pricing-prd

What this skill does

# 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