Meta Ads Competitor Intelligence Report

Turn Facebook / Meta Ads Library scraper results into a client-ready weekly competitor intelligence package for brands, agencies, and growth teams.

This Actor does not scrape Meta directly. It analyzes rows from a Meta Ads Library scraper run, dataset, or inline export and produces:

• REPORT / report.md — a human-readable competitor ad intelligence report
• OUTPUT / summary.json — a structured JSON summary for automation
• ADS_CSV / ads.csv — a traceable CSV evidence export
• One dataset summary row for quick inspection in Apify Console

Why this exists

Popular Apify Store Actors sell because they solve a direct workflow with clear inputs: “paste URLs or keywords, get useful data.” Meta ads scrapers already create the raw ad rows; this Actor handles the next paid workflow: turning raw Facebook/Instagram ad data into a repeatable marketing report.

Use it after running a compatible Facebook / Meta Ads Library scraper when you need a weekly deliverable instead of a raw dataset.

What this is for

Good use cases:

• DTC competitor monitoring — track what brands are actively advertising and what messages repeat.
• Agency client research — create quick competitor creative briefs before strategy calls.
• Ad library review workflows — turn Meta scraper datasets into Markdown, JSON, and CSV artifacts.
• Weekly marketing intelligence — compare a current dataset with a baseline dataset to identify new, removed, and continued ads.
• Scheduled reporting — run the upstream scraper weekly, then run this Actor with the new dataset and previous baseline.

Key insights included

The report highlights evidence-backed patterns such as:

• active ad count and advertiser count
• top advertiser and share of captured ads
• new / removed / continued ads when a baseline is provided
• repeated copy and message patterns
• repeated terms across ad copy
• offer / angle signals such as promo, launch, demo, proof, benefit, and urgency
• CTA destination domains and landing URL patterns
• suggested marketer review actions
• source IDs and Meta Ad Library evidence URLs

How to use

Option A — analyze a Meta ads dataset

1. Run a Facebook / Meta Ads Library scraper.
2. Copy the resulting dataset ID.
3. Run this Actor with:

{
  "reportName": "Weekly competitor ad report - sportswear",
  "datasetId": "YOUR_META_DATASET_ID",
  "search": {
    "keywords": ["Nike", "Puma", "New Balance"],
    "country": "US",
    "activeStatus": "active"
  },
  "maxItems": 500
}

Option B — analyze a run ID

{
  "reportName": "Weekly competitor ad report",
  "runId": "YOUR_META_SCRAPER_RUN_ID",
  "maxItems": 500
}

The Actor will load the run's default dataset.

Option C — compare against a baseline

For a weekly change report, provide a current dataset and a previous dataset:

{
  "reportName": "Weekly competitor ad change report",
  "datasetId": "CURRENT_META_DATASET_ID",
  "baselineDatasetId": "PREVIOUS_META_DATASET_ID",
  "maxItems": 1000
}

If no baseline is supplied, the output is clearly labeled as an initial snapshot and will not claim week-over-week movement.

Option D — paste inline rows

Use items when you already have exported ad rows or want to test the report format without connecting another dataset.

Pricing

This Actor uses pay-per-event pricing:

• Actor start: small platform-managed start event
• Completed report: charged once only after the report artifacts are generated successfully

This keeps pricing simple for weekly monitoring workflows: one run creates one report package.

Important guardrails

• This Actor analyzes public ad rows supplied by the user or by another scraper output.
• It does not promise real-time monitoring, unlimited scraping, ROAS, winning ads, spend, conversions, impressions, or performance lift.
• It does not send emails/DMs, spend ad budget, or contact prospects.
• TikTok is marked as experimental/fallback only and is not a core source in this MVP.
• Placement/platform analytics are only meaningful when the source rows contain platform values.

Output records

Dataset summary

The default dataset contains one summary row with fields such as:

• status
• reportType
• reportName
• totalAds
• activeAds
• advertiserCount
• newAds
• removedAds
• topAdvertiser
• topAdvertiserSharePct
• topOfferAngle
• topCtaDomain
• sourceDatasetId
• sourceRunId
• reportChargeStatus

Key-value store records

• OUTPUT — JSON summary
• REPORT — Markdown report
• ADS_CSV — CSV evidence export

Best results

For a sales-ready or client-ready report:

• use specific competitor brands or Facebook Page IDs in the upstream Meta scraper
• keep each weekly run's search criteria consistent
• store the prior week's dataset ID as the next run's baseline
• review evidence URLs before quoting insights externally
• tag report rows by offer angle, landing page type, and creative theme for your own playbook

Example workflow

1. Run a Meta Ads Library scraper for 3–10 competitor brands.
2. Save the dataset ID.
3. Run this Actor with the dataset ID.
4. Download REPORT, OUTPUT, and ADS_CSV.
5. Next week, use the new dataset as datasetId and the prior dataset as baselineDatasetId.

The result is a repeatable competitor ad intelligence loop instead of one-off manual ad library review.