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:

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).

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.