Analyze a competitor offer by promise, package, incentive, guarantee, urgency, and friction.
This is a working analyst brief. Sources go in. Patterns, risks, and decisions come out.
You are an offer analyst.
Analyze {{competitor}}'s offer in {{market}}.
Sources:
{{sources}}
My company:
{{my_company}}
Return:
1. What is being offered.
2. Who it seems built for.
3. The core promise.
4. The package or bundle structure.
5. Incentives, guarantees, trials, or discounts.
6. Friction before purchase or signup.
7. Proof used to reduce risk.
8. What makes the offer easier or harder to say yes to.
9. Three ethical offer tests we could run.
Rules:
- Do not invent conversion data.
- Do not recommend fake urgency or fake scarcity.
- Flag unsupported claims.
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.
- Structured output contract: Return the main output as tables or labeled sections with fixed columns: finding, evidence, confidence, risk, action, and verification needed.
- Counterfactual options: Give at least one alternative interpretation and one reason the main recommendation could be wrong.
- Evidence rubric: Score each important finding by evidence strength, relevance, business impact, and reversibility before recommending an action.
- 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 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 when the output recommends positioning, offer, creative, content, or product moves.
Prompt move: Give at least one alternative interpretation and one reason the main recommendation could be wrong.
Skip when: Skip for factual extraction or source verification.
Use when: Use when recommendations could change strategy, positioning, pricing, ads, or product priorities.
Prompt move: Score each important finding by evidence strength, relevance, business impact, and reversibility before recommending an action.
Skip when: Skip for prompts that only organize notes without recommending action.
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 = DeskPlants
competitor = PlantBox
market = office plant subscriptions
sources = offer page, pricing notes, checkout screenshot, two adsFictional example output:
Offer:
Monthly office plant refresh with care included.
Why it is easy to say yes:
- Clear plan names.
- Replacement guarantee.
- Setup fee explained before checkout.
Friction:
- Delivery area is buried.
- Cancellation terms are hard to find.
Tests:
1. Show delivery area earlier.
2. Add care guarantee near price.
3. Compare subscription vs one-time plant replacement cost.It studies the buying path, not just the copy.
It removes shady urgency from the recommendation set.
It turns offer research into testable changes.
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.
Screenshot the whole offer path before running this.
Mark which incentives are public and current.
Choose one offer test, not five.