# Meta Ad Intelligence (`acebuilds/meta-ad-intel`) Actor Analyze any brand's Facebook & Instagram ad strategy in 60 seconds. Get spend velocity, A/B-test winners, creative mix, top CTAs, and side-by-side competitor comparison — structured intelligence, not raw rows. Built for marketers, agencies, and brand strategists. - **URL**: https://apify.com/acebuilds/meta-ad-intel.md - **Developed by:** [Ace](https://apify.com/acebuilds) (community) - **Categories:** Social media, Lead generation, SEO tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $1,000.00 / 1,000 brand intelligence reports This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 Ad Intelligence **Get a competitor's full Meta ad strategy in 60 seconds — not 10,000 rows of JSON.** This actor turns Meta Ad Library data into a structured intelligence report built for marketers, agencies, and brand strategists who want answers, not raw scrapes. ### What you get For each brand you point at, one structured report: - **Lifecycle counts** — total ads, active right now, retired - **Spend velocity** — ramping, steady, or pulling back (4-week trend) - **Top winners** — longest-running ads (= A/B-test survivors with proven creative) - **Recent launches** — what they're testing in the last 7 days - **Creative mix** — image / video / carousel % - **Platform mix** — Facebook / Instagram / Audience Network / Messenger % - **Top CTAs** — which call-to-action buttons they lean on - **Hook patterns** — most-used opening words, avg copy length - **Plain-English insights** — narrative bullets, not raw fields - **Side-by-side comparison** — drop in competitor URLs and get a delta block ### Input ```json { "pageUrls": ["https://www.facebook.com/nike"], "comparePageUrls": ["https://www.facebook.com/adidas"], "country": "US", "maxAdsPerBrand": 200, "activeOnly": false } ```` | Field | Required | Description | |---|---|---| | `pageUrls` | yes | Array of Facebook page URLs or Meta Ad Library search URLs. One report per URL. | | `comparePageUrls` | no | Competitor URLs to compare against the primary brand(s). | | `country` | no | ISO 2-letter country code (default `US`) or `ALL`. | | `maxAdsPerBrand` | no | Cap on ads scraped per brand (default 200). | | `activeOnly` | no | If `true`, only currently-running ads are scraped. Default `false` so velocity and lifespan metrics work. | ### How it works This actor calls [`curious_coder/facebook-ads-library-scraper`](https://apify.com/curious_coder/facebook-ads-library-scraper) as a sub-actor for the raw Meta Ad Library scraping, then runs an analysis layer on top to produce the strategy report. You're paying for the analysis — not for re-scraping. ### Cost This actor triggers one [`curious_coder/facebook-ads-library-scraper`](https://apify.com/curious_coder/facebook-ads-library-scraper) sub-actor run **per brand URL**. You are billed for both the sub-actor run and this actor's run. **Typical cost:** 1 brand at 200 ads ≈ 0.5–1 Apify compute unit (roughly $0.01–$0.03 on pay-as-you-go). Comparing 5 brands scrapes 5 sub-actor runs in sequence. To reduce costs: lower `maxAdsPerBrand` (50–100 is plenty for velocity and creative-mix analysis) or set `activeOnly: true` to skip retired ads. You need an Apify account with a valid subscription or free-tier credits to run this actor. ### Use cases - **Agencies** — show prospects what their competitors are doing in a one-pager - **Brand strategists** — track competitor ramp/retreat without manual library browsing - **Founders** — reverse-engineer category leaders before launching - **Affiliate marketers** — find proven hooks before testing your own - **Investors** — track ad spend velocity as a brand-health signal ### Deploy ```bash apify push ``` # Actor input Schema ## `pageUrls` (type: `array`): List of Facebook page URLs (e.g. https://www.facebook.com/nike) or Meta Ad Library search URLs to analyze. Each URL produces one intelligence report. ## `comparePageUrls` (type: `array`): Optional list of competitor page URLs to compare against the primary brand(s). Adds a side-by-side comparison block to each report. ## `country` (type: `string`): ISO 2-letter country code to scope ads to (e.g. US, GB, DE) or ALL for global. ## `maxAdsPerBrand` (type: `integer`): Cap on ads scraped per brand. Lower = faster + cheaper. 200 is plenty for most brands. ## `activeOnly` (type: `boolean`): If true, analyzes only ads currently running. If false (default), includes recently-stopped ads to enable spend-velocity and A/B-winner analysis. ## Actor input object example ```json { "pageUrls": [ "https://www.facebook.com/nike" ], "country": "US", "maxAdsPerBrand": 200, "activeOnly": false } ``` # Actor output Schema ## `results` (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 = { "pageUrls": [ "https://www.facebook.com/nike" ] }; // Run the Actor and wait for it to finish const run = await client.actor("acebuilds/meta-ad-intel").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 = { "pageUrls": ["https://www.facebook.com/nike"] } # Run the Actor and wait for it to finish run = client.actor("acebuilds/meta-ad-intel").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 '{ "pageUrls": [ "https://www.facebook.com/nike" ] }' | apify call acebuilds/meta-ad-intel --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=acebuilds/meta-ad-intel", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Meta Ad Intelligence", "description": "Analyze any brand's Facebook & Instagram ad strategy in 60 seconds. Get spend velocity, A/B-test\n winners, creative mix, top CTAs, and side-by-side competitor comparison — structured intelligence,\n not raw rows. Built for marketers, agencies, and brand strategists.", "version": "0.1", "x-build-id": "5NZIljTM7MhSZcd9x" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/acebuilds~meta-ad-intel/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-acebuilds-meta-ad-intel", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/acebuilds~meta-ad-intel/runs": { "post": { "operationId": "runs-sync-acebuilds-meta-ad-intel", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/acebuilds~meta-ad-intel/run-sync": { "post": { "operationId": "run-sync-acebuilds-meta-ad-intel", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "pageUrls" ], "properties": { "pageUrls": { "title": "Brand Page URLs or Ad Library URLs", "type": "array", "description": "List of Facebook page URLs (e.g. https://www.facebook.com/nike) or Meta Ad Library search URLs to analyze. Each URL produces one intelligence report.", "items": { "type": "string" } }, "comparePageUrls": { "title": "Compare Against (optional)", "type": "array", "description": "Optional list of competitor page URLs to compare against the primary brand(s). Adds a side-by-side comparison block to each report.", "items": { "type": "string" } }, "country": { "title": "Country", "type": "string", "description": "ISO 2-letter country code to scope ads to (e.g. US, GB, DE) or ALL for global.", "default": "US" }, "maxAdsPerBrand": { "title": "Max Ads to Analyze per Brand", "minimum": 25, "maximum": 1000, "type": "integer", "description": "Cap on ads scraped per brand. Lower = faster + cheaper. 200 is plenty for most brands.", "default": 200 }, "activeOnly": { "title": "Active Ads Only", "type": "boolean", "description": "If true, analyzes only ads currently running. If false (default), includes recently-stopped ads to enable spend-velocity and A/B-winner analysis.", "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 } } } } } } } } } } ```