PlaybookPrompts

Draft a client case study from raw notes and data

Writing & Content case-studyb2bcontent-marketinglong-form

Client success stories stall because raw notes don't naturally follow a case study arc. This prompt imposes a problem-solution-result structure and forces the writer to surface specifics before drafting, preventing vague, unmemorable copy.

Prompt
You are a B2B content writer. Turn the raw client notes below into a publishable case study. Work through each step before writing.

1. EXTRACT from the notes:
   - The client's situation before the engagement (industry, size, specific problem)
   - The solution or approach used
   - At least one quantified result — if none exists, mark [RESULT NEEDED] and do not invent a number
   - One direct quote from the client (use verbatim if present; if absent, write [QUOTE NEEDED])
2. DRAFT the case study in this structure:
   - Headline: outcome-first, under 15 words
   - Subheadline: one sentence naming the client type and the core result
   - Challenge section (~100 words)
   - Approach section (~150 words) — describe what was done, not how great the vendor is
   - Results section (~100 words) — lead with numbers, then context
   - Pull quote block
   - Closing sentence that is a soft CTA, not a hard sell
3. Write in {{BRAND_VOICE_NOTE}} voice.
4. Total target length: {{TARGET_WORD_COUNT}} words.

Raw notes:
{{CLIENT_NOTES}}

Edge case: If the notes contain no measurable results, this prompt will produce a case study that reads as a testimonial rather than a proof document. Gather at least one metric before publishing.
Variables to fill in
  • {{BRAND_VOICE_NOTE}}
  • {{TARGET_WORD_COUNT}}
  • {{CLIENT_NOTES}}

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: {{BRAND_VOICE_NOTE}}{{TARGET_WORD_COUNT}}{{CLIENT_NOTES}}.
  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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