c29ee7e6db
- Fix marketplace.json: add 2 missing skills (content-strategy, product-marketing-context) - Refactor 10 skills over 500 lines to use references/ folders: - email-sequence: 926 → 291 lines - social-content: 809 → 276 lines - competitor-alternatives: 750 → 253 lines - pricing-strategy: 712 → 226 lines - programmatic-seo: 628 → 235 lines - referral-program: 604 → 239 lines - schema-markup: 598 → 175 lines - free-tool-strategy: 576 → 176 lines - paywall-upgrade-cro: 572 → 224 lines - marketing-ideas: 566 → 165 lines Each skill now has core workflow in SKILL.md (<500 lines) with detailed content in references/ folder for progressive disclosure. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
147 lines
4.4 KiB
Markdown
147 lines
4.4 KiB
Markdown
# Pricing Research Methods
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## Van Westendorp Price Sensitivity Meter
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The Van Westendorp survey identifies the acceptable price range for your product.
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### The Four Questions
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Ask each respondent:
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1. "At what price would you consider [product] to be so expensive that you would not consider buying it?" (Too expensive)
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2. "At what price would you consider [product] to be priced so low that you would question its quality?" (Too cheap)
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3. "At what price would you consider [product] to be starting to get expensive, but you still might consider it?" (Expensive/high side)
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4. "At what price would you consider [product] to be a bargain—a great buy for the money?" (Cheap/good value)
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### How to Analyze
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1. Plot cumulative distributions for each question
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2. Find the intersections:
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- **Point of Marginal Cheapness (PMC):** "Too cheap" crosses "Expensive"
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- **Point of Marginal Expensiveness (PME):** "Too expensive" crosses "Cheap"
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- **Optimal Price Point (OPP):** "Too cheap" crosses "Too expensive"
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- **Indifference Price Point (IDP):** "Expensive" crosses "Cheap"
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**The acceptable price range:** PMC to PME
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**Optimal pricing zone:** Between OPP and IDP
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### Survey Tips
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- Need 100-300 respondents for reliable data
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- Segment by persona (different willingness to pay)
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- Use realistic product descriptions
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- Consider adding purchase intent questions
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### Sample Output
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```
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Price Sensitivity Analysis Results:
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─────────────────────────────────
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Point of Marginal Cheapness: $29/mo
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Optimal Price Point: $49/mo
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Indifference Price Point: $59/mo
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Point of Marginal Expensiveness: $79/mo
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Recommended range: $49-59/mo
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Current price: $39/mo (below optimal)
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Opportunity: 25-50% price increase without significant demand impact
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```
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---
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## MaxDiff Analysis (Best-Worst Scaling)
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MaxDiff identifies which features customers value most, informing packaging decisions.
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### How It Works
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1. List 8-15 features you could include
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2. Show respondents sets of 4-5 features at a time
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3. Ask: "Which is MOST important? Which is LEAST important?"
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4. Repeat across multiple sets until all features compared
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5. Statistical analysis produces importance scores
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### Example Survey Question
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```
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Which feature is MOST important to you?
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Which feature is LEAST important to you?
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□ Unlimited projects
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□ Custom branding
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□ Priority support
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□ API access
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□ Advanced analytics
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```
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### Analyzing Results
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Features are ranked by utility score:
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- High utility = Must-have (include in base tier)
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- Medium utility = Differentiator (use for tier separation)
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- Low utility = Nice-to-have (premium tier or cut)
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### Using MaxDiff for Packaging
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| Utility Score | Packaging Decision |
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|---------------|-------------------|
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| Top 20% | Include in all tiers (table stakes) |
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| 20-50% | Use to differentiate tiers |
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| 50-80% | Higher tiers only |
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| Bottom 20% | Consider cutting or premium add-on |
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---
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## Willingness to Pay Surveys
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**Direct method (simple but biased):**
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"How much would you pay for [product]?"
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**Better: Gabor-Granger method:**
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"Would you buy [product] at [$X]?" (Yes/No)
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Vary price across respondents to build demand curve.
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**Even better: Conjoint analysis:**
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Show product bundles at different prices
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Respondents choose preferred option
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Statistical analysis reveals price sensitivity per feature
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---
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## Usage-Value Correlation Analysis
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### 1. Instrument usage data
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Track how customers use your product:
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- Feature usage frequency
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- Volume metrics (users, records, API calls)
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- Outcome metrics (revenue generated, time saved)
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### 2. Correlate with customer success
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- Which usage patterns predict retention?
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- Which usage patterns predict expansion?
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- Which customers pay the most, and why?
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### 3. Identify value thresholds
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- At what usage level do customers "get it"?
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- At what usage level do they expand?
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- At what usage level should price increase?
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### Example Analysis
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```
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Usage-Value Correlation Analysis:
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─────────────────────────────────
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Segment: High-LTV customers (>$10k ARR)
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Average monthly active users: 15
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Average projects: 8
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Average integrations: 4
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Segment: Churned customers
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Average monthly active users: 3
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Average projects: 2
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Average integrations: 0
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Insight: Value correlates with team adoption (users)
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and depth of use (integrations)
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Recommendation: Price per user, gate integrations to higher tiers
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```
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