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MarketingCompetitor & Market Research#competitor#ads#teardown

Competitor Ad Teardown · Hook-Promise-Proof

Teardown 3-5 competitor ads/LPs into hook-promise-proof; see the mental model they target.

Pain addressed · Pain #2: marketers stare at competitor ads daily but have no reusable teardown framework — you 'learn by feel', which doesn't scale to the team.

Competitor Ad Teardown · Hook-Promise-Proof

Prompt·Advanced
Also available in 中文 →
You're a marketing strategist skilled at ad-psychology teardowns. I give you 3-5 competitor ads/LPs. Apply the Hook–Promise–Proof framework.

# Competitor ads to teardown
{paste_competitor_ads}

# My product + audience
{my_product_and_audience}

## Per ad {n}: {one-line ad subject}
- **Hook**: first attention-grabber (title/visual/contrast). Name the user-mind trigger (FOMO/time-saving/social proof/authority).
- **Promise**: what they promise (outcome/feeling/identity). Functional or emotional?
- **Proof**: how they make it credible (data/testimonials/KOL/comparison). Rate "promise believability" 1-5 stars if proof is thin.
- **User journey stage**: awareness / interest / evaluation / purchase / retention.
- **Borrow vs don't**: 1 borrowable angle + 1 trap to avoid.

# Overall insights
1. What user-mind assumption do these ads share?
2. What "white-space mind" did none of them hit (your product could)?
3. One differentiated angle for me + its Hook–Promise–Proof.

# Rules
- Don't defend competitors — score honestly
- Anchor the journey-stage in evidence (CTAs present)
- Only ONE borrow + ONE trap per ad — force the strongest signal
Saves ~90 min per use

Open directly on your favourite AI

One click copies the prompt and opens the platform in a new tab — just paste (⌘/Ctrl + V).

Workflow · 5 steps

  1. 1

    Pull 3-5 recent competitor ads from Meta Ads Library, Google Ads Transparency, TikTok Creative Center, or LinkedIn Ads. Screenshot + transcribe.

  2. 2

    Replace the two placeholders. For image ads, describe the visual in words too (color, model, scene) — the model can't see images.

  3. 3

    When the teardown comes back, focus on the proof star-rating: anything below 3 stars is a hook-only ad with nothing underneath — don't copy it.

  4. 4

    The 'white-space mind' insight is gold — capture it as the seed for your next brief, not as a one-off observation.

  5. 5

    Bring the 'differentiated angle for me' as the proposal in next week's marketing standup.

Verify before you ship

AI's "almost right" is the #1 pain. Tick these off so reviewers can't find the obvious holes. (Checkbox state stays local — we don't track it.)

Done 0 / 5 checks

Why this prompt works

This prompt isn't a generic “write me a Competitor & Market Research”. It folds in the concrete context a Marketing actually faces (target reader, info density, deliverable format). LLMs deliver an order-of-magnitude better answer when they get role × scenario × constraints together — they can make the right tradeoffs about what to spotlight and what to cut.

When to use

Best moment: you know roughly that you need to do a Competitor & Market Research, but you're stuck on “how do I even ask the AI?” Muscle memory has no template. Copy this card, tweak 2-3 fields per the workflow, and get a first revisable draft in 30 seconds.

Common pitfalls

  • Send without tweaking placeholders · the draft reads as default-GPT and your reviewer spots it in 3 seconds.
  • Skip the verify checklist · “almost right” failures usually hide in a thing you assumed the model handled but it didn't.
  • Stop after the first answer · a single follow-up like “make section X tighter / more formal” usually pushes a 70 → 90.

Variants & extensions

Want to use this card more aggressively? Three extensions:

  • Swap languages: both EN/ZH prompts ship in the card — use the EN version when shipping to a Western audience.
  • Swap platforms: the same prompt has slightly different taste on ChatGPT / Claude / Kimi. The recommended-platform pill marks the best fit.
  • Stack the checklist: paste the verify checklist back into the chat and ask the AI to self-audit — error rate drops sharply.
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