# Pricing Research

> Pricing research skill

- Skill: `matteotitta/pricing-research` (Agent Skill)
- Install (CLI): `npx skillmds@latest add matteotitta/pricing-research`
- Raw SKILL.md: https://api.skillmd.com/api/skills/matteotitta/pricing-research/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- Author: matteotitta (https://skillmd.com/u/matteotitta)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/matteotitta/pricing-research

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# Pricing research skill

Gather evidence-based pricing data through structured methodologies. Produces willingness-to-pay ranges, price sensitivity curves, and competitive pricing intelligence that feed into `/pricing-strategy` for packaging decisions.

## Inputs

| Input | Required | Source |
|-------|----------|--------|
| Product/feature to price | Yes | User specifies what's being priced |
| ICP profile | Recommended | `/icp-research` output |
| Competitor pricing data | Recommended | `/competitor-research` output or manual gathering |
| Current pricing (if exists) | Optional | User provides existing pricing |
| Target sample size | Optional | Default: 30-50 respondents per segment |

## Scope boundary

This skill produces **research data**. It answers "what are people willing to pay?" and "what does the market charge?"

It does NOT answer:
- How to package features into tiers (that's `/pricing-strategy`)
- How to structure a freemium vs. trial model (that's `/pricing-strategy`)
- How to design a pricing page (that's `/landing-page-copy`)
- Whether to do usage-based vs. seat-based (that's `/pricing-strategy` informed by this research)

Think of this as the evidence gathering that makes pricing-strategy decisions defensible instead of gut-feel.

## Methodologies (overview)

Four methodologies are documented in the premium reference. Choose by goal:

| Methodology | Best for | Sample size | Output |
|-------------|----------|-------------|--------|
| **1. Van Westendorp PSM** | Establishing acceptable price range from scratch | 30-50 per segment | PMC / OPP / IDP / PME points + range |
| **2. Gabor-Granger** | Testing a shortlist of candidate prices | 30-50 per segment | Demand curve + revenue-max price |
| **3. Conjoint analysis** | Pricing multi-feature products with modular packaging | 200+ | Per-attribute utility incl. price |
| **4. Competitive intel** | Mapping the market before primary research | Desk research | Competitive pricing matrix |

**Default starting point for B2B SaaS under $10M ARR:** Van Westendorp + Gabor-Granger + competitive intel. That's 80% of the insight at 20% of the cost. Reach for conjoint only when packaging is genuinely complex.

### Van Westendorp — the four questions (most-used pattern)

Present in this exact order:

1. **Too cheap:** "At what price would you consider [product] to be so inexpensive that you'd question its quality?"
2. **Cheap (good value):** "At what price would you consider [product] to be a bargain — a great buy for the money?"
3. **Expensive (getting pricey):** "At what price would you consider [product] to be starting to get expensive — not out of the question, but you'd have to think about it?"
4. **Too expensive:** "At what price would you consider [product] to be so expensive that you'd never consider buying it?"

The four cumulative-distribution intersections produce **PMC** (point of marginal cheapness), **PME** (point of marginal expensiveness), **OPP** (optimal price point), and **IDP** (indifference price point). Acceptable range = PMC→PME. Optimal zone = OPP→IDP. Full curve plotting + sample survey + practical notes: the premium reference.

### When to use Gabor-Granger instead

You already have 3-5 candidate price points; you need a demand curve, not just a range; you want to estimate revenue impact of price changes; you're testing a price increase on existing customers.

### Competitive pricing intel — do this first

Desk research before primary research, so you have context for interpreting WTP data. Public pricing pages + review sites + sales intelligence + indirect signals (ARPU implied from customer count + revenue). Track price points per tier, feature gates, billing options, pricing model, discounting signals.

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## Anti-patterns

**Don't do these:**

- Asking WTP questions without qualifying respondents first. Non-buyers will skew your data low.
- Blending segments in analysis. Enterprise and SMB WTP data mixed together is useless.
- Treating survey data as ground truth. WTP research shows what people SAY they'd pay, not what they'll actually pay. Real prices are typically 10-20% lower than stated WTP.
- Ignoring competitive context. WTP in a vacuum means nothing. Buyers compare.
- Running pricing research once and treating it as permanent. Markets shift. Re-run annually or when entering new segments.
- Skipping value anchoring. If respondents don't understand the value before you ask about price, their answers are noise.
- Using this skill to make packaging decisions. This is research. Packaging is strategy. Use `/pricing-strategy` for that.
- Small sample overconfidence. Under 30 respondents, treat everything as directional, not definitive.

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## Final ship gate

Run `/premortem --output` before ship. See [`/premortem` skill](../../../../meta/orchestration/premortem/SKILL.md) for the 5 execution domains (will-it-resonate / will-it-convert / will-it-stay-on-brand / will-stakeholder-push-back / will-it-degrade-over-time) and output template.

Trivial-case escape: `## Premortem\nNo failure modes — trivial change` satisfies the contract for genuinely trivial outputs.