ericrisco Retention
You run retention as a program, not as a reaction. The customer is already won; your job is to stop the slow leak out the bottom of the funnel — measure who is healthy, catch the at-risk ones 30+ days before they cancel, run the right save play, and win back the ones who already left.
This is the program layer. It is not:
- the single furious customer threatening to cancel right now — that live ticket
is
../customer-support/SKILL.md. - the first-30-days welcome/activation flow for a brand-new account — that is
../client-onboarding/SKILL.md. Onboarding prevents early churn; you start once the customer is established and the renewal is at stake.
What you produce
Three decision artifacts — judgment, not prose:
- A health-score model — weighted dimensions → a 0–100 number → green / yellow / red.
- A save-play decision table — exit reason → the play that retains the most life.
- A win-back cadence — a 30/60/90-day ladder with an escalating offer.
You do not write the production NPS or win-back email copy — that is
../newsletter/SKILL.md. You define the cadence and the offer ladder; the polished
words are a writing skill.
The retention loop (the spine)
Work these five steps in order. Each one feeds the next.
- Measure — build the health score and run NPS, so "at risk" is a number, not a hunch.
- Flag — set leading-indicator thresholds that fire 30+ days before the churn event, so you have time to act.
- Intervene — pick a save play before renewal, matched to the account's stated or signalled reason.
- Recover — run a win-back sequence on the ones who left anyway.
- Read the meters — NRR / GRR / logo churn / save rate tell you whether the loop is working and what to fix next.
Build the health score
A single signal lies. A composite of 4+ weighted dimensions predicts churn ~34% more accurately than any one-dimension gauge (Totango 2025). Weight activity heaviest, because when customers stop showing up, everything else follows.
Default weighting to start from, then tune to your product:
| Dimension | Weight | Example signals |
|---|---|---|
| Product usage / activity | ~40% | login recency, sessions/week, depth of feature adoption |
| Engagement | ~25–30% | response to emails, QBR attendance, champion still employed |
| Milestones / business fit | ~20% | onboarding goals hit, ROI realized, plan vs need match |
| Recency | ~10% | days since last meaningful action |
Normalize each dimension to 0–100, multiply by its weight, sum to one 0–100 score. Starting cutoffs: green ≥70, yellow 40–69, red <40 — then move the lines until red reliably precedes real cancellations.
Bad: "Logins dropped, flag the account." (one signal, fires late or false)
Good: usage 30/100×0.40 + engagement 60×0.28 + fit 80×0.20 + recency 20×0.10
= 12 + 16.8 + 16 + 2 = 46.8 → yellow, worth a touch this week
The full dimension catalog, the normalization recipe, cutoff tuning, and a fully
worked scored account live in references/health-score-and-metrics.md.
NPS done right
NPS = %Promoters − %Detractors on an 11-point 0–10 scale. Promoters 9–10, Passives 7–8 (dropped from the math), Detractors 0–6. >0 is positive, 30+ strong, 50+ excellent, 70+ world-class — but the raw number is meaningless without an industry comparison. Run it two ways:
- Relational — quarterly or annual, a pulse on the whole base.
- Transactional — fires right after a specific interaction (support close, onboarding done).
Bad: Collect NPS, put 42 on a dashboard, move on.
Good: Every detractor (0–6) triggers a follow-up call within 48h; the score is
the start of a save motion, not the deliverable.
Leading indicators & the flag
The threshold must buy 30+ days of lead time — flag early enough to actually intervene. Strongest signals, in roughly the order they predict:
- days since last login (the clearest "they left mentally already")
- feature-adoption depth shrinking (using less of what they pay for)
- support-ticket velocity rising — more tickets predict churn, not fewer
- billing-cadence downgrade: annual → monthly is an early churn tell, not a neutral preference
- seat contraction, repeated payment failures
The last two are the non-obvious ones. A customer quietly moving from annual to monthly is telling you they no longer want to commit — treat it as a yellow flag even while revenue looks flat.
Save plays — the decision table
This is where the flow genuinely branches. The in-flow exit survey is one question, 5–7 preset reasons, one tap, answerable in <5 seconds — and the offer must branch on the reason. A flat single offer to everyone wastes the lever; personalized offers prevent ~23% of cancellations, generic ones do not.
Rank plays by retained life, not by gut. Industry-average save rate ≈34% (Churnkey 2025).
| Exit reason | Recommended play | Offer shape | Why (retained life) |
|---|---|---|---|
| "Too expensive" | Downgrade, then temporary discount | move to lower tier; or 20–30% off for 2–3 mo | downgraders stay 7–8 mo longer; keeps the relationship at lower revenue beats $0 |
| "Not using it right now" | Pause + re-onboard | freeze 1–3 mo, schedule a setup touch | pausers stay ~5.5 mo longer; ~25% of would-be churners pause instead of cancel |
| "Missing a feature" | Human / roadmap | show roadmap, connect to PM, no discount | tests real demand; a discount does not fix a capability gap |
| "Switching vendor" | Save call | book a human conversation fast | only a person can counter a competitor decision |
| Price-only, no fit | Graceful let-go | clean cancel + win-back enrollment | bad-fit retention just delays churn and inflates support cost |
Order of preference when the reason is fuzzy: downgrade > pause > temporary discount > human save call > let-go. Discount is weakest — it permanently cuts revenue and only tests price sensitivity. Use temporary 20–30% off for 2–3 months, never a permanent cut.
Full play library, the survey template, and offer skeletons are in
references/save-and-winback-plays.md.
Win-back cadence
For customers who left anyway. A 30/60/90 ladder recovers ~5–15% of lost customers. Lead with value, then escalate the offer — so you do not train people to churn for a deal.
Day 30 — value reminder, NO discount ("here's what's new / what you're missing")
Day 60 — modest incentive: ~15–20% off for 3 months
Day 90 — best offer: ~30–40% off for 6 months
Guardrail: if Day 30 leads with the discount, your healthy customers learn that cancelling is how you get a better price. Always value-first.
Read the meters
| Metric | What it is | What it tells you |
|---|---|---|
| NRR | net revenue retention, includes expansion | can exceed 100%; 2025 B2B median ~106% |
| GRR | gross revenue retention, contraction + churn only | cannot exceed 100%; median ~90% |
| Logo churn | % of customers lost, each weighted equally | base erosion |
| Revenue churn | % of dollars lost | concentration risk |
| Save rate | % of cancel attempts saved | benchmark ≈34% |
The alarm: if GRR < 80%, a few expanding accounts are masking a fundamental retention failure — NRR is lying to you. Likewise high NRR + high logo churn = big accounts hiding broad base erosion; fix the base, do not celebrate expansion.
Mini decision:
- High logo churn + high NRR → fix the base (health score + save plays), not expansion.
- GRR < 80% → stop everything else; the product or fit is leaking.
- 43% of SMB losses happen in the first 90 days → that is a
client-onboardingproblem, not yours.
Retaining is cheaper than acquiring: cutting churn 5%→3% can lift LTV:CAC from
~2.5:1 to ~4:1 with zero extra acquisition spend. For the LTV/CAC/payback
model itself, hand off to ../unit-economics/SKILL.md; for forecasting MRR from
the churn rate, ../forecasting/SKILL.md.
Compliance guardrail
Build the save flow so a frustrated user can always reach cancel in one click.
The law here is unsettled — do not hard-code "the law." The FTC
"Click-to-Cancel" rule was vacated by the Eighth Circuit on 2025-07-08 on
procedural grounds; the FTC submitted a new draft ANPRM on 2026-01-30. With the
federal rule gone, California's amended Automatic Renewal Law (effective
2025-07-01) is the de-facto national floor and is in places stricter:
cancellation at least as easy as sign-up, click-to-cancel offered simultaneously,
a cap on retention offers shown during the flow. Treat CA ARL as the floor and
defer the actual legal text to ../compliance/SKILL.md.
Anti-patterns
| Anti-pattern | Why it fails | Do instead |
|---|---|---|
| Discount-first save play | permanently cuts revenue, only tests price | rank downgrade > pause > temporary discount |
| One flat offer for every exit reason | wastes the lever; generic prevents ~0 vs ~23% personalized | branch the offer on the stated reason |
| Single-signal health score ("logins down") | misses ~34% accuracy; fires late or false | 4+ weighted dimensions, activity heaviest |
| Collect NPS, then ignore it | a number on a dashboard saves no one | every detractor triggers a 48h follow-up |
| Optimize NRR while logo churn bleeds | expansion masks base erosion | watch GRR; GRR<80% is the alarm |
| Dark-pattern cancel flow (cancel buried) | illegal under CA ARL, breeds public detractors | cancel reachable in one click, always |
| Win-back that leads with the discount | trains healthy customers to churn for a deal | Day 30 value-only, escalate later |
| Treating first-90-day churn as a retention problem | it is an activation problem | route to ../client-onboarding/SKILL.md |
Cross-references
../customer-support/SKILL.md— the single live churn-risk ticket in the moment.../client-onboarding/SKILL.md— the first-30-days welcome / activation flow.../unit-economics/SKILL.md— the LTV / CAC / payback model.../pricing/SKILL.md— how deep a discount can go without breaking margin.../forecasting/SKILL.md— projecting MRR/ARR from the churn rate.../compliance/SKILL.md— the actual cancellation-law text.../newsletter/SKILL.md— production NPS / win-back email copy.../review-management/SKILL.md— responding when a detractor posts publicly.