PlaybookPrompts

Tear down competitor ads to find positioning gaps you can own

Marketing & SEO competitive-analysispaid-adspositioning

When planning a paid campaign, reviewing what competitors are already saying prevents you from blending in. This prompt systematically dissects competitor ad copy to surface the claims nobody is making — which is where you should compete.

Prompt
You are a competitive positioning analyst. I will give you a set of competitor ad copy samples. Your job is to find the white space.

1. Read all ad samples. For each ad, extract: Core promise (one phrase), Proof mechanism used (stat, testimonial, feature, etc.), Emotional trigger (fear, aspiration, urgency, trust), CTA type (trial, demo, buy, learn more).
2. Build a positioning map: list the claims that appear in 3 or more ads — these are table-stakes messages. List claims that appear in only 1 ad — these are differentiated bets.
3. Identify 2–3 angles that no competitor is claiming at all. These are the positioning gaps. For each gap, write one sentence explaining why it might be unclaimed (risky to prove, niche audience, etc.).
4. For each gap, write one test headline (under 35 characters) that {{MY_BRAND}} could use to own it.
5. Flag any competitor claim that appears legally aggressive or unsubstantiated — note it as a risk if we mirror it.

Edge cases: This analysis is only as good as the sample size. Fewer than 5 ads will produce unreliable pattern detection — gather more before running this prompt.

My brand name and core offer: {{MY_BRAND}}
Competitor ad copy samples (paste all, separated by '---'): {{COMPETITOR_ADS}}
Target platform (e.g., Google Search, Meta, LinkedIn): {{AD_PLATFORM}}
Variables to fill in
  • {{MY_BRAND}}
  • {{COMPETITOR_ADS}}
  • {{AD_PLATFORM}}

How to use this prompt

  1. Copy the prompt above (Copy button on the top-right).
  2. Replace each {{VAR}} with your own value. Variables: {{MY_BRAND}}{{COMPETITOR_ADS}}{{AD_PLATFORM}}.
  3. Paste it into one of the recommended tools below.
  4. Iterate: tighten constraints in the prompt if the output is generic.

Why this prompt is structured this way

The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.

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Pair this prompt with a tool

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