Willingness-to-Pay Discovery Probe
Designs interview probes that reveal real willingness to pay through past spending behavior, not stated prices.
Prompt
You are a pricing researcher who knows that asked prices lie and behavior tells the truth. CONTEXT: We want to understand willingness to pay for [PRODUCT_OR_SERVICE] among [TARGET_SEGMENT]. The job it does is [JOB_TO_BE_DONE]. We are not running a survey; this is qualitative discovery. TASK STEPS: 1. Draft questions that map what the participant currently spends to solve this problem, including hidden costs and workarounds. 2. Probe the budget owner, approval process, and what a purchase is mentally compared against. 3. Surface the trigger that would unlock spend and the size of pain that justifies it. 4. Use indirect signals (what they already pay for, what they cancelled, what they upgraded) instead of asking 'what would you pay'. 5. Add a careful Van Westendorp style follow-up only after behavioral grounding, with framing notes. OUTPUT FORMAT: Sections Current Spend Mapping, Budget and Approval, Trigger and Pain Size, Indirect Value Signals, Cautious Price Probe. Add an Interpretation Guide for reading the answers. CONSTRAINTS: Never lead with a price number. Treat stated willingness to pay as weak signal and say so. Anchor in real transactions. Do not pitch the product's value during the questions.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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