For most SaaS, three checkpoints prevent long tail pain:
– Technical fit verified (auth, data in, integrations stable)
– First value achieved (the “aha” the buyer actually cares about)
– Owner named (who runs it day-to-day)
If any checkpoint fails, pause expansion plays and fix root cause. Onboarding is leverage; don’t step over it.
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.
Attractive aggregate metrics can conceal the details we need to make informed decisions. Separate activity from effect and keep the math boring and consistent. Useful, defensible formulas:
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.
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.
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.
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.