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Content Blueprint

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Generate Customer Stories at Scale

1ClickUpAugust 2026

Turn customer interview recordings into publication-ready case studies in hours instead of weeks. Marketing teams skip expensive writers and manual transcription work by automating the entire story structuring and narrative process.

2 Files Included

  • 02-customer-story-engine-consolidated.md

    27 KB

  • README.md

    2 KB

What problem does this solve?

Produces customer success stories and case studies automatically from interview recordings and notes. Marketing teams use this to turn raw customer interviews into polished stories without hiring writers or spending weeks on manual editing.

How does it work?

  1. Record or upload a customer interview to Otter.ai to generate a transcript.
  2. Copy the transcript into a Google Doc and add any supplementary notes or data points.
  3. Feed the transcript and doc into ChatGPT with a prompt that instructs it to structure the content as a case study, extract key metrics, and write a narrative arc.
  4. Review and refine the output in Google Docs, then export as a polished story. Output: a draft customer story ready for editorial review or publication.

What's the biggest win?

Convert interview recordings into publishable case studies in hours instead of weeks.

What's required to run this?

  • ClickUp account: used to structure and write the story from the transcript.
  • Otter.ai account: generates transcripts from audio recordings; supports both uploads and live recording transcription.
  • Google Docs: stores transcripts and serves as the collaborative editing environment.
  • Prompt template used by this workflow: "You are a marketing writer. Using the transcript and notes below, write a customer case study in this format: [Company name], [Industry], [Challenge], [Solution], [Results/Metrics]. Use quotes from the interview. Aim for 500-800 words. Make it compelling and include specific numbers where available."

What are the constraints?

Accuracy depends on transcript quality; poor audio or heavy accents may require manual transcript correction before feeding into ClickUp. ClickUp may invent metrics or details if the interview does not contain specific numbers, so all facts and figures must be verified against the original recording or customer data before publication.

Tools in this Blueprint

ClickUp logo
4.6(14,130 reviews)
Otter.ai logo
4.4(504 reviews)
Google Docs

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