pricing-prd

Pricing PRD (Tactical)

Overview

A pricing PRD is the tactical artifact that converts a pricing strategy into a shipped change. Where business-growth/pricing-strategy/ decides "we are moving from per-seat to per-usage and adding an enterprise tier", a pricing PRD decides "here is the page, the experiment, the rollout, and the rollback". Most PMs underinvest in this artifact — they treat pricing as a marketing problem until the rollout breaks revenue.

This skill addresses the underinvestment. It is opinionated about:

• The structure of a pricing PRD (different from a feature PRD).
• The discipline of pricing experiments (small samples + revenue sensitivity == high risk of false-positive readouts).
• Grandfathering and communication (the most-often-skipped sections that cause the most damage).
• A/B testing pricing without confusing customer or accidentally violating consumer-law principles in some jurisdictions.
• Rollback criteria written in advance, not improvised under pressure.

The frameworks behind it: Patrick Campbell's "Pricing as a feature" (ProfitWell / Paddle), Madhavan Ramanujam's Monetizing Innovation, Van Westendorp's Price Sensitivity Meter, Reforge pricing experimentation, Stripe's published pricing-page patterns, and Gabriel Weinberg's Traction.

When to Use

• Launching a new pricing model or tier
• Updating prices on existing packages
• Adding usage-based components to a previously flat-fee product
• Reshaping packaging (moving features between tiers, sunsetting a tier, adding entitlements)
• Running a pricing A/B test
• Localizing prices to a new region or currency
• Splitting self-serve and enterprise pricing surfaces

When not to use

• For the strategic question "should we change pricing at all?" — that's business-growth/pricing-strategy/.
• For the financial-modeling side of a pricing change — that's finance/ skills.
• For SKU/billing implementation details — those live in engineering tickets generated from this PRD.

Pricing PRD vs Feature PRD

A pricing PRD shares some sections with the standard 8-section PRD (create-prd/) but has critical additions:

| Section | Feature PRD | Pricing PRD | |---|---|---| | Summary | Same | Same | | Background | Why now | Plus market context: competitor pricing moves, willingness-to-pay research | | Objective | KRs (engagement, retention) | KRs explicitly named: ARPU, conversion rate, gross margin, churn | | Market segments | JTBD-based | Plus willingness-to-pay band per segment | | Solution | Features | Pricing model + packaging + page design | | Release | T-shirt sizes | Cohort strategy, grandfathering, rollback, holdout | | NEW: Pricing model | n/a | Tier/usage/hybrid decision with rationale | | NEW: Packaging | n/a | What features in what tier, with rationale | | NEW: Grandfathering | n/a | Existing customer treatment | | NEW: Communication plan | n/a | Customer email, in-app, status page, sales enablement | | NEW: A/B design | n/a | Test/control split, primary metric, MDE, holdout | | NEW: Rollback criteria | n/a | Defined thresholds at which the rollout is reversed | | NEW: Regional pricing | n/a | Currency, tax inclusion, PPP, compliance |

Pricing Model Decision

Tier vs Usage vs Hybrid

| Model | When it fits | Risks | |---|---|---| | Tier (Good/Better/Best) | Predictable value per customer, clear packaging boundaries, B2B sales-led motion | Cliffs at tier boundaries; customers downgrade rather than upgrade | | Usage-based | Value scales with usage (API calls, GB, events), self-serve, technical buyer | Revenue unpredictability, billing surprises, customer churn from bill shock | | Hybrid (platform fee + usage) | Multi-product, predictable platform value + variable feature value | Complexity in pricing communication; customers confused about expected cost | | Per-seat | Collaboration product, value scales linearly with users | Discourages adoption (gating new users behind expansion); declining in 2025-2026 | | Outcome-based | Vendor confident in measurable outcome (e.g. revenue lift, leads generated) | Hard to define; risk of customer disputing outcome measurement |

Ramanujam's Monetizing Innovation argues that the pricing model decision should precede the feature design, not follow it: "design the product around the price, not the price around the product". The PRD should state which model was chosen and why other models were rejected.

Common pricing-model anti-patterns

• Per-seat for an AI product: AI value does not scale with seats; per-seat punishes adoption.
• Usage-based without a floor: customers can drop to $0 in a slow month; cash flow becomes volatile.
• Tier with one feature differentiating Good from Better: customers shop around the differentiator; either bundle more value into Better or merge tiers.
• Public enterprise pricing: enterprise pricing should be sales-led; publishing it leaves negotiation room on the table and creates floor effects.

Willingness-to-Pay Research

Three methods, ranked from quickest to most rigorous:

Van Westendorp Price Sensitivity Meter

A four-question survey administered to 50-200 prospects/customers per segment:

1. At what price would you consider this product too expensive to consider buying?
2. At what price would you consider this product too cheap to be of high quality?
3. At what price would you consider this product to be getting expensive but you might still consider buying?
4. At what price would you consider this product to be a bargain?

The intersections of the cumulative response curves yield:

• Range of Acceptable Pricing (RAP): between the "too cheap" and "too expensive" curve intersections.
• Optimal Price Point (OPP): where "too cheap" and "too expensive" cross.
• Indifference Price Point (IPP): where "expensive" and "bargain" cross — the price the median customer perceives as fair.

Limits: stated preference, not revealed; ignores feature dimensions; works best at the price-point level, not the packaging level.

Conjoint analysis

A pairwise-choice survey that asks customers to choose between bundle configurations at different price points. Yields willingness-to-pay per feature.

Stronger than Van Westendorp because it captures the feature-price tradeoff. Weaker for cost-of-survey: typically requires 200-500 respondents per segment and statistical analysis software.

Pricing experiments (revealed preference)

A live A/B test of two price points against new sign-ups. The strongest evidence — revealed willingness to pay — but only available once the team is willing to deploy the change. See "A/B Testing Pricing" below.

Packaging Decisions

Packaging is which features go in which tier. The decisions are:

1. What goes in the entry tier? Enough to deliver the core promise; not so much that customers never upgrade.
2. What is the value carrier in each tier? Each tier should have one or two "headline" features that justify the price.
3. Where are the boundaries? The boundary between tiers should map to a measurable usage characteristic (number of seats, number of integrations, support level), not to a feature toggle that feels arbitrary.
4. What is the trial mechanism? Time-bound (14 days), feature-limited (free tier with caps), reverse-trial (start on Pro, downgrade if usage is low).

Reforge packaging principles (paraphrased)

• Each tier should have a clear customer archetype. Good = solo / SMB; Better = small team / growing; Best = scaling org / enterprise.
• The "value gap" between tiers should be at least 2x. If Better is 1.5x the price of Good, the customer hesitates; if it's 2-3x the price with 3-5x the value, the upgrade is clear.
• Avoid more than 4 tiers. Customers cannot evaluate more than 4-5 options without choice paralysis.

Grandfathering Policy

The most-often-skipped section of a pricing PRD. Existing customers were promised a pric