Map competitor positioning with evidence from website copy, product pages, ads, and public claims.
This is a working analyst brief. Sources go in. Patterns, risks, and decisions come out.
You are a positioning analyst.
Build a positioning map for {{market}}.
Companies:
{{sources}}
My company:
{{my_company}}
Return:
1. The positioning axis you chose and why.
2. Where each competitor sits on the map.
3. Evidence for each placement.
4. White space that is actually credible for us.
5. White space that sounds attractive but is weak.
6. Messaging implications for our homepage.
7. Questions we need to verify with customers or sales calls.
Rules:
- Do not create a map from vibes only.
- Use visible claims, repeated copy, pricing, product focus, and audience clues.
- Mark weak evidence clearly.
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.
- Pattern clustering: Cluster repeated signals before interpreting them. Label one-off examples as one-offs and do not treat them as strategy.
- 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 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 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 |
|---|---|---|---|
{{market}} | The category you want to map | string | AI meeting notes for sales teams |
{{sources}} | Company names plus copied claims, URLs, or notes | list | Four competitors with homepage copy |
{{my_company}} | Your own company and current positioning | text | CallKit - meeting notes for account managers |
The output below is fictional. It shows the shape you are looking for, not a real competitor result.
market = AI meeting notes for sales teams
sources = homepage copy from four competitors
my_company = CallKit, focused on account managers and renewal callsFictional example output:
Chosen axes:
- Horizontal: individual productivity to team revenue workflow.
- Vertical: generic meeting notes to sales-specific intelligence.
Credible white space:
- Renewal-call intelligence for account managers.
Weak white space:
- "Most accurate AI notes" is crowded and needs proof we do not have.
Homepage implication:
- Lead with account expansion and renewal risk, not generic transcription.It asks the AI to justify the map axes.
It separates credible white space from fantasy white space.
It turns the map into messaging decisions.
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.
Run this after collecting repeated claims from each competitor.
Reject any position that needs proof you do not have.
Test the clearest position in homepage copy or outbound copy.