Pricing 7 min read

Price Elasticity Test Template: Free Download

A complete price elasticity test template covering Van Westendorp, Gabor-Granger, and conjoint methods with data collection fields and decision criteria.

Siddharth Gangal

TL;DR

  • Three proven methodologies: Van Westendorp identifies acceptable price ranges through four perception questions. Gabor-Granger builds purchase-intent demand curves across discrete price points. Conjoint analysis reveals how customers trade off price against features.
  • Sample size matters: Gabor-Granger and Van Westendorp tests need 150–200 respondents per segment minimum. Live A/B price tests require 1,000+ observations per variant for statistical significance.
  • B2B SaaS is relatively inelastic: Enterprise customers typically show elasticity coefficients between -0.5 and -0.9. SMB customers run -1.2 to -1.8. Segment your analysis — aggregate elasticity obscures actionable signals.
  • Apply a revealed preference discount: Survey-based tests consistently overstate purchase intent by 10–20% compared to actual buying behavior. Adjust your revenue projections accordingly.
  • Build the full revenue model: A price increase that looks good on conversion metrics can produce net-negative ARR within 12 months if it raises churn. Run the complete unit economics before acting.

Pricing is one of the highest-leverage decisions an operator makes, and one of the least systematically tested. Most SaaS companies set prices based on competitive benchmarking, internal cost calculations, or founder intuition — then leave them unchanged for years. Companies that use rigorous value-based pricing methodologies report up to 25% higher revenue growth compared to those relying on cost-plus or competitor-based approaches alone.

The gap between companies that test pricing and those that do not is not primarily a resource gap. It is a methodology gap. Most teams do not have a structured template for designing a test, collecting data, analyzing results, and translating findings into a defensible pricing decision. This post provides exactly that.

The template below covers three established methodologies — Van Westendorp, Gabor-Granger, and conjoint analysis — with a test design worksheet, data collection fields, analysis framework, and decision criteria for each. Use whichever method fits your stage and objective; the decision framework at the end applies to all three.

The Three Methodologies: When to Use Each

Before selecting a testing approach, it helps to understand what each method actually measures and where each one breaks down.

Van Westendorp Price Sensitivity Meter

Developed by Dutch economist Peter van Westendorp in 1976, the Price Sensitivity Meter (PSM) is a survey-based technique that maps consumer price perception across four distinct thresholds. It is the right starting point when you are launching a new product, entering a new tier, or have no behavioral baseline to work from.

The method asks four questions for each respondent:

  • Too cheap: "At what price would this product be so inexpensive that you would question its quality?"
  • Bargain: "At what price would this product represent a great value — a real bargain?"
  • Expensive but acceptable: "At what price would this product start to feel expensive, but you would still consider buying it?"
  • Too expensive: "At what price would this product be so expensive that you would not consider buying it, regardless of quality?"

The four responses are plotted as cumulative distribution curves. Their intersections define four key thresholds: the Point of Marginal Cheapness (PMC), the Point of Marginal Expensiveness (PME), the Acceptable Price Range (APR) between them, and the Optimal Price Point (OPP) where the maximum number of respondents find the price neither too cheap nor too expensive.

The PSM's primary limitation for SaaS is that it is static — it captures a single moment of perception without accounting for feature changes, competitive context, or the effect of pricing on retention rates and LTV. It is best used as a first-pass instrument, not as the final word on pricing.

Gabor-Granger Method

Developed by economists Clive Granger and André Gabor in the 1960s, the Gabor-Granger method measures purchase intent across a range of discrete price points to construct a demand curve. It is the most direct route to identifying the revenue-maximizing price point within a defined range.

The mechanics are straightforward: respondents are shown a series of price points (typically five) and asked at each one whether they would purchase. The response to each price generates a percentage of respondents who would buy at that level. These percentages, when plotted against price, produce a demand curve — and when multiplied by price, a revenue curve whose peak identifies the optimal price.

The Gabor-Granger approach has a known limitation: because respondents see all price points sequentially, earlier exposures can anchor later responses (a "price anchoring" bias). Research design typically mitigates this through randomized price order presentation. A minimum of 150 respondents is recommended for stable curves; below 100, the confidence intervals become too wide to support a pricing decision.

Conjoint Analysis

Conjoint analysis is the most rigorous and resource-intensive of the three methods. Rather than asking respondents to evaluate a product at a given price in isolation, conjoint analysis presents a series of product configurations — each with different combinations of features and price — and asks respondents to choose between them or rank their preferences.

The statistical output, typically derived through Choice-Based Conjoint (CBC) analysis, reveals part-worth utilities: how much value respondents assign to each feature attribute and to each price level. This allows you to calculate willingness to pay for specific features, identify which feature combinations justify premium pricing, and model how demand shifts when you add or remove features at different price points.

For most SaaS teams below $20M ARR, CBC conjoint is overkill as an initial pricing instrument. It requires specialized survey design software, statistical expertise to analyze correctly, and significantly larger samples. Its natural home is evaluating major pricing model changes — moving from per-seat to usage-based, or restructuring tier boundaries — where the interaction between features and price is what you need to understand.

Price Elasticity Test Template

The following template is structured in four sections: test design, data collection, analysis framework, and decision criteria. Fill in the design section before fielding any survey. Complete the data collection and analysis sections as results come in. Use the decision criteria section to translate findings into a specific pricing recommendation.

Section 1: Test Design Worksheet

Test Design Fields

Test objective

[ ] Identify acceptable price range for new product/tier
[ ] Find revenue-maximizing price point within existing range
[ ] Evaluate how customers trade price against specific features
[ ] Validate a proposed price increase before rollout

Method selected

[ ] Van Westendorp    [ ] Gabor-Granger    [ ] Conjoint (CBC)

Target segment

Define by: company size / revenue band / role / industry / current plan tier:

_____________________________________________

Price range to test (Gabor-Granger / Conjoint)

Floor: $________    Ceiling: $________    Step intervals: $________
Number of price points: ________ (recommend 5–7)

Required sample size

Target n per segment: ________ (minimum 150; recommend 200+)
Confidence level required: [ ] 90%   [ ] 95%   [ ] 99%

Respondent recruitment method

[ ] Email survey to existing customer base (segment: _____________)
[ ] Survey panel (e.g., Respondent.io, Wynter, Qualtrics panel)
[ ] In-app prompt at defined trigger event
[ ] Live A/B test (new visitors only)

Test window

Start date: ________________    End date: ________________
Minimum duration for live tests: 2 full billing cycles (or 4 weeks, whichever is longer)

Section 2: Data Collection Fields

Van Westendorp Data Collection

For each respondent, record:

  • Respondent ID | Segment tag | Date collected
  • Q1 — Too cheap price: $________
  • Q2 — Bargain price: $________
  • Q3 — Expensive but acceptable price: $________
  • Q4 — Too expensive price: $________
  • Respondent role: ________ | Company ARR/revenue band: ________
  • Current pricing tool used (if any): ________

Aggregate outputs to calculate:

  • Point of Marginal Cheapness (PMC): intersection of "too cheap" and "bargain" cumulative distributions
  • Point of Marginal Expensiveness (PME): intersection of "expensive but acceptable" and "too expensive" cumulative distributions
  • Optimal Price Point (OPP): intersection of "too cheap" and "too expensive" distributions
  • Acceptable Price Range (APR): PMC to PME

Gabor-Granger Data Collection

For each price point tested, record:

  • Price point | n respondents shown | n who said "would purchase" | Purchase intent % | Revenue index (price × intent %)
  • Price Point 1: $________ | n= ________ | Would buy: ________ | Intent %: ________ | Revenue index: ________
  • Price Point 2: $________ | n= ________ | Would buy: ________ | Intent %: ________ | Revenue index: ________
  • Price Point 3: $________ | n= ________ | Would buy: ________ | Intent %: ________ | Revenue index: ________
  • Price Point 4: $________ | n= ________ | Would buy: ________ | Intent %: ________ | Revenue index: ________
  • Price Point 5: $________ | n= ________ | Would buy: ________ | Intent %: ________ | Revenue index: ________

Derived outputs:

  • Revenue-maximizing price point: $________ (highest revenue index)
  • Demand elasticity between adjacent price points: ΔQ%/ΔP% for each interval
  • Price point of sharpest demand drop (highest elasticity): $________ to $________

Live A/B Test Data Collection

For each variant, record weekly:

  • Variant label | Price shown | n visitors | n trials started | n converted to paid
  • Visitor-to-trial rate | Trial-to-paid rate | Revenue per visitor
  • Statistical significance at current sample size (use chi-square or Z-test for proportions)
  • Segment breakdown: traffic source / geography / device type (to check for randomization balance)

Stop conditions:

Pause test if conversion delta exceeds 40% in either direction before reaching minimum sample size. Document stop reason.

Section 3: Analysis Framework

Once data is collected, run the following analysis sequence before drawing conclusions.

Analysis Steps

Step 1 — Validate sample quality

Check response distribution for logical consistency. In Van Westendorp, flag and remove any respondent where "too cheap" > "bargain," or "bargain" > "too expensive" — these represent invalid responses that will distort intersection calculations.

Step 2 — Segment before aggregating

Calculate elasticity and price thresholds separately for each defined segment before producing an aggregate result. Aggregate elasticity frequently obscures segment-specific signals. Research from Price Intelligently found that elasticity can vary by a factor of 2–4x between enterprise and SMB cohorts in the same product.

Step 3 — Apply revealed preference discount (survey tests only)

Survey-based purchase intent consistently overstates actual buying behavior. Apply a 15–20% downward adjustment to intent percentages before calculating revenue projections. This is a standard correction in commercial pricing research.

Step 4 — Calculate price elasticity coefficients

For each price interval in a Gabor-Granger test: E = (ΔQ% / ΔP%). An elasticity of -1.0 means a 10% price increase drives a 10% reduction in purchase rate. Values between 0 and -1.0 are inelastic (price increase grows revenue). Values below -1.0 are elastic (price increase reduces revenue). B2B SaaS enterprise segments typically fall between -0.5 and -0.9. SMB segments typically fall between -1.2 and -1.8.

Section 4: Decision Criteria

Price test results only become a pricing decision when they are evaluated against four criteria. Use the following checklist before finalizing any recommendation.

Decision Criteria Checklist

1. Statistical validity

[ ] Sample size meets minimum threshold per segment
[ ] Confidence interval at target significance level does not span the decision threshold
[ ] Test ran for the full planned duration (no early stops based on preliminary results)

2. Revenue impact model

[ ] Revenue projection built at segment level, not aggregate
[ ] Revealed preference discount applied to survey-based intent figures
[ ] Churn sensitivity modeled: at what churn uplift does the revenue gain become net-negative?

3. Competitive context

[ ] Proposed new price benchmarked against 3–5 direct competitors
[ ] No test result drives price above the top of your competitive cluster unless supported by demonstrated differentiation
[ ] Confirm whether competitors have changed pricing in the last 90 days

4. Implementation plan

[ ] Grandfathering policy defined for existing customers
[ ] Communication timeline and messaging drafted
[ ] Churn monitoring window established (minimum 60 days post-rollout)
[ ] Rollback threshold defined: if churn rises above ____% in 30 days, revert

Price Elasticity Benchmarks by Segment

Having a benchmark to compare your results against helps distinguish a normal outcome from an anomaly that warrants further investigation. The following ranges are drawn from published pricing research and industry analysis.

Segment Typical Elasticity Range Interpretation
B2B SaaS — Enterprise −0.5 to −0.9 Inelastic; moderate price increases feasible with low conversion risk
B2B SaaS — Mid-Market −0.8 to −1.3 Near unitary; price increases require value narrative to protect conversion
B2B SaaS — SMB −1.2 to −1.8 Elastic; price increases will reduce conversion rate meaningfully
Consumer SaaS / PLG −1.5 to −2.5 Highly elastic; freemium and trial mechanics reduce price pressure at entry
Tenured customers (2+ years, deep adoption) −0.3 to −0.6 Strongly inelastic; switching cost and workflow dependency dampen sensitivity
New prospects (first 30 days of evaluation) −1.8 to −2.5+ Highly elastic; no switching cost, full competitive optionality

The practical implication of these ranges is that the population you test on determines the result you get. If your Van Westendorp or Gabor-Granger sample skews toward tenured enterprise customers, you will produce an inelastic curve that looks like you have broad pricing power. If it skews toward early-stage prospects, you will see the opposite. Be deliberate about who you recruit for each test.

Connecting Test Results to Operating Decisions

A price elasticity test produces numbers. Converting those numbers into a durable pricing decision requires connecting them to the rest of your operating model. A 20% price increase that generates a 15% uplift in revenue per account looks attractive in isolation. But if it also drives a 4% increase in monthly churn, the math inverts within 12 months — the incremental revenue per account is offset by the accelerated account loss.

This is where most pricing exercises break down. The team runs a survey or an A/B test, identifies a revenue-maximizing price point, and acts on it — without modeling the retention implications. A complete pricing decision model needs four inputs: the conversion impact (from the test), the churn sensitivity (estimated from cohort data or stress-tested against a range), the LTV change at the new price, and the impact on new logo acquisition economics.

Operators using Fairview can pull these inputs from a single view — conversion data, cohort churn rates, and margin per account — without assembling them manually across disconnected tools. The decision framework above maps directly to the data Fairview surfaces, so the move from test results to a pricing recommendation stays grounded in your actual operating numbers, not just the survey output.

ProfitWell research indicates that properly managed price increases — informed by elasticity testing, communicated clearly, and paired with grandfathering for existing customers — result in less than 3% additional churn when implemented correctly. The difference between a clean execution and a disruptive one is almost entirely in the preparation: knowing your elasticity, building the revenue model before you act, and having a defined rollback threshold in place.

Frequently asked questions

What is the minimum sample size for a price elasticity test?

For Gabor-Granger tests, a minimum of 150–200 respondents per segment is needed to generate statistically reliable demand curves. Van Westendorp requires a similar floor, with 200+ respondents producing stable intersections. For live A/B price tests in a SaaS context, OpenView Partners research indicates you need 1,000 or more observations per variant to achieve statistical significance at a 95% confidence level. Smaller tests can still be directionally useful, but pricing decisions based on fewer than 100 respondents should be treated as exploratory rather than definitive.

Which price elasticity method is best for SaaS pricing?

No single method is universally best. Van Westendorp is ideal when you are entering a new pricing tier or launching a new product and have no behavioral baseline — it quickly identifies the acceptable price range without requiring purchase commitment. Gabor-Granger is better when you need to optimize a specific price point and want a clean revenue curve. Conjoint analysis (choice-based CBC) is the most rigorous approach when you need to understand how customers trade off price against features, making it the most useful when evaluating pricing model changes, not just level changes. Most mature SaaS pricing teams run Van Westendorp or Gabor-Granger first for speed, then use conjoint analysis to validate before a major pricing change.

What is a typical price elasticity coefficient for B2B SaaS?

B2B SaaS tends to be relatively inelastic compared to consumer goods. Enterprise customers often show elasticity coefficients between -0.5 and -0.9, meaning a 10% price increase results in a 5–9% decline in conversion rate. SMB customers show higher sensitivity, typically in the -1.2 to -1.8 range. Coefficients vary significantly by segment: loyal customers with deep product adoption may show elasticity as low as -0.4, while new prospects evaluating against alternatives can show elasticity above -2.0. The practical implication is that price increase strategies should be segmented — what is acceptable for tenured enterprise accounts often triggers disproportionate resistance from early-stage buyers.

How do I run a live A/B price test without damaging customer trust?

The safest approach is to run live price tests on new visitor cohorts only, never on existing customers. Show different price points to new users segmented by traffic source, geography, or acquisition channel, and measure conversion rate and trial-to-paid upgrade rate over a defined window. Avoid testing prices that differ by more than 30–40% from your published price, as extreme outliers distort results and risk reputational harm if customers compare notes. Grandfather existing customers at their current rate for at least one full billing cycle before rolling out any price change. Document the test window, sample sizes, and confidence intervals before acting on results — decisions based on short test windows or underpowered samples are a common source of pricing missteps.

How should I use the price elasticity test results to make a decision?

Start by identifying the revenue-maximizing price point from your demand curve, then apply a churn risk adjustment. If your test is survey-based, apply a revealed preference discount of 10–20%: people consistently overstate purchase intent in surveys relative to actual buying behavior. Next, segment the results by customer cohort — the elasticity of new prospects rarely matches that of existing customers. Finally, stress-test the decision against your unit economics: a 20% price increase that increases revenue per account by 15% but raises churn by 4% can produce a net-negative outcome in ARR terms within 12 months. Build the full revenue impact model before acting, not just the conversion impact. Tools like Fairview can surface the downstream margin and ARR effects of a pricing change so the decision is grounded in full operating context, not just conversion metrics.