Files

Corey Haines c29ee7e6db Optimize skill files for AI agent use with progressive disclosure

- 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>

2026-01-26 16:39:45 -08:00

4.4 KiB

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Pricing Research Methods

Van Westendorp Price Sensitivity Meter

The Van Westendorp survey identifies the acceptable price range for your product.

The Four Questions

Ask each respondent:

"At what price would you consider [product] to be so expensive that you would not consider buying it?" (Too expensive)
"At what price would you consider [product] to be priced so low that you would question its quality?" (Too cheap)
"At what price would you consider [product] to be starting to get expensive, but you still might consider it?" (Expensive/high side)
"At what price would you consider [product] to be a bargain—a great buy for the money?" (Cheap/good value)

How to Analyze

Plot cumulative distributions for each question
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

List 8-15 features you could include
Show respondents sets of 4-5 features at a time
Ask: "Which is MOST important? Which is LEAST important?"
Repeat across multiple sets until all features compared
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. Identify value thresholds

At what usage level do customers "get it"?
At what usage level do they expand?
At what usage level should price increase?

Example Analysis

Usage-Value Correlation Analysis:
─────────────────────────────────
Segment: High-LTV customers (>$10k ARR)
Average monthly active users: 15
Average projects: 8
Average integrations: 4

Segment: Churned customers
Average monthly active users: 3
Average projects: 2
Average integrations: 0

Insight: Value correlates with team adoption (users)
        and depth of use (integrations)

Recommendation: Price per user, gate integrations to higher tiers

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