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MarketingProduct Marketing Managerintermediate 120 min saved

Evidence-Based Marketing Messaging Auditor

Audit your marketing copy against real customer data to eliminate unsupported claims and surface high-converting language.

Customise & copy

Add the context you have. The prompt updates as you work.

Paste your website copy, ads, or pitch deck here.

Paste recent customer interview notes or transcripts.

Paste sales call notes or objection logs.

Paste customer support ticket summaries or feedback logs.

Paste public product reviews or survey responses.

Live prompt

5 fields
You are an expert product marketing strategist and customer research analyst. Audit the provided marketing messaging against the raw audience evidence to identify unsupported claims, surface missing customer language, and recommend evidence-backed messaging improvements.

### Input Data
Current Marketing Messaging:
{{current_messaging}}

Customer Interview Transcripts:
{{interview_transcripts}}

Sales Call Notes & Objection Logs:
{{sales_notes}}

Customer Support Tickets & Feedback:
{{support_tickets}}

Public Product Reviews & Surveys:
{{public_reviews}}

### Instructions
Analyze all supplied audience evidence against the current marketing messaging. Strictly separate observed customer quotes from your interpretations as a marketer. Do not invent customer testimonials or fabricate supporting data.

Deliver your output using the following exact structure:

1. Claim Audit Matrix
Classify every major claim from the current messaging into one of four categories:
- Strongly Supported
- Weakly Supported
- Contradicted
- Unsupported
Provide the exact current claim and reference the specific evidence backing your classification.

2. Missing Customer Language & Pain Points
List frequent customer pain points and exact verbatim customer language found in the evidence that are currently absent from the marketing copy.

3. Evidence-Backed Messaging Recommendations
Provide actionable recommendations for messaging changes. Every recommended change must be explicitly tied back to at least one supplied piece of audience evidence, keeping observed customer language strictly distinct from marketer interpretation.

Proof before you use it

A real example

Tested on gemini-3.1-flash-lite on 2026-09-06

{
  "sales_notes": "Prospects constantly ask if we support custom Kubernetes clusters. Current pitch does not mention Kubernetes, causing 3-week delays in closing.",
  "public_reviews": "G2 Review: 'Great cost savings once configured, but zero downtime is an exaggeration; expect a weekend maintenance window.'",
  "support_tickets": "Ticket #402: 'Migration tool crashed when handling more than 500 nodes.' Ticket #411: 'Documentation for multi-cloud setup is outdated.'",
  "current_messaging": "Our enterprise SaaS platform reduces cloud infrastructure costs by 50% in under 30 days with zero engineering downtime and automated multi-cloud provisioning.",
  "interview_transcripts": "CFO at Fintech Corp: 'We saved about 35% after three months, not 50% right away. The hardest part was getting our DevOps team to trust the automated scripts.'"
}

A small ritual that works

How to use it

  1. 01Paste your current marketing copy into the messaging variable.
  2. 02Upload your raw customer data (interviews, sales notes, support tickets, and reviews) into the respective fields.
  3. 03Run the prompt to generate the Claim Audit Matrix and identify missing customer language.
  4. 04Apply the evidence-backed recommendations to your copy to align messaging with actual customer reality.

Why it works

The prompt enforces a rigorous, evidence-backed framework for auditing marketing messaging against raw qualitative customer data. By requiring a structured claim matrix, strict separation of verbatim customer language from marketer interpretation, and explicit traceability back to supplied transcripts, sales notes, and support tickets, it eliminates vanity metrics, unsubstantiated copywriting, and unsupported hypotheses.

Where it fails

INSUFFICIENT_EVIDENCE

Customisation tips

To adapt this prompt for your own pipeline, ensure your input variables are fed with high-density raw data (e.g., unedited customer call transcripts and verbatim ticket text) rather than sanitized summaries, as precise customer phrasing is critical for the language audit section.

Watch-outs

  • Do not allow marketers to treat single-source feedback or isolated complaints as universal truths or frequent pain points.
  • Ensure that recommended messaging changes do not invent unverified product capabilities, timelines, or guarantees that are absent from the underlying customer evidence.
  • Strictly prohibit the inclusion of inferred or paraphrased statements in the verbatim customer language section; only actual direct quotes from the source text are valid.

Works in

ChatGPTClaudeGemini

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