What This Template Is For
Most SaaS companies set prices based on competitor benchmarking, gut feel, or cost-plus math. All three methods ignore the most important input: what customers are actually willing to pay for the specific value your product delivers. Pricing research closes that gap by gathering structured data on willingness to pay before you commit to a pricing change.
This template covers the three most practical pricing research methods for SaaS: Van Westendorp (price sensitivity), Gabor-Granger (demand curve estimation), and structured pricing interviews. It includes survey scripts, interview guides, analysis frameworks, and a synthesis template for turning research data into pricing decisions. Each method can be executed in 2-4 weeks with basic survey tools and 50-100 respondents.
The Product Strategy Handbook covers how pricing research fits into the overall pricing strategy process. The Product Analytics Handbook explains how to segment your customer base for research sampling. For understanding willingness to pay as a concept, the willingness-to-pay glossary entry covers the theory. If you are running research to support a specific pricing change, use the Pricing Migration Template alongside this one.
How to Use This Template
- Define your research question. Are you validating a new price point? Choosing between pricing models? Testing willingness to pay for a new feature? The question determines which method to use.
- Select your method. Van Westendorp for price range discovery. Gabor-Granger for demand estimation at specific price points. Interviews for qualitative depth and model preference.
- Build your sample. You need 50-100 survey respondents or 15-20 interview participants, segmented by customer type.
- Run the research. Send surveys or conduct interviews using the scripts in this template.
- Analyze and synthesize. Use the analysis frameworks to extract pricing recommendations.
- Present findings to stakeholders with confidence intervals and segment breakdowns.
The Template
Research Planning
| Parameter | Details |
|---|---|
| Research question | [e.g., "What price range is acceptable for our new Pro tier?"] |
| Method(s) | [Van Westendorp / Gabor-Granger / Interviews / Combination] |
| Target sample size | [N] respondents or [N] interviewees |
| Segments to include | [Current customers, churned customers, prospects, by company size, by plan] |
| Timeline | [X] weeks (survey design: [X] days, data collection: [X] days, analysis: [X] days) |
| Budget | [Survey tool cost, incentives, interviewer time] |
| Decision this research informs | [e.g., "Set price for Q3 Pro tier launch"] |
Sample design:
| Segment | Target N | Source | Incentive |
|---|---|---|---|
| Current customers (Starter) | [N] | In-app survey or email | [Gift card, account credit, early access] |
| Current customers (Pro) | [N] | Email invitation | [Gift card, account credit] |
| Churned customers (last 6 months) | [N] | [Gift card] | |
| Prospects (in pipeline) | [N] | Sales team invitation | [Gift card, free trial extension] |
| Prospects (never trialed) | [N] | Panel or LinkedIn outreach | [Gift card] |
Method 1: Van Westendorp Price Sensitivity Meter
When to use: You want to discover the acceptable price range for a product or feature. Works best when customers have a reference point for the category.
Survey script:
Context paragraph (customize for your product):
We are considering pricing options for [Product/Feature]. It [brief description of what it does and the value it delivers]. We would like your honest input on pricing. There are no right or wrong answers.
Question 1 (Too Cheap):
At what price would you consider [Product/Feature] to be so inexpensive that you would question its quality? (Price per [unit/month/year])
Question 2 (Bargain):
At what price would you consider [Product/Feature] to be a bargain, a great buy for the money?
Question 3 (Getting Expensive):
At what price would you consider [Product/Feature] to be getting expensive, so that it is not out of the question, but you would have to give some thought to buying it?
Question 4 (Too Expensive):
At what price would you consider [Product/Feature] to be so expensive that you would not consider buying it?
Segmentation questions (include after the four price questions):
| Question | Options |
|---|---|
| What is your role? | [PM, Engineering, Design, Executive, Other] |
| Company size? | [1-10, 11-50, 51-200, 201-1000, 1000+] |
| Do you currently use [Product]? | [Yes (current customer), No (used to), No (never used)] |
| If yes, which plan? | [Free, Starter, Pro, Enterprise] |
| How would you describe your budget for [category] tools? | [Very limited, Moderate, Flexible, Not a concern] |
Analysis framework:
Plot four cumulative distribution curves:
- Too Cheap (cumulative from high to low)
- Bargain (cumulative from high to low)
- Getting Expensive (cumulative from low to high)
- Too Expensive (cumulative from low to high)
| Intersection | What It Means | Your Result |
|---|---|---|
| Too Cheap + Getting Expensive | Point of Marginal Cheapness (price floor) | $[X] |
| Bargain + Too Expensive | Point of Marginal Expensiveness (price ceiling) | $[X] |
| Too Cheap + Too Expensive | Optimal Price Point (least resistance) | $[X] |
| Bargain + Getting Expensive | Indifference Price Point (equal value perception) | $[X] |
| Acceptable price range | Between Marginal Cheapness and Marginal Expensiveness | $[X] - $[X] |
Method 2: Gabor-Granger Direct Price Testing
When to use: You have 3-5 specific price points in mind and want to estimate demand at each one. Simpler than Van Westendorp, gives you a demand curve.
Survey script:
Context paragraph (same as Van Westendorp):
We are considering pricing options for [Product/Feature]. It [description]. We would like your honest input.
Sequential price questions (randomize starting price across respondents):
| Price Point | Question |
|---|---|
| $[A]/mo | "Would you purchase [Product/Feature] at $[A] per month?" [Definitely yes / Probably yes / Probably no / Definitely no] |
| $[B]/mo | "Would you purchase [Product/Feature] at $[B] per month?" [Same scale] |
| $[C]/mo | "Would you purchase [Product/Feature] at $[C] per month?" [Same scale] |
| $[D]/mo | "Would you purchase [Product/Feature] at $[D] per month?" [Same scale] |
| $[E]/mo | "Would you purchase [Product/Feature] at $[E] per month?" [Same scale] |
Analysis framework:
| Price Point | Definitely + Probably Yes | Revenue Index (Price x % Yes) | Optimal? |
|---|---|---|---|
| $[A]/mo | [X]% | $[A] x [X]% = $[result] | - |
| $[B]/mo | [X]% | $[B] x [X]% = $[result] | - |
| $[C]/mo | [X]% | $[C] x [X]% = $[result] | - |
| $[D]/mo | [X]% | $[D] x [X]% = $[result] | - |
| $[E]/mo | [X]% | $[E] x [X]% = $[result] | - |
The price point with the highest Revenue Index (price multiplied by purchase probability) is the revenue-maximizing price. The price point with the highest purchase probability is the volume-maximizing price. Your optimal price depends on whether you are optimizing for revenue or market share.
Method 3: Structured Pricing Interviews
When to use: You need qualitative depth on how customers think about pricing, what they compare you to, and what features drive willingness to pay. Best combined with one of the survey methods above.
Interview guide (30 minutes):
Block 1: Current State (5 minutes)
- How do you currently solve [the problem your product addresses]?
- What tools do you use and approximately what do you pay for them?
- How do you evaluate whether a tool in this category is worth the price?
Block 2: Value Perception (10 minutes)
- Which features of [Product] deliver the most value to your team?
- If you had to keep only 3 features and lose the rest, which 3 would you keep?
- Which features do you rarely or never use?
- Is there a feature you wish existed that would make you willing to pay more?
Block 3: Pricing Reaction (10 minutes)
- [Show current pricing page] What is your first reaction to this pricing?
- [Show proposed pricing] How does this compare? What stands out?
- At this price, what would you expect to receive that you do not get today?
- What would make you upgrade from [current tier] to [next tier]?
- What would make you downgrade or cancel?
Block 4: Competitive Context (5 minutes)
- What other tools did you evaluate before choosing [Product]?
- How did their pricing compare?
- Was price the deciding factor? If not, what was?
Interview analysis template (one row per participant):
| Participant | Role | Company Size | Current Plan | Value Drivers | Price Reaction | WTP Range | Key Quote |
|---|---|---|---|---|---|---|---|
| P1 | [Role] | [Size] | [Plan] | [Top 3 features] | [Positive/Neutral/Negative] | $[X]-$[X] | "[Quote]" |
| P2 | [Role] | [Size] | [Plan] | [Top 3 features] | [Positive/Neutral/Negative] | $[X]-$[X] | "[Quote]" |
Synthesis Template
Combine findings from all methods into a single recommendation:
| Finding | Source | Confidence | Implication |
|---|---|---|---|
| [e.g., "Optimal price for Pro tier is $49-69/mo"] | Van Westendorp (N=87) | High | Price Pro at $59/mo (mid-range) |
| [e.g., "Revenue maximized at $59/mo, volume maximized at $39/mo"] | Gabor-Granger (N=92) | High | $59 aligns with Van Westendorp |
| [e.g., "Enterprise buyers anchor on per-seat pricing"] | Interviews (N=18) | Medium | Keep per-seat for Enterprise, flat for SMB |
| [e.g., "AI features have 35% higher WTP than base product"] | Interviews (N=18) | Medium | Consider AI add-on or premium tier |
| [e.g., "Churned customers cite price as #2 reason after competitor features"] | Interviews (N=6 churned) | Low (small N) | Price is a factor but not the primary churn driver |
Recommendation:
| Decision | Recommendation | Data Support | Risk |
|---|---|---|---|
| [Price point] | $[X]/mo for [Plan] | Van Westendorp optimal: $[X], Gabor-Granger revenue-max: $[X] | [Low/Medium/High] |
| [Pricing model] | [Per-seat / Flat / Usage] | Interviews: [X] of [N] prefer [model] | [Low/Medium/High] |
| [Tier structure] | [N] tiers at $[X], $[X], $[X] | Gabor-Granger demand curve shows natural breakpoints | [Low/Medium/High] |
| [Feature allocation] | [Feature X] in [Tier], [Feature Y] in [Tier] | Interviews: value ranking matches proposed allocation | [Low/Medium/High] |
Filled Example: B2B SaaS Analytics Platform
Research Planning
| Parameter | Details |
|---|---|
| Research question | What should we charge for a new "Team" plan between Free and Enterprise? |
| Methods | Van Westendorp (survey) + Structured Interviews |
| Sample | 92 survey respondents + 18 interviewees |
| Timeline | 3 weeks (design: 3 days, collection: 12 days, analysis: 4 days) |
Van Westendorp Results (N=92)
| Intersection | Price Point |
|---|---|
| Point of Marginal Cheapness | $29/mo |
| Point of Marginal Expensiveness | $89/mo |
| Optimal Price Point | $49/mo |
| Indifference Price Point | $59/mo |
| Acceptable range | $29 - $89/mo |
Segment breakdown:
| Segment | Optimal Price | Acceptable Range |
|---|---|---|
| Startups (<50 employees) | $39/mo | $19 - $69/mo |
| Mid-market (50-500) | $59/mo | $39 - $99/mo |
| Growth stage (funded, scaling) | $69/mo | $49 - $119/mo |
Interview Insights (N=18)
Top value drivers (ranked by frequency):
- Custom dashboards (mentioned by 16/18)
- SQL access to raw data (14/18)
- Automated weekly reports (12/18)
- Team sharing and collaboration (11/18)
- Data export (10/18)
Pricing model preference:
- 11/18 prefer flat team pricing ("I do not want to worry about adding an analyst to the team")
- 5/18 prefer per-seat ("Easier to justify per-person cost to my CFO")
- 2/18 prefer usage-based ("We only run heavy queries certain weeks")
Key quote: "I would pay $60/month for a plan that lets my whole data team access dashboards without worrying about per-seat costs. Right now I have 3 seats and 8 people who need access, so they share a login. That is bad for everyone."
Synthesis and Recommendation
| Decision | Recommendation | Data |
|---|---|---|
| Team plan price | $59/mo | VW optimal: $49, mid-market segment: $59, interviews support $50-70 range |
| Pricing model | Flat team (up to 10 users) | 11/18 interviewees prefer flat team model |
| Hero feature | Custom dashboards + SQL access | Top 2 value drivers from interviews |
| User limit | 10 users included, $5/user above | Balances "whole team" value prop with expansion revenue |
Common Mistakes to Avoid
- Asking employees instead of customers. Internal stakeholders have strong opinions about pricing but they are not the ones paying. Always research with actual or potential customers. Your sales team's intuition is useful context but it is not data.
- Sample bias toward happy customers. If you only survey active, engaged users, your WTP estimates will be inflated. Include churned customers, non-converters, and prospects in your sample. The customer segmentation glossary entry covers how to build a balanced research sample.
- Ignoring the "Too Cheap" signal. In Van Westendorp, the "Too Cheap" line tells you where customers start doubting quality. If your price is below this threshold, you are leaving money on the table and potentially hurting perceived value. Many SaaS products are underpriced.
- Running research once and treating it as permanent. Willingness to pay changes as your product improves, competitors shift, and the market evolves. Re-run pricing research annually or whenever you make a significant product change. The RICE framework can help you prioritize which pricing research questions to tackle first.
- Small sample sizes without acknowledging uncertainty. With 30 respondents, your Van Westendorp curves will be noisy. Report confidence intervals or at least acknowledge the margin of error. "The optimal price is between $45-55 based on 30 responses" is more honest than "The optimal price is $49."
Key Takeaways
- Price based on customer willingness-to-pay data, not competitor benchmarks or gut feel
- Van Westendorp finds the acceptable price range. Gabor-Granger estimates demand at specific prices. Interviews explain why.
- Segment your research sample: startups, mid-market, and enterprise have different WTP
- Re-run pricing research annually or before any major pricing change
- Report findings with confidence levels. Small samples give directional guidance, not certainty
About This Template
Created by: Tim Adair
Last Updated: 3/5/2026
Version: 1.0.0
License: Free for personal and commercial use