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.

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-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.