Meta Ads Intelligence Analyzer

Turn supplied Meta/Facebook Ads Library-style records into buyer-ready competitor intelligence for agencies, e-commerce teams, and local-business marketers.

This Apify Actor does not scrape Meta directly in v1. Instead, it analyzes ad records you already have from compliant exports, datasets, or internal workflows and returns structured insights on hooks, offer angles, CTAs, competitor positioning, and practical recommendations.

Why buyers use it

Marketing teams do not just need raw ad rows. They need to know what competitors are saying, what offers are repeated, what CTAs dominate, and where there is whitespace for a stronger campaign.

Use this Actor to:

• Audit competitor ad messaging before launching a campaign.
• Build client-ready creative intelligence reports.
• Extract offer angles, hooks, CTAs, pain points, value props, and positioning.
• Compare advertisers inside a market or local service category.
• Create repeatable weekly competitor watch reports without using private cookies or brittle scraping.

Input

The Actor accepts inline ad records or a local JSON file.

{
  "ads": [
    {
      "id": "ad-1001",
      "pageName": "Peak Protein Co.",
      "primaryText": "Tired of expensive protein that tastes chalky? Save 20% on our bestselling whey bundles today only.",
      "headline": "20% off protein bundles",
      "cta": "SHOP_NOW",
      "linkUrl": "https://example.com/protein-bundle",
      "publisherPlatforms": ["facebook", "instagram"]
    }
  ],
  "inputFile": "",
  "brandName": "FitFuel Competitor Set",
  "industry": "E-commerce supplements",
  "reportMode": "both",
  "includeRecommendations": true
}

Supported top-level fields:

• ads: Array of Meta/Facebook Ads Library-style records.
• inputFile: Local JSON path containing either an array of ads or an object with ads.
• brandName: Brand, market, or competitor set name for the report.
• industry: Industry context used for deterministic recommendations.
• reportMode: summary, per_ad, or both.
• includeRecommendations: Include practical recommendations when true.

Ad records can use flexible field names such as text, body, primaryText, headline, title, pageName, advertiserName, cta, callToAction, linkUrl, landingPageUrl, publisherPlatform, impressions, spend, and common snapshot fields.

Output

Each run emits one JSON report to the default dataset path or the requested local --output path.

The output always includes:

• analyzedAds
• summary
• topHooks
• offerAngles
• ctaPatterns
• competitorPositioning
• recommendations
• monetizationNotes

See samples/sample-output.json for a full example.

Local development

npm install
npm run build
npm test
npm run sample

The sample command runs without an Apify token and writes samples/sample-run-output.json.

You can also run directly:

npm run build
node dist/src/index.js --input samples/sample-input.json --output /tmp/meta-ads-report.json

If no input is provided, the Actor falls back to samples/sample-input.json for local development.

Apify behavior

The repository follows Apify Actor conventions:

• .actor/actor.json
• .actor/input_schema.json
• Dockerfile
• dataset-style JSON output

In Apify-style local storage, the Actor checks:

storage/key_value_stores/default/INPUT.json

and writes:

storage/datasets/default/000000001.json

No API keys, private cookies, or Meta credentials are used.

Analysis approach

The MVP uses deterministic local analysis rather than an LLM. This keeps it fast, explainable, cheap to run, and safe for a paid Store MVP.

Current extraction/classification includes:

• Hook patterns: discount, urgency, social proof, local relevance, convenience, lead magnets, premium aspiration, and more.
• Offer angles: percentage discount, bundle, free shipping, financing, trial, guarantee, quote, limited-time offer, outcome promise, launch.
• CTA patterns: shop, learn more, book, sign up, quote, download, message, call, apply.
• Positioning: price-led value, premium quality, speed/convenience, trust/proof, local specialist, outcome-led performance.
• Per-ad recommendations and aggregate market recommendations.

Store positioning

Meta Ads Intelligence Analyzer is positioned as a lightweight paid intelligence Actor, not a commodity scraper. Buyers bring ad records; the Actor turns them into monetizable strategy artifacts they can use in client reports, campaign planning, and competitor monitoring.