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Pricing Research Brief

Brief a pricing study — conjoint or Van Westendorp — before you touch a price point.

What is the Pricing Research Brief?

The Pricing Research Brief is a free AI skill that designs the research study behind a pricing decision for food and beverage products — not the price itself, but the methodology that will tell you what to charge. You give it the product, the pricing question you're trying to answer, and your category context; it returns a recommended methodology (Van Westendorp price sensitivity or a conjoint/trade-off design, whichever fits the question), the sample and screening plan, the core question logic, and the analysis approach that turns responses into a defensible price recommendation. It is built for insights managers and research leads who get handed a pricing question and need a rigorous study design before a vendor or internal team fields anything. Because it separates study design from the pricing call itself, the brief stays methodologically honest instead of reverse-engineering questions to justify a price already decided. Live food and beverage demand data helps sanity-check the category price landscape the study will test against.

Who it's for

  • Insights managers scoping a pricing study for a new or existing product
  • Category and revenue management teams commissioning conjoint or price-sensitivity research
  • Market research leads briefing an external pricing research vendor
  • Innovation teams needing a price read before a launch business case

What you get

  • A recommended methodology: Van Westendorp, conjoint, or Gabor-Granger, with rationale
  • A sample plan and respondent screening criteria
  • Core question logic and scale structure for the chosen method
  • Price points or ranges to test, with the reasoning behind each
  • The analysis approach that converts responses into a price recommendation
  • A brief-ready document to hand to a research vendor

How to use it

  1. 1Paste the prompt into ChatGPT, Claude or Gemini.
  2. 2Fill the bracketed inputs (product, pricing question, category, price range).
  3. 3Answer its clarifying questions about your channel and competitive set.
  4. 4Ask it to translate the brief into a fieldable questionnaire draft.

See it in action

Example input

Product: fictional 'Fernbrook' granola, currently $5.49 for a 12oz bag in US grocery. We're considering a reformulation with higher-cost ingredients and need to know how much shoppers will actually pay before we commit to the recipe. Two closest competitors sit at $4.99 and $6.49.

Example output

Pricing research brief — Fernbrook Granola reformulation

Business question, restated
Before committing to a higher-cost recipe, you need to know the ceiling shoppers will actually pay for a reformulated Fernbrook bag — not just whether they say they like the idea of "better" granola.

Recommended methodology: Van Westendorp Price Sensitivity Meter
Given the question is "what price range is acceptable," not "which specific feature combination matters most," Van Westendorp fits better than a full conjoint here — it's faster to field, cheaper, and answers exactly this question. Reserve conjoint for a future study if you need to know which specific reformulation attributes (protein content, ingredient sourcing, pack size) justify the price independently of each other.

Sample plan
Respondents: current granola category buyers, screened to include both Fernbrook buyers and competitive-brand buyers (60/40 split recommended, since you need both loyalist and switcher price sensitivity). Sample size: minimum n=200 per key sub-group if you plan to cut by loyalist vs. switcher; n=150 minimum if reporting only in aggregate. Confirm exact power with your fielding vendor. Exposure: show the reformulated concept (ingredients, claims, positioning) before asking price questions, so responses reflect the new product, not the current $5.49 bag.

Core question logic (the four Van Westendorp questions, asked in this order)
1. "At what price would you consider this product to be so expensive you would not consider buying it?" (too expensive)
2. "At what price would you consider this product starting to get expensive, but you'd still consider buying it?" (expensive/high)
3. "At what price would you consider this product a bargain — a great buy for the money?" (cheap/good value)
4. "At what price would you consider this product so cheap you'd doubt its quality?" (too cheap)

Price points to pre-test in a follow-up screen
Test open-ended price entry first (the four questions above), then, in a second block, show discrete price points at $4.99, $5.49, $5.99, $6.49, and $6.99 and ask purchase intent at each — this discrete read cross-validates the open-ended Van Westendorp curve and maps directly onto your two named competitors' actual shelf prices.

Analysis approach
Plot the four price curves; the range between the "too expensive" and "too cheap" intersection points defines your acceptable price range, and the point where "too expensive" crosses "not cheap" is your optimal price point. Cross-reference this against the discrete purchase-intent read at $5.99 and $6.49 specifically, since those bracket your likely reformulated cost.

Assumptions to validate
This design assumes shoppers can meaningfully evaluate a reformulated concept without physically tasting it — if the ingredient change is a major taste driver, consider pairing this with an in-home or central-location taste test before finalizing price. The 60/40 loyalist/switcher split is a starting recommendation, not a confirmed requirement — validate against your actual buyer base composition.

Vendor-ready brief summary
Fernbrook is testing a reformulated granola concept using Van Westendorp price sensitivity methodology among n=200+ category buyers, split between current Fernbrook and competitive-brand purchasers, to establish an acceptable price range ahead of a recipe finalization decision, cross-validated against discrete purchase intent at five price points spanning $4.99-$6.99.

Want me to turn this into the actual fieldable questionnaire, question by question?

The prompt

Here's the start of the prompt. Download the free bundle for the full, ready-to-paste version — plus the installable Claude Skill and Custom-GPT instructions.

# Role
You are a senior pricing research methodologist for food & beverage brands. You design the study, not the price — you stay rigorous about methodology and refuse to let a pricing brief become a reverse-engineered justification for a number someone already picked.

# Context I'll provide
- Product and current price, if any: [PRODUCT / CURRENT PRICE]
- The pricing question I need answered: [QUESTION e.g. what to charge for a reformulation, a new SKU, or a price increase]
- Category and channel: [CATEGORY / CHANNEL]
- Competitor price points: [COMPETITOR PRICES]
- Constraints: [CONSTRAINTS e.g. budget, timeline, sample access]

# Your task
1. If the product, pricing question, or competitor context is missing or vague, ask up to 3 clarifying questions BEFORE writing anything.

Frequently asked questions

What is a pricing research brief?
A pricing research brief is a study design document for a pricing decision — it specifies the methodology (such as Van Westendorp price sensitivity or conjoint analysis), the sample, the question logic, and the analysis approach, without making the pricing call itself. It's the brief a researcher or vendor needs before fielding any pricing study, keeping the eventual recommendation grounded in a defensible method rather than a guess dressed up as data.
How is this different from the Price-Pack Architecture Planner skill?
The Price-Pack Architecture Planner works out the finished portfolio decision — sizes, packs, and price points across a range, assuming you already know roughly what the market will bear. This skill sits upstream of that: it designs the actual research study that establishes what shoppers will pay in the first place. Run this to get the evidence, then use the architecture planner to turn that evidence into a portfolio of sizes and price points.
Which AI models can run this prompt?
Any capable chat model — ChatGPT, Claude, or Google Gemini. The prompt is model-agnostic, so paste it directly into a chat, save it as a Custom GPT, or store it as a reusable skill so every pricing study your team commissions starts from the same methodological discipline.
Will this tell me what price to actually charge?
No — it designs the study that will answer that question; it does not fabricate a price recommendation before any data exists. It will not invent elasticity curves or benchmark numbers to shortcut the research; every price point it suggests testing is labeled as a starting point, and the real answer comes only after the study is fielded and analyzed.

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