship, support, and Voice of Customer signals into a prioritized churn-risk workflow.

Tools used

Google SheetsHubSpotGainsightPower BI

Portfolio reconstruction. Customer-level details anonymized for confidentiality.

CHI weights and demo account data are illustrative for portfolio presentation.

Signal architecture

Four customer signal families feed one CHI score and one action queue.

Product Adoption & Usage

Usage, activation, adoption depth

MAU / WAUlogin frequencyfeature adoptionadoption depthonboarding completion

Engagement & Relationship

Sponsor health, cadence, success plans

executive sponsor healthmeeting cadenceresponsivenessstakeholder engagementsuccess-plan progress

Support & Technical Health

Severity, ageing, escalations

support ticket volumeP0/P1 issuesissue ageingescalationsunresolved product issues

Voice of Customer (VoC)

NPS, CSAT, sentiment

NPSCSATdetractor signalsqualitative feedbackcustomer sentiment

CHI Engine

Weighted Customer Health Score

15+ checkpoints rolled into four signal families.

CHI = (Adoption x 35%) + (Engagement x 25%) + (Support x 20%) + (VoC x 20%)

Flag account

Healthy, Watch, High Risk, or Critical.

Trigger CSM action

Route the account into the right playbook.

Track recovery

Monitor CHI movement until risk is resolved.

Interactive calculator

Test the CHI logic

Move any signal. The weighted CHI score, risk band, radar chart, and next-best action update instantly.

Product Adoption & Usage

35% CHI weight

RiskStrong

Engagement & Relationship

25% CHI weight

RiskStrong

Support & Technical Health

20% CHI weight

RiskStrong

Voice of Customer (VoC)

20% CHI weight

RiskStrong

Customer Health Index

CHI

Watch

Weakest signal: Support & Technical Health

Next Best Action

Target weakest signal with a 30-day plan

Signal balance

What is driving this customer's health score?

CHI weights and demo account data are illustrative for portfolio presentation.

Healthy

75-100

Maintain value and explore expansion.

Watch

55-74

Target the weakest signal with a 30-day intervention.

High Risk

35-54

Launch a formal customer recovery plan.

Critical

0-34

Executive save motion immediately.

Portfolio prioritization

Accounts sorted by CHI

A quick view of where CSM attention should go first.

Risk queue

Flagged accounts & next best action

Anonymized/demo portfolio data, sorted from highest risk to lowest risk.

Account	CHI	Trend	Risk	Primary Signal	Next Best Action
Enterprise E	32	-19	Critical	Adoption collapse + unresolved support	Save plan + leadership sponsor
Mid-market C	43	-14	High Risk	Low engagement + NPS detractor	Executive intervention
Scaled D	55	-5	Watch	Onboarding lag	Enablement sprint
Enterprise B	61	-8	Watch	Usage decline + 2 P1 issues	30-day recovery plan
Enterprise A	84	+6	Healthy	Strong adoption + sponsor engagement	Expansion discovery

Operating workflow

From signal to flag to intervention

The calculator is valuable because risk produces a clear CSM motion.

Ingest

Pull product, CRM, support, and customer-feedback signals.

Normalize

Convert 15+ checkpoints into comparable health signals.

Score

Calculate CHI and identify the weakest health pillar.

Flag

Classify the account as Healthy, Watch, High Risk, or Critical.

Route

Trigger the appropriate CSM playbook and next-best action.

Review

Track CHI movement until the risk is resolved.

Playbook logic

Data to decision to CSM action

CHI is not just a dashboard score. It translates customer health into a specific operating motion.

Healthy

Motion

Maintain value

Identify expansion opportunities

Watch

Motion

Identify weakest signal

Create targeted intervention

High Risk

Motion

Recovery plan

Increase customer engagement

Resolve blockers

Critical

Motion

Executive intervention

Save plan

Leadership sponsorship

The Problem

Customer health signals were fragmented across product usage, customer conversations, support issues, and feedback, making risk identification reactive.

The Approach

Created a Customer Health Index combining 15+ checkpoints across four signal families.

The Decision Layer

Instead of simply displaying metrics, the model translated health signals into risk categories and next-best actions.

The Outcome

The model identified 2 of 3 potential churns before formal notice and gave Customer Success teams a more structured way to prioritize intervention.

Impact

Designed to make churn risk visible before the renewal conversation.

Instead of relying on a single lagging indicator, the model combines behavioral, relational, technical, and customer-sentiment signals into one decision layer for Customer Success teams.

2 of 3

Potential churns identified before formal notice

15+

Risk checkpoints across the customer lifecycle