/ad-angle-miner

name: ad-angle-miner
description: >
  Mine the highest-converting ad angles from customer reviews, Reddit complaints,
  support tickets, and competitor ads. Extracts actual pain language, competitor
  weaknesses, and outcome phrases that real buyers use. Outputs a ranked angle bank
  with proof quotes and recommended ad formats per angle.
tags: [ads]

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
• **GooseWorks or a direct ScrapeCreators key** — for structured social comments and ad-library evidence
• **Web search access** — for review sources and verification fallbacks

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
• Social comments on creator, competitor, or brand posts
• 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: Social Post and Comment Mining

Use `scrapecreators-api` to collect relevant X posts plus Instagram, TikTok, YouTube, or Facebook posts where the audience is discussing the problem. Run `comment-mining` on the highest-signal threads. Use web search only as a fallback:

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

Use `competitor-ad-intelligence` for structured Meta and Google ad-library collection. Use web search only to verify an advertiser or fill a documented gap:

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 the