Pricing and monetization review

Most "pricing page" requests are actually model problems. A prettier page cannot rescue the wrong value metric, three tiers nobody can choose between, or an anchor that makes the real offer look expensive. This playbook works top-down: get the money model right, then make the number feel right, then build the page that presents it, then prove the change lifted revenue and did not just shuffle customers between tiers.

Governing principle: the model sets the ceiling; display, page, and testing only capture what the model makes possible. Fix them in that order.

When to use this

• The user is setting prices for the first time or has not revisited them in a long time.
• Revenue per user is flat, or the mix of plans is not what the business needs.
• A price increase is planned and the fear is churn.
• The pricing page gets traffic but few people pick a plan or start a trial.
• The trial/freemium model itself is in question (too many low-intent free users, or trials that

never convert).

When NOT to use this (reach for instead)

• People never reach the pricing page because an earlier funnel stage leaks: diagnose with

fix-a-leaky-funnel first.

• The page converts poorly but the model is sound and it is really a layout/copy problem:

use the improve-conversion playbook (structure → words → persuasion → proof).

• Free users won't activate regardless of price: that is a value/onboarding problem, route to

product-led-growth.

• Recurring-revenue retention and failed-payment recovery (dunning, win-back, LTV over time):

retention-lifecycle owns keeping the revenue you priced.

The ordered review

Step 1: Model and tiers: fix what you charge

Pull pricing-monetization. Get from it: the value metric (what the price scales with), the tier structure and how many tiers, add-ons, the trial model (free trial vs freemium vs demo), and whether the offer should be subscription, usage-based, or high-ticket. Decide the shape of the money before anything visual. This step sets the ceiling for every step after it.

Step 2: Display psychology: make the number feel right

Pull marketing-psychology. Get from it: anchoring (a high reference tier that makes the target plan look reasonable), decoy tiers, price-display framing (charm pricing, per-day framing, annual framing), how discounts are perceived, and social proof placed at the decision point. This is how the same price converts better or worse. Apply it to the model from Step 1, not to a random price.

Step 3: Pricing page: build the surface that presents it

Pull conversion-cro. Get from it: pricing-page structure, the plan-comparison layout, CTA placement per tier, trust and objection handling at the point of purchase, and the checkout/field surgery that gets a chosen plan across the line. If the page copy needs rewriting, pair with copywriting-messaging for the plan names, feature lines, and CTA micro-copy. This step captures the demand the model and psychology created.

Step 4: Measure impact: prove revenue actually moved

Pull analytics-data. Get from it: the right revenue metric to watch (ARPU, revenue per visitor, not just conversion rate), an experiment or staged rollout that isolates the pricing change, and a causal read so you know the lift is real. Critically, check that a "win" raised total revenue rather than just moving buyers to a cheaper tier or trading conversion rate for price. The validated change becomes the new baseline.

Why this order

If you A/B test a pricing page (Step 3/4) before fixing the model (Step 1), you optimize the presentation of a broken offer and cap your own upside. If you tune display psychology (Step 2) before choosing the value metric (Step 1), you are anchoring against the wrong number. Model first, always.

How to adapt

• High-ticket / sales-assisted offers: weight Step 1 heavily (offer construction, guarantees,

payment terms) and pair Step 3 with sales-outbound or launch-gtm rather than a self-serve checkout page.

• Ecommerce / DTC: add ecommerce for bundle pricing, AOV levers, promo framing, and free-

shipping thresholds; pricing-monetization still owns the base model.

• SaaS raising prices on existing customers: add retention-lifecycle to grandfather, message,

and dun the increase without spiking churn, and use email-marketing for the announcement sequence.

• Freemium-to-paid conversion is the real question: pair Step 1 with product-led-growth so the

free tier is a funnel, not a leak, before you touch the paywall.

• Not enough volume to test a price: skip Step 4's live experiment and use willingness-to-pay

signals and cohort revenue reads from analytics-data; never claim significance on thin data.

From God of Skills: a curated, hand-tested directory of AI skills, prompts, templates and image style guides. Source: https://godofskills.com/skills/pricing-and-monetization-review?ref=claude-skill