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. Validate responses in one currency, billing period, and product description. Flag missing, nonnumeric, or inconsistent threshold ordering (too cheap ≤ cheap ≤ expensive ≤ too expensive); document any exclusions instead of silently changing answers.
2. Plot candidate price on the x-axis and the share of respondents on the y-axis. The two cheap curves decrease as price rises; the two expensive curves increase. At each candidate price p, use:

Use a consistent endpoint convention for ties and record it. Do not apply an increasing cumulative distribution to all four questions: it reverses the meaning of the cheap curves. Sawtooth's example illustrates the too-cheap share falling as price increases.

1. Find the intersections:
2. Point of Marginal Cheapness (PMC): "Too cheap" crosses "Expensive"
3. Point of Marginal Expensiveness (PME): "Too expensive" crosses "Cheap"
4. Optimal Price Point (OPP): "Too cheap" crosses "Too expensive"
5. Indifference Price Point (IDP): "Expensive" crosses "Cheap"

The acceptable price range: PMC to PME Candidate prices to test: OPP and IDP can inform a shortlist; they do not establish an economically optimal zone.

The four questions measure price perceptions, not purchase probabilities. Before forecasting demand, use additional purchase-intent research (such as the Newton-Miller-Smith extension, with its assumptions stated), a choice study or actual price tests. Compare revenue, margin and retention; a higher acceptable price does not guarantee unchanged demand. See Sawtooth's explanation of the method and its limits.

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:
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Point of Marginal Cheapness:  $29/mo
Optimal Price Point:          $49/mo
Indifference Price Point:     $59/mo
Point of Marginal Expensiveness: $79/mo

Candidate test prices: $49 and $59/mo
Current price: $39/mo
Price changes: +25.6% and +51.3%
Demand, conversion and churn impact: not measured by these four questions
Next step: validate purchase behavior and unit economics before rollout

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

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:
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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