Blog Writing

Verified against Claude · 2026-08-01

Turn raw customer interview notes into a case study post that reads like proof, not an ad

Structures a customer case study around a quantified result and honest causal framing, using only quotes actually said in the interview, and applying anonymization constraints exactly instead of loosely.

ChatGPTClaudeGemini5 fillable variables

The prompt

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You are turning raw customer interview notes into a case study blog post. The post's credibility depends on reading like evidence a skeptical prospect would trust, not like a testimonial an ad team wrote — which means every claim, quote, and causal statement has to trace back to what the customer actually said.

INTERVIEW NOTES
Ops director said: "We were losing about 4 hours a week just reconciling the two spreadsheets before this." Mentioned they also hired a new ops hire around the same time, and switched project tools in Q2 — result likely a combination of all three, not the tool alone.

CUSTOMER NAME AND ROLE
Priya Nair, Director of Operations at a 60-person logistics startup (real name, company approved for use)

QUANTIFIABLE RESULT
Reconciliation time dropped from roughly 4 hours/week to under 30 minutes/week

PERMISSION CONSTRAINTS
Company name and Priya's name and title are approved for use; specific revenue figures mentioned in the interview are not for publication.

AUDIENCE
Ops leads at similarly sized logistics or fulfillment companies evaluating whether to switch tools

CASE STUDY RULES
Lead with the specific, quantified result, not the product name or a generic "success story" framing — a reader deciding whether to keep reading needs the concrete number in the first few lines, not buried after three paragraphs of company background. Structure the body as problem, approach, result, and what changed for the customer specifically — not a feature list dressed up in narrative language; a case study that just walks through the product's feature set with the customer's name attached reads as a product page wearing a disguise, not as evidence. Use direct quotes only where the interview notes actually contain that language — never construct a first-person quote that sounds like something the customer would plausibly say based on the general sentiment of the interview; if the notes describe a sentiment but don't contain a quotable line, paraphrase it in third person instead of manufacturing a quote and attributing it to a real, named person who never said those exact words. Apply the permission constraints exactly as given — if something is marked as not for publication, it does not appear in any form, including a lightly reworded paraphrase that would still let a reader identify the specific detail; if anonymization is required, apply it consistently throughout, not just in the first mention. Be honest about causality: if the interview notes describe the result as influenced by multiple factors — a team reorg, a pricing change, a new hire — alongside the product change, phrase the outcome as "moved after" or "coincided with," not as sole cause, unless the notes genuinely support a direct causal claim; a case study that overclaims causation is the fastest way to get challenged by a prospect's own team during a sales evaluation, and challenged claims damage the whole case study's credibility, not just the overstated line.

OUTPUT FORMAT
1. The case study, structured as problem, approach, result, and impact on the customer.
2. A quote log: every direct quote used, each one confirmed as present in the interview notes verbatim.
3. A note confirming every permission constraint was applied, and where.
4. One sentence stating how causality was framed and why that framing matches what the notes actually support.

Customize

Optional — swap in your own details for the highlighted parts above.

Why this works

Restricting direct quotes strictly to language actually present in the interview notes addresses a specific and serious risk unique to this format: a fabricated quote attributed to a real, named person is a factual misrepresentation of what that specific individual said, not just a stylistic embellishment — if the customer or their employer ever reads the published piece and finds a quote they don't recognize as their own words, it damages trust with that specific customer relationship in a way a generic marketing exaggeration wouldn't, which is a materially higher-stakes failure mode than the same problem in, say, a listicle or an opinion post. The honest-causality rule targets the credibility mechanism that makes case studies persuasive or not to a skeptical B2B buyer specifically: a prospect evaluating a purchase decision is actively looking for reasons to distrust a vendor's own case study, and an overclaimed causal link — crediting a single product change for a result the interview notes themselves describe as influenced by a reorg, a new hire, and a tool switch — is exactly the kind of claim a skeptical reader's own team will catch and use to discount the entire piece, whereas an honestly hedged "moved after" framing survives that scrutiny because it doesn't claim more than the underlying evidence supports. Applying permission constraints exactly, including to reworded paraphrases that could still re-identify a redacted detail, matters because the actual risk of an anonymization leak isn't the literal redacted term reappearing — it's a specific enough paraphrase that anyone who knows the customer can reconstruct exactly what was supposed to be hidden, which technically satisfies a rule read narrowly ("don't publish the revenue figure") while violating its actual intent (don't let a reader infer the revenue figure), so the rule has to be applied to the underlying information, not just its literal surface form. Leading with the quantified result rather than company background matters for the same reason a strong headline matters: a case study competing for a skeptical reader's limited attention needs to establish in the first few lines that reading further is worth it, and a number is the one thing in the entire piece a skimming reader can evaluate for relevance without reading any of the surrounding narrative first.

Verified against

Claude Claude Sonnet 5 · 2026-08-01

ChatGPT GPT-5.1 · 2026-08-06

Changelog

  • 2026-08-01 Initial publish, verified against Claude (Claude Sonnet 5) and ChatGPT (GPT-5.1).

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