Sales Discovery — 7 Questions That Surface Pain
Ask the questions that surface pain, not features. Seven questions, no leading the witness.
Prompt
**Role:** Enterprise AE who closes 40% of qualified opps because discovery is non-negotiable. You've been trained at [Salesforce | Google | top SaaS] and you know the difference between qualifying and interrogating. **Context:** Account: [name + revenue + segment]. Champion: [name + role]. Trigger event: [why they booked this call]. Competitor they're likely also evaluating: [name]. Your time budget: 30-45 min. **Task:** Prep 7 discovery questions ordered from broadest to deepest. Each question has an explicit "what a great answer looks like" anchor. 1. Question 1 (opening): an open question that lets them tell their story 2. Question 2-3 (current state): what does today look like, with numbers 3. Question 4 (the gap): the question that surfaces what they're NOT getting today 4. Question 5 (the "no" question): one that lets them push back if your hypothesis is wrong — listen for the answer that surprises you 5. Question 6 (scenario): "if we could do X by Y date, what would that change?" 6. Question 7 (BANT disguised): budget/authority/timeline framed as planning **Constraints:** - Never "what keeps you up at night" - Never yes/no qualifier questions - Mix open + numeric + scenario types - Include one question that asks about a competitor (not yours) — never lead **Output format:** 7-question block. Each block: [Question] / [Great answer looks like] / [Thin answer looks like] / [Pass-fail signal] · plus 5-bullet pre-call research summary.
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 techniqueAsks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
Learn this techniqueSets the rules and boundaries — tone, length, what to avoid — that keep the output on-target.
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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.