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Playbook · Early fit

Early fit × Pay

Charge for depth, not for the core loop: depth-gated freemium or a reverse trial, the paywall after the first major win, and the trial end announced up front. Judge on paid per visitor with revenue-quality guardrails.

M2 ×Pay3 lead moves2 variants
M2 Early fit
How do we put every new user on the path that retained users took?
Pay
Is paying the natural next step for an invested user?

M2 × Pay

The playbook

Goal. Turn early devoted use into revenue without taxing the habit loop.

Moves. Depth-gated freemium: premium features that matter only to devoted users HK-MON02 (Hooked Ch.1); a reverse trial with a "keep what you used" check-in INF-T-RC-05 (Influence Ch.2, p.20); the trial end announced on day one INF-T-SC-13 (Influence Ch.7, p.193); users build real value during the trial, so converting means keeping their own work GT#46 (Actionable Gamification Ch.12, p.326); boosters as the premium unit GT#31 (Actionable Gamification Ch.7, p.145); the paywall after the first major win.

How we'll know. Trial-to-paid or paid per visitor (primary); conversion by tenure (diagnostic); at least two revenue-quality guardrails (refunds, 30-day cancels).

Lead with these moves

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

    Charge for depth, never for the core loop

    depth-gated freemium

    Keep the habit-forming core free and charge for what devoted users want more of: storage, offline use, history, collaboration, advanced controls.

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

    Reverse trial with a keep-what-you-used check-in

    full trial, then choose what to keep

    Start new users on the full product for a fixed period, then hold a scheduled check-in that shows what they used ("you used 3 of the 5 paid features") and lets them keep it or step down to free without losing their data.

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

    Trial end announced up front

    trial end date stated on day one

    Tell trial users on day one exactly what goes away and when ("All paid features until May 31, then pick the plan you want"), show the date in the product, and remind them once before it ends with a one-click way to keep the plan. A pre-announced end turns the loss of access into a decision; a surprise end turns it into resentment.

Also in season

Not now

  • Annual-plan discountsLocking people in before the habit forms just delays the churn.
  • Limited-time discounts to convertDiscounts pull in buyers who leave when the discount ends.
  • Hard paywall before the habitA paywall before the habit converts some and loses the rest.
  • Honest-deadline conversion momentAn honest deadline helps only after people have had their win.
  • Lowball or drip pricingLate fees and lowballs convert now and come back as refunds and chargebacks.

How to measure it

Questions to answer first

  1. Does the paywall show up before or after a user's first big win, and does it block the core action or only extra depth?
  2. Which features matter only to your most devoted users, and are those what you charge for?
  3. Do you ask for money before or after people use the product regularly? What share of each cohort pays at month 1, 3, 6 and 12?
  4. If you run a trial or a reverse trial, do users know from day one what they'll lose and on which date?
  5. Did you choose your trial length, card requirement and paywall by paying customers per visitor, or by conversion rate alone?
  6. Walk the product as someone determined not to pay. Where do you feel pushed to pay, and is it for more power or for relief from friction the product created?
  7. Are trials, subscriptions, upgrades, cancels (with reasons), refunds, orders and the prices shown logged per user?
  8. How are refunds, chargebacks and cancellations within 30 days trending, especially after any urgency-driven offer?

Variant · B2B sales-led

Early fit × Pay · Sales-led B2B

For sales-led products, buyer decision points are in season from Early fit: proof from similar logos, a mutual action plan with a clear endpoint, and an honest downsell when a smaller plan fits.

Moves. Proof from similar logos at each deal stage INF-T-SP-17 (Influence Ch.4, p.107); a mutual action plan that shows the endpoint from the first step INF-T-CC-34 (Influence Ch.3, p.43) and starts with a small, easy commitment INF-T-CC-05 (Influence Ch.3, p.53); an honest downsell when a smaller plan fits INF-T-AU-08 (Influence Ch.6, p.175).

Guardrails. Early churn of new accounts and discounting depth.

Lead with these moves

Also in season

Not now

  • Limited-time discounts to convertDiscounts pull in buyers who leave when the discount ends.
  • Lowball or drip pricingLate fees and lowballs convert now and come back as refunds and chargebacks.

How to measure it

Questions to answer first

  1. What proof do buyers ask for at each stage of a deal, and can you show results from companies like theirs?
  2. Do you ever recommend a cheaper plan when it fits the customer better?
  3. What worries stop visitors from trying (setup effort, cost, data, getting locked in), and what would remove each one?
  4. Is your first ask one nearly everyone says yes to, and does every following step give something back?

Variant · E-commerce / DTC

Early fit × Pay · E-commerce

Turn first buyers into second-time buyers: reorder reminders timed to depletion, a one-tap repeat order, and proof and pricing that stay honest at checkout.

Moves. Reorder reminders timed to when the product runs out HK-TRG25 (Hooked Ch.2), scheduled from the customer's own order history HK-INV25 (Hooked Ch.5); a one-tap repeat order HK-ACT01 (Hooked Ch.3); a review or referral ask right after delivery HK-MON05 (Hooked Ch.1).

Checkout stays honest. Because the purchase is the value event, checkout levers are in season: proof from similar buyers at the moment of doubt INF-T-SP-12 (Influence Ch.4, p.98); the all-in price first INF-T-CC-34 (Influence Ch.3, p.43); live, true stock and deadlines that really end INF-T-SC-01 (Influence Ch.7, p.180) INF-T-SC-03 (Influence Ch.7, p.181); one unchecked add-on after the main choice, with a running total INF-T-FN-07 (Influence Ch.1, p.11); a lower-risk first try (guarantees, free returns) HK-ACT07 (Hooked Ch.3).

How we'll know. Repeat rate within the natural cycle (primary); reorder-reminder conversion (diagnostic); discount dependence and refunds (guardrails).

Lead with these moves

Also in season

Not now

  • Lowball or drip pricingLate fees and lowballs convert now and come back as refunds and chargebacks.
  • Limited-time discounts to convertDiscounts pull in buyers who leave when the discount ends.
  • Loyalty tiers without devaluationTiers work once a large repeat base exists.

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 checkout, what reassures a first-time buyer: reviews from similar buyers, a guarantee, the all-in price, a delivery date?
  3. How often, and by how much, is the final price at checkout higher than the first price a buyer saw?
  4. Do you ask for a review or a referral right after the moment the customer got what they came for?
  5. Thinking of typical loyal users, not the most extreme ones, how often should they do the value event: several times a day, daily, weekly, monthly, or only when a need comes up?

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.2 · Ch.5 · Ch.3

  • Influence · Robert Cialdini

    Ch.2, p.20 · Ch.7, p.193 · Ch.4, p.107 · Ch.3, p.43 · Ch.6, p.175 · Ch.3, p.53 · Ch.4, p.98 · Ch.7, p.180 · Ch.7, p.181 · Ch.1, p.11

  • Actionable Gamification · Yu-kai Chou

    Ch.12, p.326 · Ch.7, p.145

Source IDs: HK-MON02HK-MON01INF-T-RC-05INF-T-SC-13GT#46GT#31INF-T-SP-17INF-T-CC-34INF-T-AU-08INF-T-CC-05HK-TRG25HK-INV25HK-ACT01HK-MON05INF-T-SP-12INF-T-SC-01INF-T-SC-03INF-T-FN-07HK-ACT07

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

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