Teardown competitor welcome emails by promise, sequence logic, proof, offer, and next click.
This is a working analyst brief. Sources go in. Patterns, risks, and decisions come out.
You are a lifecycle email strategist.
Analyze the competitor welcome flow and explain how each message moves the reader toward the next action.
My company:
{{my_company}}
Competitor:
{{competitor}}
Market:
{{market}}
Sources:
{{sources}}
Return:
1. Flow map with email number, timing, subject, and goal.
2. First promise and how it changes across the flow.
3. Proof, objection handling, and offer placement.
4. Links and next-click intent.
5. What we should learn, ignore, and test.
6. A cleaner welcome flow outline for our company.
Rules:
- Use only the sources I provide.
- Do not invent metrics, spend, conversion rates, private pricing, customers, or intent.
- Mark unsupported claims as [UNVERIFIED].
- Separate observation, interpretation, and recommendation.
Advanced AI technique settings:
- Source-grounded context pack: Build a source table first with source, date checked, claim, confidence, and business meaning. Use only that table for the final recommendations.
- Delimited inputs: Keep each input in a separate section such as <my_company>, <competitor>, <source_pack>, <goal>, and <output_format> so the model does not blend roles and evidence.
- Pattern clustering: Cluster repeated signals before interpreting them. Label one-off examples as one-offs and do not treat them as strategy.
- Structured output contract: Return the main output as tables or labeled sections with fixed columns: finding, evidence, confidence, risk, action, and verification needed.
- Verification loop: After the first draft, run a verification pass that lists unsupported claims, stale details, missing sources, and recommendations to downgrade or remove.Copy the prompt. Fill the variables. Then check the output for real.
These are selected for this specific competitor research job. Use the prompt-ready instruction when it helps, and skip it when the condition does not fit.
Use when: Use when the answer depends on competitor pages, screenshots, ads, pricing, SEO exports, or reviews.
Prompt move: Build a source table first with source, date checked, claim, confidence, and business meaning. Use only that table for the final recommendations.
Skip when: Skip only for brainstorming with no factual claims.
Use when: Use when mixing company context, competitor evidence, goals, examples, and output requirements.
Prompt move: Keep each input in a separate section such as <my_company>, <competitor>, <source_pack>, <goal>, and <output_format> so the model does not blend roles and evidence.
Skip when: Skip for very short single-source prompts.
Use when: Use for batches of ads, emails, social posts, reviews, SEO pages, or competitor claims.
Prompt move: Cluster repeated signals before interpreting them. Label one-off examples as one-offs and do not treat them as strategy.
Skip when: Skip for a single landing page or one pricing table.
Use when: Use when the output must be compared, reviewed, or turned into tasks.
Prompt move: Return the main output as tables or labeled sections with fixed columns: finding, evidence, confidence, risk, action, and verification needed.
Skip when: Skip when the desired output is narrative copy.
Use when: Use before sharing research with a client, team, sales deck, ad brief, or website backlog.
Prompt move: After the first draft, run a verification pass that lists unsupported claims, stale details, missing sources, and recommendations to downgrade or remove.
Skip when: Skip only for private rough notes.
| Variable | Meaning | Type | Example |
|---|---|---|---|
{{my_company}} | Your company, product, or brand | string | Northstar CRM |
{{competitor}} | The competitor you want to analyze | string | Acme CRM |
{{market}} | The category or market context | string | B2B CRM for agencies |
{{sources}} | URLs, screenshots, notes, exports, or pasted copy | list | Homepage URL, pricing URL, ad screenshots |
The output below is fictional. It shows the shape you are looking for, not a real competitor result.
my_company = TrailDesk
competitor = CampFlow
market = outdoor trip planning app
sources = 5 welcome emails, subject lines, send times, linked pagesFictional example output:
Flow logic: email 1 confirms the trip-planning promise, email 2 shows saved time, email 3 introduces social proof.
Weakness: the trial CTA appears before the product value is clear.
Test: move our proof email before the upgrade ask.It reads emails as a sequence, not isolated copy.
It captures timing and links, not only subject lines.
It turns the teardown into a usable flow outline.
Paste the page copy, ad screenshots, pricing table, SEO notes, or transcript first.
Use it as a source-backed brief: keep strong evidence, downgrade weak evidence, and decide what deserves action.
Ask for observations, risks, and next actions tied to the evidence.
Every factual claim has a source or is marked as unverified.
Pricing, dates, and product claims were checked on the original source.
The output separates observation from interpretation.
The output gives actions you can reject, edit, or test.
Nothing is treated as final just because an AI tool wrote it.
Save timing and links with each email.
Mark claims that need proof.
Build one test for your own welcome flow.