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Playbook · Mature to Stalled / declining

Mature and Stalled × Pay

Grow revenue from the base you have: expansion by capability, caps near the natural bound for power users, price rises tied to added value with grandfathering, and save flows that reward loyalty. At Stalled, re-package and retire unused tiers.

M4-M5 ×Pay3 lead moves2 variants
M4 Mature
Can we deepen and protect the habit?
M5 Stalled / declining
Is this a discovery collapse, an endgame collapse or novelty decay?
Pay
Is paying the natural next step for an invested user?

M4-M5 × Pay

The playbook

Goal. Expansion and net revenue retention, protected by a habit that lowers price sensitivity.

Moves. Expansion by capability and seats; collection sets with completion unlocks GT#16 (Actionable Gamification Ch.8, p.182); usage caps near the natural bound with rising caps for power users GT#68 (Actionable Gamification Ch.10, p.253) (evidence warning: the supporting cap studies come from a heavily discredited lab, grade B/C; test locally); price rises tied to added value HK-MON03 (Hooked Ch.1) with existing users grandfathered, because taking away a freedom people had triggers reactance INF-T-SC-19 (Influence Ch.7, p.184); an honest re-choice at renewal INF-T-CC-31 (Influence Ch.3, p.79); pause instead of cancel, and loyal users getting offers at least as good as cancel offers.

At Stalled. Re-package around a motivation the category doesn't deliver OCT-M08 (Actionable Gamification Ch.16, p.444); retire unused tiers HK-ACT43 (Hooked Ch.8); fresh-start win-back.

Lead with these moves

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

    Raise prices with added value and grandfathering

    value-tied price rise

    Habitual users are less price-sensitive, which leaves room to raise prices. Tie each increase to visible added value, protect loyal users with grandfathering or long notice, and test elasticity by engagement segment first.

  • TechniqueEvidence grade B. Moderate evidence: solid studies with some limits.

    Grandfather when you take something back

    notice, choice and grandfathering on removals

    When you raise prices, shrink a free tier or remove a feature, protect existing users: grandfather current terms for a period, give long notice, offer a choice (keep the old version for 90 days, opt in early) and tie the change to a clear gain. Taking back a freedom people already have triggers far more resistance than never granting it.

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

    Magnetic Caps (GT#68)

    calibrated usage limit

    A limit on how often a Desired Action can be taken, which makes the action more appealing and pulls people toward the limit. Chou's calibration rule is to cap at or just above the natural upper bound, for example the 90th percentile, and to raise caps for proven power users.

Also in season

Not now

  • Limited-time discounts to convertDiscounts pull in buyers who leave when the discount ends.
  • Extra scarcity on an engaged baseScarcity on a base that is already engaged adds pressure, not value, and wears off fast.
  • Lowball or drip pricingLate fees and lowballs convert now and come back as refunds and chargebacks.
  • 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. Last quarter, how much recurring revenue was new, expansion, contraction and churned, and what is your 12-month net revenue retention?
  2. At renewal, do you remind users what they got from the product and let them choose again, honestly?
  3. After a user turns down an offer, do you present one honest, smaller alternative, and only one?
  4. Do loyal users get offers at least as good as the ones you give people who try to cancel?
  5. Do you ever recommend a cheaper plan when it fits the customer better?
  6. How are refunds, chargebacks and cancellations within 30 days trending, especially after any urgency-driven offer?
  7. Which features matter only to your most devoted users, and are those what you charge for?

Variant · B2B sales-led

Mature and Stalled × Pay · Sales-led B2B

Protect and grow the installed base: an honest re-choice at renewal that recaps the value delivered, a save desk with one honest fallback, and expansion tied to capability.

Moves. A renewal re-choice that recaps the value delivered INF-T-CC-31 (Influence Ch.3, p.79); a save desk with one honest fallback, never a loop of offers INF-T-RC-16 (Influence Ch.2, p.28); an honest downsell instead of a lost account INF-T-AU-08 (Influence Ch.6, p.175); value-tied price changes with existing customers grandfathered INF-T-SC-19 (Influence Ch.7, p.184).

Lead with these moves

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

    Re-choice moment at renewal

    renewal summary with an easy change

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

    Honest downsell

    recommend the cheaper plan that fits

  • TechniqueEvidence grade B. Moderate evidence: solid studies with some limits.

    Rejection-then-retreat

    larger real offer, then one smaller one

Also in season

Not now

  • Extra scarcity on an engaged baseScarcity on a base that is already engaged adds pressure, not value, and wears off fast.
  • Lowball or drip pricingLate fees and lowballs convert now and come back as refunds and chargebacks.
  • 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. At renewal, do you remind users what they got from the product and let them choose again, honestly?
  2. After a user turns down an offer, do you present one honest, smaller alternative, and only one?
  3. Last quarter, how much recurring revenue was new, expansion, contraction and churned, and what is your 12-month net revenue retention?
  4. Do loyal users get offers at least as good as the ones you give people who try to cancel?
  5. If active users or revenue are shrinking, is it fewer new users, more long-time users leaving, or new users not coming back?

Variant · E-commerce / DTC

Mature and Stalled × Pay · E-commerce

For a large repeat base: loyalty tiers that don't devalue, reorder and seasonal reminders timed to each customer's cycle, win-back at a fresh start, and an assortment refresh that keeps variety ahead of consumption.

Moves. Loyalty tiers without devaluation CASE-M12 (Starbucks Card, Loyalty & Mobile Dashboard (Q4 FY25), Starbucks investor relations, 2025); an assortment refresh as the store's content pipeline HK-RWD36 (Hooked Ch.4); reorder and seasonal reminders timed to each customer's own cycle HK-TRG25 (Hooked Ch.2) HK-TRG38 (Hooked Ch.7); win-back timed to a fresh start, with what's new since the last order F21 (Implementation intentions and goal achievement: a meta-analysis, Gollwitzer & Sheeran, Advances in Experimental Social Psychology, 2006); loyal customers get offers at least as good as lapsing ones. Split any decline by phase before discounting.

Out. Daily reminder cascades: most stores sit on the Recall & Trust route, where purchases are too far apart for daily mechanics.

Guardrails. Discount dependence and refunds.

Lead with these moves

Also in season

Not now

  • Limited-time discounts to convertDiscounts pull in buyers who leave when the discount ends.
  • Extra scarcity on an engaged baseScarcity on a base that is already engaged adds pressure, not value, and wears off fast.
  • 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. Do loyal users get offers at least as good as the ones you give people who try to cancel?
  2. When does a typical customer need you again (a refill, a rebooking, a renewal), and does anything remind them at that moment?
  3. If active users or revenue are shrinking, is it fewer new users, more long-time users leaving, or new users not coming back?
  4. How are refunds, chargebacks and cancellations within 30 days trending, especially after any urgency-driven offer?

Cases at this stage

  • Starbucks · 2025

    Round

    Starbucks Rewards: stars, tiers and devaluation risk

    34.2M US 90-day active members, and member spend at 58% of US company-operated tender (data as of Sept 28, 2025; 60% a year earlier). The three-tier relaunch on March 10, 2026 drew mostly negative reaction from casual members (press).

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

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
  • 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.1 · Ch.8 · Ch.4 · Ch.2 · Ch.7

  • Influence · Robert Cialdini

    Ch.7, p.193 · Ch.7, p.184 · Ch.3, p.79 · Ch.6, p.175 · Ch.2, p.28

  • Actionable Gamification · Yu-kai Chou

    Ch.10, p.253 · Ch.8, p.182 · Ch.16, p.444

  • 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.

  • Starbucks Card, Loyalty & Mobile Dashboard (Q4 FY25) · Starbucks investor relations, 2025

    Case grade B. Company-reported numbers.

Source IDs: HK-MON03INF-T-SC-19GT#68GT#16INF-T-CC-31OCT-M08HK-ACT43INF-T-AU-08INF-T-RC-16HK-RWD36HK-TRG25HK-TRG38F21CASE-M12

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

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