How do I build a customer health score that actually predicts churn?
Start with four to six inputs, not twenty. The ones that earn their place are depth of product usage (not logins), breadth across the account, support pattern, and whether an executive still shows up. Weight them, then do the honest test: score your last twelve churned accounts retroactively. If the score was green ninety days before they left, your inputs are wrong and you should rebuild rather than defend it. Segment matters too, since a healthy enterprise account and a healthy SMB account look nothing alike. Treat the score as a prioritisation tool for your week, not as a verdict.
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5 resources, 3 India-specific, 5 link-checked.
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The most complete breakdown of what goes into a health score: which four to six categories to weight, how to segment scores by journey stage, and the failure modes (too many metrics, too much subjective input).
Chargebee's customer success leader answering an Indian founder's question in the SaaSBoomi community. Eight points, starting with the one most Indian startups get wrong: treating CS as an extension of support.
Zoho on the two churn predictors that actually work early (usage depth and NPS) plus a worked cohort example. Written for teams without a data function.
Walks through building the cohort table itself, and separates acquisition cohorts from behavioural cohorts, which is the distinction that turns cohort analysis from a pretty chart into a diagnosis.
A build session, not a definition: which signals to weight, how to avoid a score that just mirrors logins, and how to test whether it predicts anything. From an India based customer success platform, so the examples are mid market rather than enterprise only.
