Predictive health scoring for a Series C SaaS platform
Replacing a colour-coded spreadsheet with a usage-, sentiment-, and outcome-based health model — so CSMs could intervene 30+ days earlier with the right play.
The situation
- Gross retention had slipped from 92% to 86% over four quarters and renewals were being surprised in the final 60 days.
- Health scores were maintained manually by CSMs in a spreadsheet and rarely matched what later happened at renewal.
- Product usage, support sentiment, and outcome data lived in three different systems with no shared definition of 'healthy.'
How we engaged
Diagnose
Interviewed 18 CSMs and 12 renewed/churned customers to map the real leading indicators of churn — not the ones in the existing rubric.
Design
Built a weighted health model across product engagement, outcome attainment, executive engagement, and support sentiment, with explicit thresholds and recommended plays per segment.
Deliver
Implemented the model in the CS platform, instrumented the missing product events, and wired health-triggered tasks into the CSM workflow.
Operate
Established a weekly health-review cadence and a quarterly model-tuning ritual co-owned by CS Ops and Product Analytics.
What we built and ran
- A four-pillar health model (Adoption · Outcomes · Relationship · Sentiment) with segment-specific weights
- An at-risk playbook library mapped to the dominant failure modes per segment
- Health-triggered task automation embedded into the CSM daily workflow
- An executive scorecard with cohort-level health trend, save rate, and forecast retention
What we'd carry forward
- Health scoring fails when it tries to be a single number. Pillars + thresholds drive better behaviour than weighted averages.
- If a CSM can't act on a score by tomorrow morning, the score is the wrong unit of work — design plays, not dashboards.
- Tune the model quarterly. Drift is silent and erodes trust faster than missing the next renewal.
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