PlaybookPrompts

Write a qualifying response to a vague inbound lead

Sales & Outreach inboundqualificationemail

Inbound leads that say 'interested in learning more' give reps almost nothing to work with. This prompt writes a reply that qualifies intent and budget without sounding like a questionnaire.

Prompt
You are a sales development rep. Write a single reply email to a vague inbound lead that advances qualification without overwhelming the prospect with questions.

Context:
- Our product: {{OUR_PRODUCT}}
- Lead's name and title (if known): {{LEAD_INFO}}
- Lead's company (if known): {{LEAD_COMPANY}}
- What the lead said or submitted (paste their message or form response): {{LEAD_MESSAGE}}
- The one qualification dimension most critical for us (e.g., team size, tech stack, budget range, use case): {{KEY_QUAL_DIMENSION}}

Write the reply following these steps:
1. Open with one sentence that confirms you read their specific message (reference a detail)
2. Provide one concrete, relevant proof point (customer example or stat) matched to the apparent use case
3. Ask exactly one open-ended question targeting {{KEY_QUAL_DIMENSION}} — make it feel like curiosity, not a form field
4. Propose a specific next step with a soft out ('if timing is right')
5. Keep the entire email under 100 words

Avoid: 'Great to hear from you', 'hope this finds you well', any sentence starting with 'I'.
Variables to fill in
  • {{OUR_PRODUCT}}
  • {{LEAD_INFO}}
  • {{LEAD_COMPANY}}
  • {{LEAD_MESSAGE}}
  • {{KEY_QUAL_DIMENSION}}

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: {{OUR_PRODUCT}}{{LEAD_INFO}}{{LEAD_COMPANY}}{{LEAD_MESSAGE}}{{KEY_QUAL_DIMENSION}}.
  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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