Ad Angle Miner
Dig through customer voice data — reviews, Reddit, support tickets, competitor ads — to extract the specific language, pain points, and outcome desires that make ads convert. The output is an angle bank your team can pull from for any campaign.
Core principle: The best ad angles aren't invented in a brainstorm. They're extracted from what real people are already saying. This skill finds those angles and ranks them by strength of evidence.
When to Use
- "What angles should we run in our ads?"
- "Find pain points we can use in ad copy"
- "What are people complaining about with [competitors]?"
- "Mine reviews for ad messaging"
- "I need fresh ad angles — not the same tired stuff"
Phase 0: Intake
- Your product — Name + what it does in one sentence
- Competitors — 2-5 competitor names (for review mining)
- ICP — Who are you targeting? (role, company stage, pain)
- Data sources to mine (pick all that apply):
- G2/Capterra/Trustpilot reviews (yours + competitors)
- Reddit threads in relevant subreddits
- Twitter/X complaints or praise
- Support tickets or NPS comments (paste or file)
- Competitor ads (Meta + Google)
- Any angles you've already tested? — So we can skip those
Phase 1: Source Collection
1A: Review Mining
Run review-scraper for your product and each competitor:
python3 skills/review-scraper/scripts/scrape_reviews.py \
--product "<product_name>" \
--platforms g2,capterra \
--output json
Focus on:
- 1-2 star reviews of competitors — Pain they're failing to solve
- 4-5 star reviews of you — Outcomes that delight buyers
- 4-5 star reviews of competitors — Strengths you need to counter or match
- Review language patterns — Exact phrases buyers use
1B: Reddit/Community Mining
Run reddit-scraper for relevant subreddits:
python3 skills/reddit-scraper/scripts/scrape_reddit.py \
--query "<product category> OR <competitor> OR <pain keyword>" \
--subreddits "<relevant_subreddits>" \
--sort relevance \
--time month \
--limit 50
Extract:
- Questions people ask before buying
- Complaints about current solutions
- "I wish [product] would..." statements
- Comparison threads (vs discussions)
1C: Twitter/X Mining
Run twitter-scraper:
python3 skills/twitter-scraper/scripts/scrape_twitter.py \
--query "<competitor> (frustrating OR broken OR hate OR love OR switched)" \
--max-results 50
1D: Competitor Ad Mining (Optional)
Run ad-creative-intelligence to see what angles competitors are currently using. This reveals:
- Angles they've validated (long-running ads = working)
- Angles they're testing (new ads)
- Angles nobody is running (white space)
1E: Internal Data (Optional)
If the user provides support tickets, NPS comments, or sales call transcripts — ingest and tag with the same framework below.
Phase 2: Angle Extraction
Process all collected data through this extraction framework:
Angle Categories
| Category | What to Look For | Ad Power |
|---|
| Pain angles | Specific frustrations with status quo or competitors | High — pain motivates action |
| Outcome angles | Desired results buyers describe in their own words | High — positive aspiration |
| Identity angles | How buyers describe themselves or want to be seen | Medium — emotional resonance |
| Fear angles | Risks of NOT switching or acting | Medium — loss aversion |
| Competitive displacement | Specific reasons people switched from a competitor | Very high — direct comparison |
| Social proof angles | Outcomes or metrics buyers cite in reviews | High — credibility |
| Contrast angles | Before/after or old way/new way framings | High — clear value prop |
For Each Angle, Extract:
- The angle — One-sentence framing
- Proof quotes — 2-5 verbatim quotes from sources
- Source count — How many independent sources mention this?
- Competitor weakness? — Does this exploit a specific competitor's gap?
- Emotional register — Frustration / Aspiration / Fear / Relief / Pride
- Recommended format — Search ad / Meta static / Meta video / LinkedIn / Twitter
Phase 3: Scoring & Ranking
Score each angle on:
| Factor | Weight | Description |
|---|
| Evidence strength | 30% | Number of independent sources mentioning it |
| Emotional intensity | 25% | How strongly people feel about this (language intensity) |
| Competitive differentiation | 20% | Does this set you apart, or could any competitor claim it? |
| ICP relevance | 15% | How closely does this match the target buyer's world? |
| Freshness | 10% | Is this angle already overused in competitor ads? |
Total score out of 100. Rank all angles.
Phase 4: Output Format
# Ad Angle Bank — [Product Name] — [DATE]
Sources mined: [list]
Total angles extracted: [N]
Top-tier angles (score 70+): [N]
---
## Tier 1: Highest-Conviction Angles (Score 70+)
### Angle 1: [One-sentence angle]
- **Category:** [Pain / Outcome / Identity / Fear / Displacement / Proof / Contrast]
- **Score:** [X/100]
- **Emotional register:** [Frustration / Aspiration / etc.]
- **Proof quotes:**
> "[Verbatim quote 1]" — [Source: G2 review / Reddit / etc.]
> "[Verbatim quote 2]" — [Source]
> "[Verbatim quote 3]" — [Source]
- **Source count:** [N] independent mentions
- **Competitor weakness exploited:** [Competitor name + specific gap, or "N/A"]
- **Recommended formats:** [Search ad headline / Meta static / Video hook / etc.]
- **Sample headline:** "[Draft headline using this angle]"
- **Sample body copy:** "[Draft 1-2 sentence body]"
### Angle 2: ...
---
## Tier 2: Worth Testing (Score 50-69)
[Same format, briefer]
---
## Tier 3: Emerging / Low-Evidence (Score < 50)
[Brief list — angles with potential but insufficient evidence]
---
## Competitive Angle Map
| Angle | Your Product | [Comp A] | [Comp B] | [Comp C] |
|-------|-------------|----------|----------|----------|
| [Angle 1] | Can claim ✓ | Weak here ✗ | Also claims | Not relevant |
| [Angle 2] | Strong ✓ | Strong | Weak ✗ | Not relevant |
...
---
## Recommended Test Plan
### Week 1-2: Test Tier 1 Angles
- [Angle] → [Format] → [Platform]
- [Angle] → [Format] → [Platform]
### Week 3-4: Test Tier 2 Angles
- [Angle] → [Format] → [Platform]
Save to clients/<client-name>/ads/angle-bank-[YYYY-MM-DD].md.
Cost
| Component | Cost |
|---|
| Review scraper (per product) | ~$0.10-0.30 (Apify) |
| Reddit scraper | ~$0.05-0.10 (Apify) |
| Twitter scraper | ~$0.10-0.20 (Apify) |
| Ad scraper (optional) | ~$0.40-1.00 (Apify) |
| Analysis | Free (LLM reasoning) |
| Total | ~$0.25-1.60 |
Tools Required
- Apify API token —
APIFY_API_TOKEN env var
- Upstream skills:
review-scraper, reddit-scraper, twitter-scraper
- Optional:
ad-creative-intelligence (for competitor ad angles)
Trigger Phrases
- "Mine ad angles from reviews"
- "What angles should we run?"
- "Find pain language for our ads"
- "Build an ad angle bank for [client]"
- "What are people complaining about with [competitor]?"
Claude
ChatGPT
OpenClaw