
Pricing
A solo builder uses pricing research methods to identify the optimal price point and acceptable price range for their product through structured survey analysis.
Install
npx skills add https://github.com/infrasity-labs/dev-gtm-claude-skills --skill pricingWhat is this skill?
- Van Westendorp Price Sensitivity Meter identifies acceptable price ranges
- MaxDiff Analysis ranks feature/pricing preferences
- Willingness to Pay surveys segment demand by customer persona
Adoption & trust: 1 installs on skills.sh; 24 GitHub stars; trending (+100% hot-view momentum).
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Journey fit
Primary fit
Pricing research belongs in the validate stage because determining willingness to pay and market price sensitivity is critical before finalizing your go-to-market strategy. The pricing subphase specifically focuses on quantifying revenue potential and market acceptance, which these methods directly address through data-driven survey techniques.
SKILL.md
READMESKILL.md - Pricing
# Pricing Research Methods ## Contents - Van Westendorp Price Sensitivity Meter (The Four Questions, How to Analyze, Survey Tips, Sample Output) - MaxDiff Analysis (How It Works, Example Survey Question, Analyzing Results, Using MaxDiff for Packaging) - Willingness to Pay Surveys - Usage-Value Correlation Analysis ## Van Westendorp Price Sensitivity Meter The Van Westendorp survey identifies the acceptable price range for your product. ### The Four Questions Ask each respondent: 1. "At what price would you consider [product] to be so expensive that you would not consider buying it?" (Too expensive) 2. "At what price would you consider [product] to be priced so low that you would question its quality?" (Too cheap) 3. "At what price would you consider [product] to be starting to get expensive, but you still might consider it?" (Expensive/high side) 4. "At what price would you consider [product] to be a bargain—a great buy for the money?" (Cheap/good value) ### How to Analyze 1. Plot cumulative distributions for each question 2. Find the intersections: - **Point of Marginal Cheapness (PMC):** "Too cheap" crosses "Expensive" - **Point of Marginal Expensiveness (PME):** "Too expensive" crosses "Cheap" - **Optimal Price Point (OPP):** "Too cheap" crosses "Too expensive" - **Indifference Price Point (IDP):** "Expensive" crosses "Cheap" **The acceptable price range:** PMC to PME **Optimal pricing zone:** Between OPP and IDP ### Survey Tips - Need 100-300 respondents for reliable data - Segment by persona (different willingness to pay) - Use realistic product descriptions - Consider adding purchase intent questions ### Sample Output ``` Price Sensitivity Analysis Results: ───────────────────────────────── Point of Marginal Cheapness: $29/mo Optimal Price Point: $49/mo Indifference Price Point: $59/mo Point of Marginal Expensiveness: $79/mo Recommended range: $49-59/mo Current price: $39/mo (below optimal) Opportunity: 25-50% price increase without significant demand impact ``` --- ## MaxDiff Analysis (Best-Worst Scaling) MaxDiff identifies which features customers value most, informing packaging decisions. ### How It Works 1. List 8-15 features you could include 2. Show respondents sets of 4-5 features at a time 3. Ask: "Which is MOST important? Which is LEAST important?" 4. Repeat across multiple sets until all features compared 5. Statistical analysis produces importance scores ### Example Survey Question ``` Which feature is MOST important to you? Which feature is LEAST important to you? □ Unlimited projects □ Custom branding □ Priority support □ API access □ Advanced analytics ``` ### Analyzing Results Features are ranked by utility score: - High utility = Must-have (include in base tier) - Medium utility = Differentiator (use for tier separation) - Low utility = Nice-to-have (premium tier or cut) ### Using MaxDiff for Packaging | Utility Score | Packaging Decision | |---------------|-------------------| | Top 20% | Include in all tiers (table stakes) | | 20-50% | Use to differentiate tiers | | 50-80% | Higher tiers only | | Bottom 20% | Consider cutting or premium add-on | --- ## Willingness to Pay Surveys **Direct method (simple but biased):** "How much would you pay for [product]?" **Better: Gabor-Granger method:** "Would you buy [product] at [$X]?" (Yes/No) Vary price across respondents to build demand curve. **Even better: Conjoint analysis:** Show product bundles at different prices Respondents choose preferred option Statistical analysis reveals price sensitivity per feature --- ## Usage-Value Correlation Analysis ### 1. Instrument usage data Track how customers use your product: - Feature usage frequency - Volume metrics (users, records, API calls) - Outcome metrics (revenue generated, time saved) ### 2. Correlate with customer success - Which usage patterns predict retention? - Which usage patterns predict expansion? - Which customers pay the most, and why? ### 3.