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Playbook · Scale

Scale × Pay

Optimize the pricing page and the conversion moment: premium-first order, 2-4 plans with an honest recommendation, pay-or-earn, and a White-Black-White moment with an honest deadline. Every test carries revenue-quality guardrails.

M3 ×Pay3 lead moves2 variants
M3 Scale
Can we scale acquisition, conversion and loops without leaking?
Pay
Is paying the natural next step for an invested user?

M3 × Pay

The playbook

Goal. Paid conversion or revenue per user up, with at least two revenue-quality guardrails inside their margins.

Moves. Premium first, so the rest looks reasonable by contrast INF-T-FN-06 (Influence Ch.1, p.10); real anchors and annual next to monthly with real savings INF-T-FN-04 (Influence Ch.1, p.7); a recommended plan by fit, with an honest downsell INF-T-AU-08 (Influence Ch.6, p.175); add-ons after the main choice, one at a time, unchecked INF-T-FN-07 (Influence Ch.1, p.11); one honest fallback after a decline, never a loop INF-T-RC-16 (Influence Ch.2, p.28); pay now or earn it, with a reachable earn path GT#69 (Actionable Gamification Ch.10, p.250); a White-Black-White conversion moment: mastery first, then an honest deadline or real limit, then a truthful celebration OCT-M16 (Actionable Gamification Ch.14, p.391); the full price shown first INF-T-CC-34 (Influence Ch.3, p.43).

Guardrails. 30-60-day downgrades, refunds, 30-day cancels and the first-seen vs final price gap. Pricing and paywall tests use a stricter significance level.

Lead with these moves

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

    Premium first (expensive-first order)

    top-tier-first price order

    Present the most complete plan or product first, then the smaller options. By contrast, the middle and entry options look more reasonable, and some buyers stay with a higher tier than they would reach by trading up from the bottom.

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

    White, then Black, then White at conversion (OCT-M16)

    honest deadline inside a good experience

    Build a White Hat experience first, add honest urgency or scarcity at the moment of the Desired Action, then return quickly to White Hat with true reassurance that the choice was smart, popular and good. Chou calls it Careful Transitioning; without the urgency, people intend to act and never do.

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

    Anchored Juxtaposition (GT#69)

    pay now or earn it

    Show two routes to the same reward side by side: pay now, or complete Desired Actions that help the system (invite friends, finish a profile, stay active). Many users start on the effort path, tire, and some pay, so the business gains either way; Dropbox's storage offer is Chou's example.

Also in season

Not now

  • Lowball or drip pricingLate fees and lowballs convert now and come back as refunds and chargebacks.
  • Cash bounty per inviteSmall cash rewards can backfire; make the reward more of the product.
  • Decoy plans as a sure winDecoy effects weaken with realistic options; test a decoy, never assume it.

How to measure it

Questions to answer first

  1. Does your pricing page show the premium plan first or last, and have you tested the order?
  2. How many plans do you show, how clearly do they differ, and is one recommended for a reason the buyer can see?
  3. When buyers can add extras, do those appear only once the main purchase is chosen, one by one, not pre-selected, with the total updating as they go?
  4. How often, and by how much, is the final price at checkout higher than the first price a buyer saw?
  5. At the moment of decision, do people who want to buy get an honest reason to act now, or do they keep putting it off?
  6. After a user turns down an offer, do you present one honest, smaller alternative, and only one?
  7. 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?
  8. Do you ever recommend a cheaper plan when it fits the customer better?
  9. How are refunds, chargebacks and cancellations within 30 days trending, especially after any urgency-driven offer?
  10. Looking at your last several test winners, are they leaning more and more on urgency, scarcity or loss framing?

Variant · B2B sales-led

Scale × Pay · Sales-led B2B

Test packaging, expand by seats and team features, and keep credibility high with an admitted minor limitation and an honest downsell. Read net revenue retention, not just new bookings.

Moves. Packaging tests (premium first INF-T-FN-06 (Influence Ch.1, p.10), a recommended plan by fit INF-T-FN-11 (Influence Ch.1, p.4)); expansion by seats and team features; an admitted minor flaw for credibility INF-T-AU-07 (Influence Ch.6, p.174); honest downsells INF-T-AU-08 (Influence Ch.6, p.175).

Lead with these moves

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

    Admit a minor, real limitation

    say what the product is not good for

  • 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.

    Premium first (expensive-first order)

    top-tier-first price order

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.

How to measure it

Questions to answer first

  1. How many plans do you show, how clearly do they differ, and is one recommended for a reason the buyer can see?
  2. Does your pricing page show the premium plan first or last, and have you tested the order?
  3. Do you ever recommend a cheaper plan when it fits the customer better?
  4. Last quarter, how much recurring revenue was new, expansion, contraction and churned, and what is your 12-month net revenue retention?
  5. What proof do buyers ask for at each stage of a deal, and can you show results from companies like theirs?

Variant · E-commerce / DTC

Scale × Pay · E-commerce

With repeat buyers, add subscribe-and-save with skip and pause, collection sets with completion unlocks, and real scarcity only where stock is truly limited. Read average order value with revenue-quality guardrails.

Moves. Subscribe-and-save with easy skip and pause; collection sets with completion unlocks GT#16 (Actionable Gamification Ch.8, p.182); live, real stock and deadlines (never invented) INF-T-SC-01 (Influence Ch.7, p.180) GT#86 (Actionable Gamification Ch.12, p.329); add-ons after the main choice INF-T-FN-07 (Influence Ch.1, p.11); a referral loop for repeat buyers.

Guardrails. Discount dependence, returns, refunds and the first-seen vs final price gap.

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.
  • Countdown timers that resetFake urgency converts once and returns as refunds and distrust.

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. When buyers can add extras, do those appear only once the main purchase is chosen, one by one, not pre-selected, with the total updating as they go?
  3. How often, and by how much, is the final price at checkout higher than the first price a buyer saw?
  4. How are refunds, chargebacks and cancellations within 30 days trending, especially after any urgency-driven offer?
  5. Do referred users refund or churn more, or less, than users who found you on their own?

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

  • Influence · Robert Cialdini

    Ch.1, p.10 · Ch.1, p.7 · Ch.6, p.175 · Ch.1, p.11 · Ch.2, p.28 · Ch.3, p.43 · Ch.6, p.174 · Ch.1, p.4 · Ch.7, p.180

  • Actionable Gamification · Yu-kai Chou

    Ch.10, p.250 · Ch.14, p.391 · Ch.12, p.329 · Ch.15, p.401 · Ch.8, p.182

  • Hooked · Nir Eyal

    Ch.2

Source IDs: INF-T-FN-06INF-T-FN-04INF-T-AU-08INF-T-FN-07INF-T-RC-16GT#69OCT-M16GT#65INF-T-CC-34OCT-M20INF-T-AU-07INF-T-FN-11GT#16HK-TRG25GT#86INF-T-SC-01

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

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