The Smallest Possible Health Model
Three inputs beat twelve: activation state, usage recency/depth, and key feature adoption. Start with equal weights. If AUC/ROC against churn is ~0.5, your inputs are wrong, not your math. Fix the signals first.
Three inputs beat twelve: activation state, usage recency/depth, and key feature adoption. Start with equal weights. If AUC/ROC against churn is ~0.5, your inputs are wrong, not your math. Fix the signals first.
Expansion works when eligibility, timing, and value narrative are explicit. Eligibility = adoption threshold + business context. Timing = observed usage plateau or unlocked capability. Narrative = “outcome next” not “more features.” Track win rate and payback against this criteria or change it.
Involuntary churn is failure to collect (payment issues, expired cards). Voluntary churn is a decision (no value, no budget, switching). The fixes differ. Involuntary churn is mostly ops and billing hygiene. Voluntary churn is product–market fit, onboarding, and value communications. Separate the streams in your reporting or you’ll chase the wrong problems.
Support resolves issues; Digital CS drives behavior change at scale. Think three loops: 1) Teach the next action (contextual nudges beat blasts) 2) Detect risk early (silence and strange patterns both matter) 3) Reward progress (show momentum) If ops, data, and CS are aligned, these loops reduce human workload while improving outcomes.
Playbooks without outcomes turn into activity reports. Start with 2–3 customer outcomes you can measure (time-to-first-value, usage depth, key feature adoption). Then write plays that move those, and instrument the deltas. If a play can’t be tied to a metric next week, it’s not ready.
Health scores work when they are legible and predictive. Keep the inputs few, stable, and behavior-based (e.g., activation milestones met, usage depth/recency, key feature adoption). Write the attribution rules down so you can explain changes. If you can’t predict churn or expansion better than chance, your model is a vanity metric—fix the inputs before tuning…
Customer Success is a company-wide strategy to maximize customer outcomes and, as a result, durable revenue (retention and expansion). In practice it integrates product knowledge, domain expertise, and relationship management into repeatable programs. The simplest test: do customers achieve the outcomes they hired the product for, and can we demonstrate that with data over time?