| name | ad-creative-intelligence |
| description | Scrape competitor ads from Meta and Google ad libraries, cluster by hook/angle/format, and surface new creative directions your team hasn't tested. Chains meta-ad-scraper and google-ad-scraper. Use when a marketing team wants to understand the competitive ad landscape before launching a campaign, or wants fresh creative inspiration from what's already working in the market.
|
| tags | ["ads"] |
Ad Creative Intelligence
Scrape competitor ads from Meta and Google, then analyze creative patterns — hooks, formats, angles, CTAs — to find directions you haven't tried and identify what's resonating in the market.
Core principle: The best ad creative teams don't guess. They start with evidence from what's already working, then differentiate.
When to Use
- "What ads are my competitors running?"
- "Find new creative angles for our paid campaigns"
- "What hooks are working in [our space]?"
- "I want to audit the ad landscape before we launch"
- "What format — video, image, carousel — is dominant in our category?"
Phase 0: Intake
- Competitor names and website domains (e.g.,
apollo.io, clay.run)
- Your own domain (to optionally include for self-audit comparison)
- Channels to cover: Meta only, Google only, or both? (default: both)
- Time range: active ads only (default) or historical?
- What's your product category? (helps with analysis framing)
Phase 1: Scrape Meta Ads
Run meta-ad-scraper for each competitor:
python3 skills/meta-ad-scraper/scripts/scrape_meta_ads.py \
--domain <competitor_domain> \
--output json
Collect per ad:
- Ad copy (headline + body text)
- Visual description / media type (image, video, carousel)
- CTA button text
- Landing page URL
- Estimated run time (active duration proxy)
- Platforms (Facebook, Instagram, etc.)
Phase 2: Scrape Google Ads
Run google-ad-scraper for each competitor:
python3 skills/google-ad-scraper/scripts/scrape_google_ads.py \
--domain <competitor_domain> \
--output json
Collect per ad:
- Headline variants (up to 3)
- Description lines
- Ad type (search, display, YouTube)
- Landing page URL
Phase 3: Cluster & Analyze
After collecting all ads, perform structured analysis:
Hook Pattern Clustering
Group all ad headlines/openers by hook type:
| Hook Type | Pattern | Example |
|---|
| Fear/Loss | Risk of missing out or falling behind | "Your competitors are already using AI SDRs" |
| Outcome | Direct result promise | "10x your pipeline in 30 days" |
| Question | Challenges current assumption | "Still doing outbound manually?" |
| Social proof | Names customers or numbers | "Join 500+ B2B teams using [product]" |
| Contrarian | Challenges conventional wisdom | "Cold email isn't dead. Your copy is." |
| Empathy | Validates their pain | "We know SDR ramp time is brutal" |
| Product-led | Feature as hook | "[Feature] is live — see what's new" |
Count how many ads per competitor use each hook type. This reveals their primary messaging strategy.
Format Distribution
| Format | Meta | Google |
|---|
| Static image | [N] | N/A |
| Video | [N] | [N] |
| Carousel | [N] | N/A |
| Search text | N/A | [N] |
| Display banner | N/A | [N] |
CTA Taxonomy
List all unique CTAs found. Common patterns:
- Urgency: "Start free", "Try now", "Get started today"
- Low-friction: "See how it works", "Watch demo", "Learn more"
- Outcome: "Book a demo", "Get your free audit", "Calculate your ROI"
Landing Page Analysis
For top-traffic ads (those running longest), note:
- What angle does the landing page reinforce?
- Is the LP general or campaign-specific?
- What's the primary conversion goal?
Phase 4: Gap Analysis & Recommendations
Identify:
- Angles nobody is running — Hook types absent from competitor ads = white space
- Overcrowded angles — If everyone leads with "save time", avoid it or be more specific
- Format opportunities — If no one is running video in your space, it may stand out
- Underutilized proof — Are competitors avoiding specific proof points you could own?
- CTA patterns to test — What CTAs do the longest-running ads use?
Phase 5: Output Format
# Ad Creative Intelligence Report — [DATE]
## Coverage
- Competitors analyzed: [list]
- Meta ads collected: [N]
- Google ads collected: [N]
---
## Meta Ad Analysis
### Hook Distribution
| Hook Type | [Comp1] | [Comp2] | [Comp3] |
|-----------|---------|---------|---------|
| Fear/Loss | 40% | 10% | 0% |
| Outcome | 30% | 50% | 60% |
...
### Top Performing Ads (Longest Running)
**[Competitor] — [Ad Title/Hook]**
> [Ad copy excerpt]
- Format: [type]
- CTA: [text]
- Running since: [date]
- Why it likely works: [analysis]
---
## Google Ad Analysis
### Headline Patterns
[Top headline structures with examples]
### Most Common CTAs
[ranked list]
---
## Creative Gap Analysis
### Angles Nobody Is Running
1. [Angle] — Why it could work for you: [reasoning]
2. [Angle] — ...
### Overcrowded Angles (Avoid or Differentiate)
- [Angle] — [N] of [N] competitors use this
### Format White Space
- [Format] is not being used by competitors on [platform]
---
## Recommended Creative Experiments
### Experiment 1: [Name]
- Platform: [Meta/Google]
- Format: [type]
- Hook angle: [type]
- Proposed headline: "[headline]"
- Proposed body: "[copy]"
- Why test this: [rationale]
### Experiment 2: ...
Save to clients/<client-name>/intelligence/ad-creative-intel-[YYYY-MM-DD].md.
Cost
| Component | Cost |
|---|
| Meta ad scraper (per competitor) | ~$0.20-0.50 (Apify) |
| Google ad scraper (per competitor) | ~$0.20-0.50 (Apify) |
| Total for 3 competitors | ~$1-3 |
Tools Required
- Apify API token —
APIFY_API_TOKEN env var
- Upstream skills:
meta-ad-scraper, google-ad-scraper
Trigger Phrases
- "What ads are [competitor] running?"
- "Audit the ad landscape for [product category]"
- "Run ad creative intel for [client]"
- "Find new paid ad angles we haven't tried"
