Email flow teardown prompts

AI competitor lifecycle email calendar prompt

Turn competitor email captures into a lifecycle calendar with themes, offers, proof, and gaps.

This is a working analyst brief. Sources go in. Patterns, risks, and decisions come out.

Use this prompt

You are a lifecycle marketing analyst.

Build a competitor lifecycle calendar from the email evidence and identify repeatable themes.

My company:
{{my_company}}

Competitor:
{{competitor}}

Market:
{{market}}

Sources:
{{sources}}

Return:
1. Calendar table by date, theme, offer, and likely audience.
2. Repeated campaign types.
3. Seasonal or event-driven moves.
4. Proof and objection patterns.
5. Missing lifecycle moments we can own.
6. A 30-day email plan based on verified opportunities.

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.

Advanced AI techniques

Use these techniques for this prompt

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.

Source grounding

Source-grounded context pack

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.

Prompt structure

Delimited inputs

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.

Pattern analysis

Pattern clustering

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.

Output contract

Structured output contract

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.

Verification workflow

Verification loop

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.

Replace placeholders

Replace these variables before running the prompt

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

Expected shape

Compare a filled input with a realistic output shape

The output below is fictional. It shows the shape you are looking for, not a real competitor result.

Example input

my_company = FitBox
competitor = LiftCrate
market = fitness subscription box
sources = 42 emails, subject lines, send dates, offer notes

Fictional example output

Fictional example output:

Repeated themes: new routine, bundle value, member proof.
Gap: no onboarding education after purchase.
Plan idea: 4-email post-purchase education sequence before the next offer.

Prompt logic

Why this prompt works

It converts inbox noise into campaign patterns.
It tracks dates and themes together.
It turns competitor monitoring into a usable calendar.

Mistakes to avoid

Asking the AI to analyze a competitor with no sources.

Paste the page copy, ad screenshots, pricing table, SEO notes, or transcript first.

Treating the output as research truth.

Use it as a source-backed brief: keep strong evidence, downgrade weak evidence, and decide what deserves action.

Asking for generic strategy advice.

Ask for observations, risks, and next actions tied to the evidence.

Verification checklist

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.

Use the output safely

What you should do next

Export emails with dates.
Group by campaign theme.
Use the plan only after checking your own customer lifecycle.