Email

Teardown competitor email flows

Analyze competitor email sequences by trigger, promise, offer, timing, proof, and conversion path.

Best use case

Use this prompt when the source set matches the job

Use this when you collected welcome flows, launch emails, nurture emails, abandoned cart emails, or lifecycle campaigns.

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

Teardown competitor email flows

You are my competitor intelligence operator.

Task: Turn competitor email flows into sequence insights and original email tests.

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

Available sources, exports, URLs, files, screenshots, notes, and tool outputs:
{{sources}}

- Reconstruct the sequence by trigger, timing, message, offer, proof, and CTA before recommending changes.
- 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.

Do the work:
1. Reconstruct the flow by trigger, timing, audience, message, CTA, and offer.
2. Identify the job of each email in the sequence.
3. Extract repeated claims, proof, objections, and urgency mechanics.
4. Compare the flow against our own audience and offer.
5. Recommend sequence changes we can test honestly.

Return:
- Flow map.
- Message hierarchy.
- Offer and CTA analysis.
- What to test.
- What not to copy.

Rules:
- Separate observed evidence, inferred signal, and recommended action.
- Put dates next to any recent or change-based claim.
- Cite URLs when you use web search or deep research.
- Name the tool, connector, MCP server, or export when you use one.
- Do not copy competitor creative. Translate the learning into our company context.
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 Competitors One competitor, a short competitor set, or a tracked category. 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: DTC coffee subscription
Competitors: 4 coffee brands
Sources: welcome emails, abandoned cart emails, launch sequence screenshots, landing page notes
Decision: improve our welcome and first-purchase sequence

What a useful answer should look like

Fictional example output

Flow insight:
Competitors use education before discounting. The best emails sell taste confidence first.

Test:
Email 2 should explain roast profile and first-order choice, not push a bigger discount.

Verification

Check whether the answer is useful

  • The output names the evidence behind each recommendation.
  • The output uses current sources, exports, or tool results where the task depends on fresh data.
  • The answer separates facts, estimates, and decisions.
  • The final next moves are specific enough to assign.

Mistakes

Mistakes that make this prompt weak

  • Asking for strategy before the source set is clear.
  • Mixing old screenshots with current claims without dates.
  • Copying competitor language instead of translating the insight.
  • Skipping verification because the answer sounds confident.

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