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Playbook · Mature to Stalled / declining

Mature and Stalled × Return

Protect current-user retention, manage novelty decay with a content pipeline and boosters, and find a new internal trigger. Recover lapsed users with fresh starts instead of more sends.

M4-M5 ×Return3 lead moves1 variant
M4 Mature
Can we deepen and protect the habit?
M5 Stalled / declining
Is this a discovery collapse, an endgame collapse or novelty decay?
Return
What makes the core action repeat until nobody has to prompt it?

M4-M5 × Return

The playbook

Goal. Keep the habit of the users you have; current-user retention moved daily actives more than any other transition in the best public case.

Diagnose. Per-feature engagement by cohort age (novelty decay HK-T11 (Hooked Ch.4)); which user-state transition leaks; whether sends are rising while unprompted use falls.

Moves. A content pipeline that outpaces consumption HK-RWD36 (Hooked Ch.4); user creation as an infinite source of variety HK-RWD35 (Hooked Ch.4); boosters and milestone unlocks GT#31 (Actionable Gamification Ch.7, p.145) GT#19 (Actionable Gamification Ch.7, p.148); a new internal trigger HK-TRG27 (Hooked Ch.2); win-back with an easier plan and fresh-start timing HK-TRG35 (Hooked Ch.7).

Watch. Novelty lifts fade fast: one game's large step-count lift was gone within weeks CASE-M23 (Gotta catch'em all! Pokémon GO and physical activity among young adults: difference in differences study, The BMJ (Howe et al.), 2016).

Lead with these moves

  • TechniqueEvidence grade C. Limited evidence: few or small studies.

    Run a release pipeline that outpaces consumption

    release calendar against fading novelty

    If your variety can be used up, track how engagement fades with cohort age and run a release pipeline faster than users consume it. Check that each release adds a new source of variety, not just a new theme on the old one.

  • TechniqueEvidence grade D. Practitioner framework or anecdote: plausible, untested.

    Boosters (GT#31)

    temporary power-up

    A temporary or conditional power-up that helps users reach a Win-State, then expires and returns them to their normal state. Chou calls Boosters the best kind of reward, because they make the user's own activity more effective instead of sitting on a shelf.

  • TechniqueEvidence grade D. Practitioner framework or anecdote: plausible, untested.

    Milestone Unlock (GT#19)

    capability unlocked at a milestone

    Unlock a new, useful capability right at a natural stopping point, so users want to try it, test it on harder work and notice that the next milestone is close. A strong variant unlocks the tool that solves the user's most recent pain.

Also in season

Not now

  • Content re-skinsA new coat of paint fades faster than the content it covers.
  • More sends to lapsed usersMore sends rent engagement without forming a habit.
  • Urgency and loss pushes on a tired basePressure on a tired base speeds up the exits it is meant to stop.
  • Extra scarcity on an engaged baseScarcity on a base that is already engaged adds pressure, not value, and wears off fast.

How to measure it

Questions to answer first

  1. Where do you lose the most users: new users not becoming regulars, regulars slipping, or slipping users going dormant?
  2. Is the variety finite (content you produce) or infinite (people, user creation)? If finite, does new content arrive faster than users use it up?
  3. Do users come back more often after a reward is shown to them, and is that lift fading in older cohorts?
  4. Over the last two months, have you been sending more messages while sessions nobody prompted went down?
  5. Which nearby feeling or situation could support a second reason to come back?
  6. If active users or revenue are shrinking, is it fewer new users, more long-time users leaving, or new users not coming back?
  7. Of users active yesterday who were also active the week before, what share are active today? And what share of this month's actives came back after 30+ days away?
  8. What happens when a user misses a day or a week? Can they pick up again easily, without feeling scolded?

Cases at this stage

  • Pokemon GO (Niantic) · 2016

    Round

    Pokemon GO: the extra steps faded

    +955 steps a day in the first week after installing, falling over the following weeks and back to pre-install levels by week 6 (Howe et al., BMJ, Dec 13, 2016; about 1,182 US adults aged 18-35).

    M5 ×Return
    Case grade for the number: A. Evidence grade for the mechanism link: A.

  • Peloton · 2026

    Round

    Peloton: loyal members, shrinking top of the funnel

    Paid Connected Fitness subscriptions fell from 2.800M to 2.553M (-8.8%) in fiscal 2026, while average net monthly churn stayed at 1.7% (1.6% in fiscal 2025), per the 10-K for the year ended June 30, 2026.

    M4-M5 ×DiscoverStay
    Case grade for the number: A. Evidence grade for the mechanism link: D.Analyst inferenceCorrelational

Variant · Recall & Trust

Mature and Stalled × Return · Recall & Trust

For an occasional-need product with a large base: honest renewal re-choices, reminders timed to each customer's own cycle, win-back at a fresh start, and reputation that keeps the product first in mind when the need comes back.

Goal. Stay first in mind and one tap away when the need comes back, and make every renewal an honest re-choice.

Diagnose first. Split any decline by phase: fewer new customers, long-time customers not renewing, or recent customers not coming back.

Moves. A renewal re-choice that recaps the value received INF-T-CC-31 (Influence Ch.3, p.79); reminders timed to depletion or season, on the channel the customer picked HK-TRG25 (Hooked Ch.2); gift-like seasonal messages with no ask HK-TRG38 (Hooked Ch.7); the next order or booking scheduled from the customer's own history HK-INV25 (Hooked Ch.5) and a one-tap repeat HK-ACT01 (Hooked Ch.3); win-back timed to a fresh start, with what's new since the last visit F21 (Implementation intentions and goal achievement: a meta-analysis, Gollwitzer & Sheeran, Advances in Experimental Social Psychology, 2006); reviews and referrals right after each value moment HK-MON05 (Hooked Ch.1).

Out. Daily streaks, leaderboards and daily reminder cascades, including lapse sequences built for daily products.

How we'll know. Repeat rate within the natural cycle (primary); resurrection rate and reminder-to-order conversion (diagnostic); notification disables and unsubscribes (guardrails).

Lead with these moves

Also in season

Not now

  • Daily streaks, leaderboards and daily reminder cascadesPeople need this every few weeks, not every day; daily mechanics don't fit.
  • More sends to lapsed usersMore sends rent engagement without forming a habit.
  • Urgency and loss pushes on a tired basePressure on a tired base speeds up the exits it is meant to stop.

How to measure it

Questions to answer first

  1. When does a typical customer need you again (a refill, a rebooking, a renewal), and does anything remind them at that moment?
  2. At renewal, do you remind users what they got from the product and let them choose again, honestly?
  3. Where do you lose the most users: new users not becoming regulars, regulars slipping, or slipping users going dormant?
  4. If active users or revenue are shrinking, is it fewer new users, more long-time users leaving, or new users not coming back?
  5. Do you ask for a review or a referral right after the moment the customer got what they came for?

Cases at this stage

  • Airbnb · 2017

    Round

    Airbnb: reputation beats similarity

    In an experiment with 8,906 Airbnb users, high reputation significantly increased trust between dissimilar users, offsetting the pull toward similar others; analyses of about 1M real Airbnb interactions agreed (Abrahao et al., PNAS, Sept 2017).

    M3 ×DiscoverStay
    Case grade for the number: A. Evidence grade for the mechanism link: A.

Limits

What we don’t claim

  • The Hook Model is a practical design tool, not a validated or complete theory of behavior.Hooked, Introduction
  • Octalysis is a practitioner framework built from observing games, not a validated scientific model.Actionable Gamification Ch.15, pp.399-400
  • Habit metrics need the longer habit horizon (weeks to months) before they can be read.
  • Seasons are defaults for the cell. Evidence from your own product can move a technique in or out of season.
  • A playbook names candidate moves; which one goes first depends on the binding constraint in your product.

Sources

  • Hooked · Nir Eyal

    Ch.4 · Ch.2 · Ch.1 · Ch.7 · Ch.5 · Ch.3

  • Actionable Gamification · Yu-kai Chou

    Ch.7, p.145 · Ch.7, p.148

  • Influence · Robert Cialdini

    Ch.3, p.79

  • Meaningful metrics: the Duolingo growth model · Duolingo Blog (Erin Gustafson), 2023

    Benchmark confidence A. A primary source for the benchmark.

  • Gotta catch'em all! Pokémon GO and physical activity among young adults: difference in differences study · The BMJ (Howe et al.), 2016

    Case grade A. From a regulator, a public filing or a peer-reviewed study.Evidence grade A. Robust evidence: replicated, peer-reviewed research.

  • Implementation intentions and goal achievement: a meta-analysis · Gollwitzer & Sheeran, Advances in Experimental Social Psychology, 2006

    Evidence grade B. Moderate evidence: solid studies with some limits.

Source IDs: HK-T11HK-RWD36HK-RWD35GT#31GT#19HK-TRG27CASE-M23INF-T-CC-31HK-TRG25HK-TRG38HK-INV25HK-ACT01HK-MON05HK-MON07F21

Book content is paraphrased, never quoted at length. Last verified October 24, 2026.

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