Messaging

Break competitor messaging into claims, proof, and tests

Break down competitor messaging by audience words, claims, proof, objections, and repeated phrases.

Best use case

Use this prompt when the source set matches the job

Use this when you have homepage copy, ads, emails, landing pages, or sales page snippets.

Before you paste

Give the prompt sources, tools, dates, and a decision

  • Paste raw notes with labels like homepage, pricing page, ad copy, SERP notes, offer page, export, screenshot, or review set.
  • Add the date you checked anything that can change, especially ads, prices, search results, AI answers, and website pages.
  • Tell AI which tools it can use: web search, deep research, files, code, browser, MCP, Semrush, Ahrefs, Similarweb, Panoramata, Sheets, or your own workspace.
  • Tell AI what decision the answer should support, so it gives you a useful recommendation instead of a generic summary.

Modern AI workflow

Use the prompt with current AI tools, not only a blank chat box

  • Use deep research or web search for current public evidence, then cite the URLs and date checked.
  • Use file or data analysis for exports, screenshots, CSVs, and historical logs. Do not summarize rows by instinct.
  • Use MCP/connectors when available so the AI can query Semrush, Ahrefs, Similarweb, Panoramata, Sheets, CRM, or your own files directly.
  • Use agent mode for multi-step research: collect, extract, compare, verify, then write.
  • Use artifacts, Canvas, tables, or charts when the output is a map, report, dashboard, or campaign plan.

Prompt

Break competitor messaging into claims, proof, and tests

You are helping me tear down competitor messaging.

My company: {{my_company}}
Competitor: {{competitor}}
Category: {{category}}
Decision I need to support: {{decision}}

Messaging sources:
{{sources}}

Analyze:
1. The words the competitor repeats.
2. The customer problem they make most visible.
3. The promise and proof behind it.
4. The objections they handle.
5. The claims that need verification.
6. The messaging gaps I could use.

Then rewrite the insight as:
- What they are really saying.
- Why it might work.
- Where it is weak.
- What I should test next.

Keep the answer direct. No fluffy marketing language.

- Extract repeated words, claims, proof, objections, and customer language before writing message tests.
- Use any provided URLs, files, screenshots, exports, or connected tool outputs before analyzing.
- Cite the source, export, tool, or URL behind any claim that affects the decision.
Open in ChatGPT Open in Claude Open in Gemini

Edit the prompt first if needed. ChatGPT and Claude open prefilled; Gemini opens with the prompt copied.

Variables

Replace these fields before you run the prompt

Variable What it means Example
{{my_company}} Required My company The company, product, store, or service you are comparing against the competitor. A DTC skincare brand selling refillable face wash
{{competitor}} Required Competitor The competitor you want to analyze. Use one competitor at a time when the source set is deep. Brand X
{{category}} Required Market or category The buying context. This helps the AI avoid comparing the wrong kind of business. Premium skincare, France and UK
{{sources}} Required Sources and retrieval targets Paste collected sources, exports, screenshots, notes, URLs to check, or the MCP/tool datasets the AI should use. Homepage copy, pricing page, top 5 ads, title tags, Semrush export, Ahrefs export, Similarweb notes, Panoramata campaign examples
{{decision}} Required Decision to support The action you need to take after the analysis. Rewrite our landing page hero and offer comparison table

Example

Use this example to match the right level of detail

Source notes you paste into AI

My company: no-code onboarding tool
Competitor: fictional tool called FlowStart
Category: SaaS onboarding
Sources: homepage hero, 3 feature sections, 5 ad headlines
Decision: refine our homepage message

What a useful answer should look like

Fictional example output

What they are really saying:
"You can launch onboarding without waiting for engineering."

Why it might work:
It speaks to marketing and product teams blocked by dev queues.

Weak spot:
They do not explain how the onboarding stays consistent with product events.

Test:
Lead with speed, but prove data reliability earlier.

Verification

Check whether the answer is useful

  • Repeated words are quoted from the source notes.
  • Claims and proof are separated.
  • The output gives testable message angles.
  • Weak spots are not stretched into fake competitor failures.
  • Current claims include URLs, dates checked, and source confidence.
  • Tool outputs, exports, and AI-generated inferences are clearly separated.
  • The answer uses tables, charts, artifacts, or a report structure when that makes the decision easier.

Mistakes

Mistakes that make this prompt weak

  • Treating nice copy as strong copy without checking proof.
  • Summarizing tone but missing the offer.
  • Writing your message as the opposite of theirs without a reason.
  • Using the prompt like a chat-only summary when modern AI could search, analyze files, run tools, or schedule follow-ups.
  • Letting the AI create a polished answer without showing the evidence trail.

Source notes

Use AI to collect data, then make it show the evidence

A good AI workflow can search, inspect pages, analyze exports, call MCP tools, compare screenshots, and build tables. Make it show URLs, dates, exports, screenshots, or connector results behind the answer before you trust the recommendation.

What you should do next

Run it once, then verify the useful parts

Replace the fields, paste a labeled source set, run the prompt, and check the answer before using it in a strategy report.

Use next