Tailored discovery call question list
Use this after your research brief to get a prioritized list of open-ended discovery questions tailored to this prospect, grouped by stage of the call, with the reason behind each question.
Free AI prompt · ChatGPT · Claude · Gemini
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One-page prospect research brief
Use this 10 minutes before a call. Paste the prospect's website copy, LinkedIn About section and any notes, and get a one-page brief that tells you what they do, what they likely care about and where you fit.
What you'll fill in
- [prospect company] The company you are meeting with e.g. “Ferro Coffee Roasters”
- [contact name and role] Who you are meeting and their job title e.g. “Dana Ruiz, Head of Operations”
- [pasted research] Paste their homepage/About page text, the contact's LinkedIn About and recent posts, and any news or notes you have e.g. “(homepage text, LinkedIn summary, 2 recent posts)”
- [what you sell] One or two sentences on your product or service e.g. “Inventory forecasting software for small food and beverage brands”
Copy the prompt
The prompt
You are a senior account executive helping me prepare for a sales call. Build a one-page prospect brief using ONLY the research I paste below. You do not have live access to the internet unless browsing is turned on, so do not invent facts, funding, headcount or customers. If something is not in the material, write "Unknown — ask on the call." Prospect company: [prospect company] Person I am meeting: [contact name and role] What I sell: [what you sell] Research (pasted): """ [pasted research] """ Produce the brief with these sections: 1. Company in two sentences — what they sell, to whom, and how they seem to make money. 2. Signals worth noticing — 3 to 5 specific details from the research (new product, hiring, expansion, a complaint, a priority the contact posts about). Quote or cite the line each one comes from. 3. Likely priorities for [contact name and role] — 3 bullets, each labelled "Stated" (in the source) or "Inferred" (your reasoning, with one line on why). 4. Where we might fit — 2 or 3 plausible problems my offer could solve for them, ranked by how strongly the research supports it. 5. Risks and unknowns — what could make this a poor fit, and the facts I still need. 6. Three conversation openers that reference something specific from the research, without sounding like I stalked them. Keep the whole brief under 400 words. Use short bullets. Be honest when the evidence is thin.
How to use it
- Open a new chat in Claude (the others work too).
- Paste the prompt and replace every [bracket] with your details.
- Review the output, check any facts, and edit to taste.
Why Claude: Claude handles long pasted pages well and is careful about separating what the source says from what it is guessing.
Tips
- Paste raw text rather than links. Unless your tool has browsing turned on, the model cannot open URLs.
- Paste the contact's own recent posts. They are the best signal of what that person cares about this quarter.
- Check the "Inferred" bullets before the call. They are hypotheses to test, not facts to repeat back.
Example output
Prospect brief — Ferro Coffee Roasters (meeting Dana Ruiz, Head of Operations)
1. Company: Ferro roasts and sells specialty coffee through its own online store, three cafés and a growing wholesale list of restaurants. Revenue appears split between direct-to-consumer subscriptions and wholesale.
2. Signals worth noticing
- "Now shipping nationwide" banner on the homepage, so they recently expanded beyond local delivery.
- Careers page lists a "Warehouse Lead" opening.
- Dana posted: "Nothing humbles you like running out of your best seller during a holiday rush."
- The wholesale page says "new partners onboarding monthly."
3. Likely priorities for Dana
- Avoiding stockouts on best sellers (Stated, from her post).
- Scaling fulfilment for national shipping (Inferred: new banner plus warehouse hire).
- Forecasting green-coffee purchases (Inferred: longer lead times for importers. Verify.)
4. Where we might fit
- Demand forecasting by SKU across DTC and wholesale (strong support).
- Reorder alerts for green-coffee inventory (moderate support).
5. Risks and unknowns
- Current tools unknown. Ask about spreadsheets versus their ERP.
- Unknown who signs off on software purchases.
6. Openers
- "Your post about the holiday stockout stuck with me. What happened behind the scenes?"
- "Congrats on going national. What has changed most in the warehouse?"
- "How are you balancing wholesale orders against subscriptions right now?"
FAQ
Does this prompt work in ChatGPT, Claude and Gemini?
Yes. It is plain-language and written to work in all three. Best fit: Claude. Claude handles long pasted pages well and is careful about separating what the source says from what it is guessing.
What do I need to fill in?
Replace each bracket with your own details: [prospect company], [contact name and role], [pasted research], [what you sell]. The more specific your input, the more useful the result.
Is this prompt free?
Yes. Copy it above and use it as often as you like. It is one of the free samples from the Sales call prep in 15 minutes pack.
Will I get the same result as the example?
No. The example was made for a fictional business to show the shape and quality of the output. Your result depends on your input and the model, so review and edit before you use it.
Do I need a paid AI plan?
Usually not. Free plans handle most prompts. Paid plans help when you paste in long documents or run a long multi-step sequence in one chat.
More prompts in Sales call prep in 15 minutes
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Use this when you want the call to feel organized from the first minute. You get a timed agenda you can share, plus a natural opening script that sets expectations and earns permission to ask questions.
Objection handling prep (two-step)
Use this before a call where price, timing or a competitor is likely to come up. First the model predicts the objections this prospect is likely to raise. Then it drafts honest responses and clarifying questions you can actually say.
Sales call role-play with feedback (three-step)
Use this when a call really matters. The AI plays your prospect, with realistic resistance, for a practice run. Then it steps out of character and scores how you did against the skills that matter.