athina-ai/competitor-ad-teardown

Competitor Ad Teardown

Go deeper than surface-level ad monitoring. Take a single competitor and reverse-engineer their entire paid strategy: what they're running, where they're sending traffic, what they're testing, what's working, and where they're vulnerable.

Core principle: A competitor's ad portfolio is a window into their growth strategy. Long-running ads reveal what converts. New ads reveal what they're testing. Landing pages reveal their positioning bets. This skill reads all the signals.

When to Use

• "Tear down [competitor]'s ad strategy"
• "What's [competitor] spending their ad budget on?"
• "Reverse-engineer [competitor]'s paid funnel"
• "How is [competitor] positioning themselves in ads?"
• "Deep competitive ad analysis on [competitor]"

Phase 0: Intake

1. Competitor name + domain — Who are we tearing down?
2. Your product — For comparison framing
3. Channels — Meta, Google, or both? (default: both)
4. Depth level:
   • Standard: Ad scrape + landing page analysis
   • Deep: Standard + historical comparison + funnel reconstruction
5. Known competitor landing pages? — Any URLs you've seen in their ads

Phase 1: Ad Collection

1A: Meta Ad Library Scrape

python3 skills/meta-ad-scraper/scripts/scrape_meta_ads.py \
  --domain <competitor_domain> \
  --output json

For each ad, capture:

• Ad copy (headline + primary text)
• Visual type (image / video / carousel)
• CTA button
• Landing page URL
• Active duration (first seen → still running or stopped)
• Platforms (Facebook, Instagram, Audience Network)
• Ad variations (A/B tests — same landing page, different creative)

1B: Google Ads Transparency Scrape

python3 skills/google-ad-scraper/scripts/scrape_google_ads.py \
  --domain <competitor_domain> \
  --output json

For each ad:

• Headline variants
• Description lines
• Ad type (Search / Display / YouTube / Shopping)
• Landing page URL (from display URL)
• Geographic targeting (if visible)

Phase 2: Landing Page Analysis

For each unique landing page URL found in ads:

Fetch: [landing_page_url]

Extract:

• Hero headline — Does it match the ad promise?
• Subheadline — Value prop expansion
• Primary CTA — What action are they driving? (Demo / Free trial / Sign up / Download)
• Social proof — Logos, testimonials, case study metrics
• Pricing visibility — Is pricing shown or hidden?
• Form fields — How much info do they ask for?
• Page type — General homepage / dedicated LP / feature page / use-case page
• Message match score — How well does the LP deliver on the ad's promise? (1-10)

Phase 3: Strategic Analysis

3A: Campaign Clustering

Group all ads into logical campaigns by:

• Landing page destination — Ads pointing to the same URL = same campaign
• Messaging theme — Similar copy angles = same strategic bet
• Audience signal — Different copy for different personas

3B: Per-Campaign Analysis

For each campaign cluster:

| Dimension | Analysis | |-----------|----------| | Strategic intent | What is this campaign trying to achieve? (Awareness / Lead gen / Free trial / Competitive displacement) | | Target persona | Who is this ad speaking to? (Role, pain, stage) | | Positioning bet | What market position are they claiming? | | Hook strategy | Fear / Outcome / Social proof / Contrarian / Product-led | | Conversion path | Ad → LP → CTA → [Demo call / Free trial / Content download] | | Longevity signal | How long has this been running? (Longer = likely working) | | A/B tests detected | Multiple creatives to same LP = active testing |

3C: Budget Allocation Inference

Based on ad volume and platform distribution, estimate where they're concentrating spend:

| Platform | Ad Count | % of Total | Estimated Focus | |----------|----------|-----------|-----------------| | Meta (Facebook) | [N] | [X%] | [Awareness / Retargeting] | | Meta (Instagram) | [N] | [X%] | [Visual / younger audience] | | Google Search | [N] | [X%] | [Bottom-funnel capture] | | Google Display | [N] | [X%] | [Awareness / retargeting] | | YouTube | [N] | [X%] | [Education / awareness] |

3D: Historical Comparison (Deep Mode)

If Web Archive data exists for their landing pages:

• Has their positioning changed in the last 6-12 months?
• What campaigns did they retire? (Possible losers)
• What campaigns have they scaled up? (Possible winners)

3E: Vulnerability Analysis

Identify weaknesses in their ad strategy:

| Vulnerability Type | Description | |-------------------|-------------| | Message-LP mismatch | Ad promises one thing, LP delivers another | | Single-persona dependency | All ads target the same persona — missing segments | | Platform concentration | Heavy on one platform, absent from others | | No social proof | Ads or LPs lack credibility markers | | Weak CTA | Asking for too much too soon (demo before value) | | Generic positioning | Claims anyone could make — not differentiated | | Stale creative | Same ads running unchanged for months — fatigue risk |

Phase 4: Output Format

# Competitor Ad Teardown: [Competitor Name] — [DATE]

Domain: [competitor.com]
Channels analyzed: [Meta, Google]
Total ads found: [N] (Meta: [N], Google: [N])
Unique landing pages: [N]
Estimated active campaigns: [N]

---

## Executive Summary

[3-5 sentence summary: What is this competitor doing with paid ads? What's working? Where are they vulnerable?]

---

## Campaign Breakdown

### Campaign 1: [Inferred Campaign Name]
- **Ads in cluster:** [N]
- **Platform(s):** [Meta / Google / Both]
- **Strategic intent:** [Awareness / Lead gen / Competitive displacement / etc.]
- **Target persona:** [Description]
- **Hook strategy:** [Type]
- **Landing page:** [URL]
  - Hero: "[Headline text]"
  - CTA: "[Button text]"
  - Message match: [Score/10]
- **Longevity:** [First seen date → status]
- **A/B tests detected:** [Yes/No — what they're testing]

**Sample ad:**
> **Headline:** [text]
> **Body:** [text]
> **CTA:** [button]
> **Format:** [Image/Video/Carousel]

**Assessment:** [1-2 sentences — is this working? Why/why not?]

### Campaign 2: ...

---

## Funnel Map

[Ad: Hook/Angle] → [LP: /landing-page-url] → [CTA: Book Demo] ↓ [Ad: Different angle] → [LP: /same-or-different] → [CTA: Free Trial]

---

## Budget Allocation Estimate

| Platform | Share | Focus Area |
|----------|-------|-----------|
| [Platform] | [X%] | [Intent] |

---

## What's Working (Long-Running Ads)

| Ad | Platform | Running Since | Why It Likely Works |
|----|----------|--------------|-------------------|
| [Headline excerpt] | [Platform] | [Date] | [Analysis] |

---

## Vulnerability Report

### 1. [Vulnerability]
**Evidence:** [What we observed]
**Your opportunity:** [How to exploit this gap]

### 2. ...

---

## Recommended Counter-Plays

### Counter-Play 1: [Name]
- **Target their weakness:** [Which vulnerability]
- **Your ad angle:** [Hook]
- **Platform:** [Where to run]
- **LP strategy:** [What your landing page should emphasize]

### Counter-Play 2: ...

Save to clients/<client-name>/ads/competitor-teardown-[competitor]-[YYYY-MM-DD].md.

Cost

| Component | Cost | |-----------|------| | Meta ad scraper | ~$0.20-0.50 (Apify) | | Google ad scraper | ~$0.20-0.50 (Apify) | | Landing page fetching | Free | | Web Archive lookup (deep mode) | Free | | Analysis | Free (LLM reasoning) | | Total | ~$0.40-1.00 |

Tools Required

• Apify API token — APIFY_API_TOKEN env var
• Upstream skills: meta-ad-scraper, google-ad-scraper
• fetch_webpage — for landing page analysis

Trigger Phrases

• "Tear down [competitor]'s ads"
• "What's [competitor] running on Meta/Google?"
• "Reverse-engineer [competitor]'s paid funnel"
• "Deep ad analysis on [competitor]"
• "Find weaknesses in [competitor]'s ad strategy"