retention

# 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: 1. **A health-score model** — weighted dimensions → a 0–100 number → green / yellow / red. 2. **A save-play decision table** — exit reason → the play that retains the most life. 3. **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. 1. **Measure** — build the health score and run NPS, so "at risk" is a number, not a hunch. 2. **Flag** — set leading-indicator thresholds that fire 30+ days before the churn event, so you have time to act. 3. **Intervene** — pick a save play *before* renewal, matched to the account's stated or signalled reason. 4. **Recover** — run a win-back sequence on the ones who left anyway. 5. **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. ```text 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). ```text 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. ```text 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-onboarding` problem, 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.