Pricing and Market Simulation
We study what customers will pay and what they would choose instead. When the study supports it, we model how changes to price, features, or bundles could affect demand and revenue.
Research design
Illustrative research design: an attraction is considering a new venue and needs to choose its features and ticket offer.
Ranking features can show what people value. Comparing combinations can show which mix appeals to more people. Testing offers at different prices adds the tradeoffs needed for a pricing decision.
Read a completed product pricing studyStated preferences and modeled choices are not guaranteed attendance or revenue. The assumptions and available alternatives affect the interpretation.
Customers judge price alongside the features, benefits, brand, package, and alternatives that shape the offer's value.
Research records how people respond to prices and offers. A simulator uses a model of those responses to compare options you are considering. The steps below connect the two.
Step 1
Show the intended buyers realistic offers, prices, and alternatives. Specify what is included and how payment works so people evaluate the same terms.
Step 2
Examine how well the model represents participants' responses. Use relevant sales or market data to assess the estimates where those inputs are available.
Step 3
Change the price, package, or competitive offer within the model's scope. Report the estimated differences, the assumptions, and what the study cannot establish.
Compare scenarios against the same baseline and assumptions. Modeled choice is not automatically market share, and higher revenue does not establish higher profit without the relevant costs.
The method depends on whether you need to explore value, test prices for a defined offer, or compare combinations of features and price. These approaches answer different questions.
Method
When it helps
Participants choose among offers that vary in price, features, brand, or package. The model estimates how those differences affect choice.
Business decision
Which combinations do customers choose among the alternatives tested?
Method
When it helps
A suitable research model is available and the team needs to compare specified offer or competitor changes. Results depend on the assumptions and alternatives included.
Business decision
How do estimated choices change across the scenarios we can realistically implement?
Method
When it helps
Participants choose the most and least appealing or important features from repeated sets. This prioritizes features; it does not measure willingness to pay on its own.
Business decision
Which benefits should we take into an offer or pricing test?
Method
When it helps
Participants describe prices they regard as cheap, expensive, too cheap, or too expensive for a defined offer. These are perceived thresholds, not a sales forecast.
Business decision
What price range warrants further evaluation?
Method
When it helps
Participants state whether they would buy a defined offer at specified prices. The offer stays consistent while price changes.
Business decision
How does stated purchase interest change by price point?
Method
When it helps
Separate groups evaluate one price or offer version each, without seeing the other test options.
Business decision
Which price or offer version performs better?
Method
When it helps
The team needs to compare combinations of items while accounting for people interested in more than one. Reach does not establish purchase or revenue.
Business decision
Which combination appeals to the most different people?
Method
When it helps
Interviews or discussions explore what buyers compare, what they expect to receive, and why an offer seems worth its price.
Business decision
What should the pricing study measure or explain?
This public case shows price-sensitivity research followed by qualitative work to explain the result.
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The scope may call for a price-response report, scenario tables, or a simulator. Agree who will use the findings and which decisions the outputs need to support.
An explanation of what customers value and how they respond to the tested prices. The report distinguishes feature priorities, perceived price thresholds, and choices among offers.
Illustrative question
Does a feature remain attractive when customers see the price of the offer that includes it?
A recommendation tied to the prices and offers evaluated, including the alternatives considered and the evidence behind the preferred option.
Illustrative question
Does the bundle need a different price, different contents, or a clearer explanation of its value?
Where the design supports modeling, named scenarios show how estimated outcomes differ from a common baseline. Each comparison states what changed and what stayed fixed.
Illustrative question
Does the preferred option still look attractive if a competitor lowers its price?
Scenario tables or a simulator, where included in scope, come with guidance on what the model can compare and how to interpret its results.
Illustrative question
Is a proposed price or package within the conditions studied, or does it require another test?
Start with what is still open to change. If the offer is fixed, a price-response study can examine interest at different price points. Van Westendorp questions explore perceived price thresholds, while Gabor-Granger examines stated purchase interest across specified prices. If features, tiers, brands, or competing offers also matter, conjoint choice research may be more useful. Feature prioritization can inform that design, but does not establish willingness to pay on its own. We recommend the method against the decision and the comparisons required.
Review current prices, competitive alternatives, the offer's value, and the range the business could realistically implement. The range needs enough variation to reveal tradeoffs while keeping the choices credible. Participants also need a clear price basis: what is included, the pack or usage quantity, payment frequency, and any relevant fees or commitment. We agree those details before fieldwork. A model should not be treated as evidence for prices or offer combinations far outside the conditions participants evaluated.
Yes. The design can vary features, service levels, package sizes, and prices to examine which combinations people choose. Including your existing offers helps investigate whether a new option attracts demand or mainly shifts preference within your own range. We can also explore why a bundle underperforms. In Russell's published commercial floor-cleaner study, qualitative follow-up helped explain a weak bundle result after the quantitative pricing work. The price and the contents of an offer may need different changes.
We define alternatives around the customer's actual decision, including relevant competitors and the ability to keep the current solution or decline the purchase where appropriate. A forced choice among only your own offers answers a narrower question than a market comparison. A simulator can then explore specified changes to competitor prices or features within the model's scope. Those scenarios describe what could happen under stated assumptions; they do not establish what competitors will do or how the entire market will respond.
That depends on whether the decision concerns retaining customers, winning new ones, or changing the offer for both. Current customers may judge value using experience, existing terms, or switching costs that prospects do not share. Where those differences matter, the sample must support separate analysis. In B2B studies, we also establish who uses, selects, and approves payment for the offer. A customer's stated response to a hypothetical price increase should not automatically be interpreted as observed cancellation or renewal behavior.
No. Pricing research can estimate responses and compare scenarios, but actual sales also depend on factors such as awareness, distribution, availability, competitor activity, and repeat purchase. Modeled choice among the offers in a study is not automatically market share. Where suitable sales or market data exists, it may help calibrate the model and assess whether estimates are plausible. We explain the assumptions, the evidence supporting them, and the limits of applying the findings beyond the tested market conditions.
Not necessarily. A lower price may attract more demand while generating less revenue per purchase. A higher-revenue option may also cost more to deliver. Research-based demand estimates can be combined with price to compare revenue scenarios; assessing contribution or profit additionally requires appropriate cost inputs and a clear definition of what is included. We agree which objective matters, such as adoption, revenue, or contribution, and compare the tradeoffs. A demand score alone cannot identify the most profitable price.
Share the decision deadline, target audiences, current and proposed offers, price ranges, key competitors, and what can realistically change. Existing research, sales patterns, and customer data can help establish the starting point; cost inputs are needed if financial comparisons extend beyond revenue. We also agree which scenarios your team expects to compare and who will use the outputs. Audience availability, markets, offer complexity, and modeling requirements determine the scope, timing, and whether an interactive simulator or a focused set of scenario tables is more useful.
Explore tools selected for this research. Browse all research tools and calculators.
What prices feel reasonable to your audience?
Import respondent prices, review inconsistent answers, compare conventional price ranges, and measure resampling stability. Data stay in your browser.
Analyze price perceptionsWhich tested prices have the highest stated revenue and contribution?
Analyze stated purchase response at tested prices. Compare revenue and contribution, import local CSV records, inspect uncertainty, and export the methods.
Analyze tested pricesTell us which price, package, or market move is under consideration and what alternatives customers will actually see.
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