# Meta Ads Competitor Intelligence Report (`jy-labs/weekly-competitor-ad-intelligence`) Actor Turn Facebook / Meta Ads Library scraper datasets into weekly competitor intelligence reports. Generate a client-ready Markdown report, JSON summary, and CSV evidence export with top advertisers, offer-angle signals, CTA domains, and traceable ad IDs. - **URL**: https://apify.com/jy-labs/weekly-competitor-ad-intelligence.md - **Developed by:** [Juyeop Park](https://apify.com/jy-labs) (community) - **Categories:** Marketing, Social media, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.49 / completed ad intelligence report This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## 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: ```json { "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 ```json { "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: ```json { "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. # Actor input Schema ## `reportName` (type: `string`): Human-readable report title shown in REPORT and OUTPUT. ## `datasetId` (type: `string`): Dataset ID from a Facebook / Meta Ads Library scraper run, for example Apify's Facebook Ads Library Scraper or another compatible Meta ad export. Use this, runId, or inline items. ## `runId` (type: `string`): Run ID from a Meta Ad Library scraper run. The Actor loads the run's default dataset. ## `items` (type: `array`): Inline rows exported from a Facebook / Meta Ads Library scraper. Useful for samples, API calls, or testing the report format without another dataset. ## `baselineDatasetId` (type: `string`): Optional previous Meta dataset ID for week-over-week change comparison. If omitted, the report is an initial snapshot. ## `baselineRunId` (type: `string`): Optional previous Meta scraper run ID for change comparison. ## `baselineItems` (type: `array`): Optional prior rows for change comparison. If omitted, the report is an initial snapshot. ## `search` (type: `object`): Keywords, country, activeStatus, Page IDs, and period shown in the report. This metadata does not run the Meta scraper by itself. ## `maxItems` (type: `integer`): Maximum rows to load from dataset/run sources. Larger values create bigger CSV evidence exports. ## `includeTikTokExperimental` (type: `boolean`): Scope control only. TikTok is not a core source and must not be sold as full platform analytics in this MVP. ## Actor input object example ```json { "reportName": "Sample weekly sportswear competitor ad report", "items": [ { "libraryID": "sample-nike-001", "brand": "Nike", "keyword": "Nike", "body": "Run-ready styles for every training day. Explore the latest performance collection.", "startDate": "2026-05-20", "isActive": true, "platforms": [ "Facebook", "Instagram" ], "ctaLink": "https://www.nike.com/running" }, { "libraryID": "sample-puma-001", "brand": "PUMA", "keyword": "Puma", "body": "New season essentials built for training, streetwear, and everyday movement.", "startDate": "2026-05-22", "isActive": true, "platforms": [ "Facebook", "Instagram" ], "ctaLink": "https://us.puma.com/us/en/sports" }, { "libraryID": "sample-newbalance-001", "brand": "New Balance", "keyword": "New Balance", "body": "Fresh running gear and comfort-first sneakers for your next workout.", "startDate": "2026-05-24", "isActive": true, "platforms": [ "Facebook" ], "ctaLink": "https://www.newbalance.com/running/" } ], "search": { "keywords": [ "Nike", "Puma", "New Balance" ], "country": "US", "activeStatus": "active", "period": "sample input" }, "maxItems": 500, "includeTikTokExperimental": false } ``` # Actor output Schema ## `summaryDataset` (type: `string`): No description ## `jsonSummary` (type: `string`): No description ## `markdownReport` (type: `string`): No description ## `adsCsv` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "reportName": "Sample weekly sportswear competitor ad report", "items": [ { "libraryID": "sample-nike-001", "brand": "Nike", "keyword": "Nike", "body": "Run-ready styles for every training day. Explore the latest performance collection.", "startDate": "2026-05-20", "isActive": true, "platforms": [ "Facebook", "Instagram" ], "ctaLink": "https://www.nike.com/running" }, { "libraryID": "sample-puma-001", "brand": "PUMA", "keyword": "Puma", "body": "New season essentials built for training, streetwear, and everyday movement.", "startDate": "2026-05-22", "isActive": true, "platforms": [ "Facebook", "Instagram" ], "ctaLink": "https://us.puma.com/us/en/sports" }, { "libraryID": "sample-newbalance-001", "brand": "New Balance", "keyword": "New Balance", "body": "Fresh running gear and comfort-first sneakers for your next workout.", "startDate": "2026-05-24", "isActive": true, "platforms": [ "Facebook" ], "ctaLink": "https://www.newbalance.com/running/" } ], "search": { "keywords": [ "Nike", "Puma", "New Balance" ], "country": "US", "activeStatus": "active", "period": "sample input" }, "maxItems": 500 }; // Run the Actor and wait for it to finish const run = await client.actor("jy-labs/weekly-competitor-ad-intelligence").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "reportName": "Sample weekly sportswear competitor ad report", "items": [ { "libraryID": "sample-nike-001", "brand": "Nike", "keyword": "Nike", "body": "Run-ready styles for every training day. Explore the latest performance collection.", "startDate": "2026-05-20", "isActive": True, "platforms": [ "Facebook", "Instagram", ], "ctaLink": "https://www.nike.com/running", }, { "libraryID": "sample-puma-001", "brand": "PUMA", "keyword": "Puma", "body": "New season essentials built for training, streetwear, and everyday movement.", "startDate": "2026-05-22", "isActive": True, "platforms": [ "Facebook", "Instagram", ], "ctaLink": "https://us.puma.com/us/en/sports", }, { "libraryID": "sample-newbalance-001", "brand": "New Balance", "keyword": "New Balance", "body": "Fresh running gear and comfort-first sneakers for your next workout.", "startDate": "2026-05-24", "isActive": True, "platforms": ["Facebook"], "ctaLink": "https://www.newbalance.com/running/", }, ], "search": { "keywords": [ "Nike", "Puma", "New Balance", ], "country": "US", "activeStatus": "active", "period": "sample input", }, "maxItems": 500, } # Run the Actor and wait for it to finish run = client.actor("jy-labs/weekly-competitor-ad-intelligence").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "reportName": "Sample weekly sportswear competitor ad report", "items": [ { "libraryID": "sample-nike-001", "brand": "Nike", "keyword": "Nike", "body": "Run-ready styles for every training day. Explore the latest performance collection.", "startDate": "2026-05-20", "isActive": true, "platforms": [ "Facebook", "Instagram" ], "ctaLink": "https://www.nike.com/running" }, { "libraryID": "sample-puma-001", "brand": "PUMA", "keyword": "Puma", "body": "New season essentials built for training, streetwear, and everyday movement.", "startDate": "2026-05-22", "isActive": true, "platforms": [ "Facebook", "Instagram" ], "ctaLink": "https://us.puma.com/us/en/sports" }, { "libraryID": "sample-newbalance-001", "brand": "New Balance", "keyword": "New Balance", "body": "Fresh running gear and comfort-first sneakers for your next workout.", "startDate": "2026-05-24", "isActive": true, "platforms": [ "Facebook" ], "ctaLink": "https://www.newbalance.com/running/" } ], "search": { "keywords": [ "Nike", "Puma", "New Balance" ], "country": "US", "activeStatus": "active", "period": "sample input" }, "maxItems": 500 }' | apify call jy-labs/weekly-competitor-ad-intelligence --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jy-labs/weekly-competitor-ad-intelligence", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Meta Ads Competitor Intelligence Report", "description": "Turn Facebook / Meta Ads Library scraper datasets into weekly competitor intelligence reports. Generate a client-ready Markdown report, JSON summary, and CSV evidence export with top advertisers, offer-angle signals, CTA domains, and traceable ad IDs.", "version": "0.2", "x-build-id": "vDgleEBZLXWaec8Jz" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jy-labs~weekly-competitor-ad-intelligence/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jy-labs-weekly-competitor-ad-intelligence", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jy-labs~weekly-competitor-ad-intelligence/runs": { "post": { "operationId": "runs-sync-jy-labs-weekly-competitor-ad-intelligence", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jy-labs~weekly-competitor-ad-intelligence/run-sync": { "post": { "operationId": "run-sync-jy-labs-weekly-competitor-ad-intelligence", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "reportName": { "title": "Report name", "type": "string", "description": "Human-readable report title shown in REPORT and OUTPUT.", "default": "Weekly Competitor Ad Intelligence" }, "datasetId": { "title": "Current Meta dataset ID", "type": "string", "description": "Dataset ID from a Facebook / Meta Ads Library scraper run, for example Apify's Facebook Ads Library Scraper or another compatible Meta ad export. Use this, runId, or inline items." }, "runId": { "title": "Current Meta run ID", "type": "string", "description": "Run ID from a Meta Ad Library scraper run. The Actor loads the run's default dataset." }, "items": { "title": "Inline current Meta ad rows", "type": "array", "description": "Inline rows exported from a Facebook / Meta Ads Library scraper. Useful for samples, API calls, or testing the report format without another dataset.", "items": { "type": "object", "additionalProperties": true } }, "baselineDatasetId": { "title": "Baseline dataset ID", "type": "string", "description": "Optional previous Meta dataset ID for week-over-week change comparison. If omitted, the report is an initial snapshot." }, "baselineRunId": { "title": "Baseline run ID", "type": "string", "description": "Optional previous Meta scraper run ID for change comparison." }, "baselineItems": { "title": "Inline baseline ad rows", "type": "array", "description": "Optional prior rows for change comparison. If omitted, the report is an initial snapshot.", "items": { "type": "object", "additionalProperties": true } }, "search": { "title": "Search criteria metadata", "type": "object", "description": "Keywords, country, activeStatus, Page IDs, and period shown in the report. This metadata does not run the Meta scraper by itself.", "default": { "keywords": [ "Nike", "Puma", "New Balance" ], "country": "US", "activeStatus": "active" }, "additionalProperties": true }, "maxItems": { "title": "Max source rows", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum rows to load from dataset/run sources. Larger values create bigger CSV evidence exports.", "default": 500 }, "includeTikTokExperimental": { "title": "Mark TikTok as experimental/fallback", "type": "boolean", "description": "Scope control only. TikTok is not a core source and must not be sold as full platform analytics in this MVP.", "default": false } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```