PlaybookPrompts

Generate pain hypotheses for a new persona you're targeting

Sales & Outreach discoverypersona-researchprospecting

Breaking into a new persona means building credibility fast. This prompt generates a ranked list of pain hypotheses you can test in early calls — grounded in what that role actually worries about, not what your product solves.

Prompt 
You are a B2B sales strategist with experience selling to {{TARGET_PERSONA}} roles. I'm expanding outreach to this persona and need to build a realistic picture of their daily frustrations before I start pitching.

1. List the top 6 pains this persona is most likely experiencing in companies of {{COMPANY_SIZE}} in the {{TARGET_INDUSTRY}} industry. Write each pain from the first-person perspective of the persona, in plain language — not product-category language.
2. Rank the 6 pains by how acutely they're likely felt (most urgent first) and briefly explain your ranking logic.
3. For each pain, write one diagnostic question I could ask in a discovery call to confirm or rule out whether this pain exists for this specific person.
4. Flag which pains my product (described below) addresses directly, which it addresses partially, and which it doesn't address — be honest.
5. Suggest two adjacent pains I should know about even if I can't solve them, because they affect buying decisions.

Target persona (title / function): {{TARGET_PERSONA}}
Company size and industry: {{COMPANY_SIZE}} / {{TARGET_INDUSTRY}}
What my product does: {{PRODUCT_DESCRIPTION}}

Note: Pain hypotheses are starting points for discovery, not scripts. Treat any item your prospect doesn't confirm as invalid.
Variables to fill in
  • {{TARGET_PERSONA}}
  • {{COMPANY_SIZE}}
  • {{TARGET_INDUSTRY}}
  • {{PRODUCT_DESCRIPTION}}

How to use this prompt

  1. Copy the prompt above (Copy button on the top-right).
  2. Replace each {{VAR}} with your own value. Variables: {{TARGET_PERSONA}}{{COMPANY_SIZE}}{{TARGET_INDUSTRY}}{{PRODUCT_DESCRIPTION}}.
  3. Paste it into one of the recommended tools below.
  4. Iterate: tighten constraints in the prompt if the output is generic.

Why this prompt is structured this way

The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.

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