Keep risk reviews to three questions: what moved this account’s health since last review, what’s the next intervention, and what evidence says it will work. Ban status theater. If we can’t state the risk and the counterfactual clearly, log an assumption and test it.
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?
Churn is a lagging indicator. By the time a customer cancels, the problem started months earlier, usually as low engagement or stalled adoption. If you’re reacting to churn risk at renewal time, you’re already behind. The simplest formula for keeping customers I have been able to validate is below: Retention = Experience + Outcomes(Adoption(Engagement)) Work…
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.
QBRs drift into slide parades. Run “value reviews” instead: restate the customer’s outcomes, show what moved, and agree on the next two experiments. Skip vanity metrics. End with a one‑page summary the exec can forward without translation.
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…
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.