ad-angle-miner

# 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" ## Prerequisites - **Environment variable:** `APIFY_API_TOKEN` — required for review scraping and Reddit scraping - **Web search access** — your AI agent must support `web_search` or equivalent for Twitter/X and competitor ad lookups ## Phase 0: Intake 1. **Your product** — Name + what it does in one sentence 2. **Competitors** — 2-5 competitor names (for review mining) 3. **ICP** — Who are you targeting? (role, company stage, pain) 4. **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) 5. **Any angles you've already tested?** — So we can skip those ## Phase 1: Source Collection ### 1A: Review Mining (Apify) Use the Apify Amazon Reviews Scraper (or web_search for G2/Capterra/TrustRadius reviews). **Option 1: Amazon product reviews via Apify** Start a run of the `web_wanderer/amazon-reviews-extractor` actor: ``` POST https://api.apify.com/v2/acts/web_wanderer~amazon-reviews-extractor/runs?token=$APIFY_API_TOKEN Content-Type: application/json { "products": [ "https://www.amazon.com/dp/PRODUCT_ASIN" ], "maxReviews": 100 } ``` Poll until the run finishes: ``` GET https://api.apify.com/v2/acts/web_wanderer~amazon-reviews-extractor/runs/{RUN_ID}?token=$APIFY_API_TOKEN ``` When `status` is `SUCCEEDED`, fetch results: ``` GET https://api.apify.com/v2/datasets/{DATASET_ID}/items?token=$APIFY_API_TOKEN ``` **Output fields:** Each review has `rating` (1-5), `reviewTitle`, `reviewText`, `reviewDate`, `verifiedPurchase` (bool), `productAsin`, `productTitle`, `helpfulVoteCount`. **Option 2: G2/Capterra/TrustRadius reviews via web_search** For B2B products, run web searches to find review content: ``` web_search: "<product_name> reviews site:g2.com" web_search: "<product_name> reviews site:capterra.com" web_search: "<product_name> reviews site:trustradius.com" web_search: "<competitor_name> reviews site:g2.com" ``` Focus on: - **1-2 star reviews of competitors** — Pain they're failing to solve - **4-5 star reviews of you** — Outcomes that delight buyers - **4-5 star reviews of competitors** — Strengths you need to counter or match - **Review language patterns** — Exact phrases buyers use ### 1B: Reddit/Community Mining (Apify) Use the `trudax/reddit-scraper-lite` actor to search Reddit for relevant threads: **Search by keyword:** ``` POST https://api.apify.com/v2/acts/trudax~reddit-scraper-lite/runs?token=$APIFY_API_TOKEN Content-Type: application/json { "searches": [ "<product category> OR <competitor> OR <pain keyword>" ], "maxItems": 50 } ``` **Browse a specific subreddit:** ``` POST https://api.apify.com/v2/acts/trudax~reddit-scraper-lite/runs?token=$APIFY_API_TOKEN Content-Type: application/json { "startUrls": [ {"url": "https://www.reddit.com/r/SUBREDDIT_NAME/hot/"} ], "maxItems": 50 } ``` Poll until complete: ``` GET https://api.apify.com/v2/acts/trudax~reddit-scraper-lite/runs/{RUN_ID}?token=$APIFY_API_TOKEN ``` Fetch results when `status` is `SUCCEEDED`: ``` GET https://api.apify.com/v2/datasets/{DATASET_ID}/items?token=$APIFY_API_TOKEN ``` **Output fields:** Each item has `dataType` ("post" or "comment"), `title` (posts only), `body`, `communityName`, `upVotes`, `numberOfComments` (posts), `url`, `createdAt`. Extract: - Questions people ask before buying - Complaints about current solutions - "I wish [product] would..." statements - Comparison threads (vs discussions) ### 1C: Twitter/X Mining (web_search) Use web_search to find relevant Twitter/X posts — no scraper or credentials needed: ``` web_search: "<competitor> (frustrating OR broken OR hate) site:x.com" web_search: "<competitor> (love OR switched to OR replaced) site:x.com" web_search: "<product category> (recommendation OR alternative OR looking for) site:twitter.com" web_search: "<competitor> site:x.com" (for general sentiment) ``` Run 3-5 queries covering: - Competitor complaints and frustrations - Product category praise / switching stories - "What do you use for X?" buying-intent threads ### 1D: Competitor Ad Mining (web_search) Use web_search to check the Meta Ad Library for competitor ad creatives — no separate tool needed: ``` web_search: "<competitor_name> site:facebook.com/ads/library" web_search: "<competitor_name> facebook ads library" web_search: "<competitor_name> ad creative examples" ``` 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: 1. **The angle** — One-sentence framing 2. **Proof quotes** — 2-5 verbatim quotes from sources 3. **Source count** — How many independent sources mention this? 4. **Competitor weakness?** — Does this exploit a specific competitor's gap? 5. **Emotional register** — Frustration / Aspiration / Fear / Relief / Pride 6. **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 ```markdown # 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 `angle-bank-[YYYY-MM-DD].md` in the current working directory (or user-specified path). ## Cost | Component | Cost | |-----------|------| | Amazon review scraper (per product) | ~$0.10-0.30 (Apify) | | Reddit scraper | ~$0.05-0.10 (Apify) | | Twitter/X (web_search) | Free | | Competitor ads (web_search) | Free | | G2/Capterra reviews (web_search) | Free | | Analysis | Free (LLM reasoning) | | **Total** | **~$0.15-0.40** | ## Tools Required - **Environment variable:** `APIFY_API_TOKEN` — for Apify actors (review scraper, Reddit scraper) - **Web search** — built into your AI agent (for Twitter/X, competitor ads, G2/Capterra reviews) - No third-party libraries needed. All data collection uses HTTP APIs (`requests` or equivalent) and web_search. ## 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]?"