# Google Ads Competitor Intelligence Ranker (`eltropa/google-ads-longevity-activity-ranker`) Actor Prioritize which competitor Google Ads deserve your attention first. Rank public Google Ads Transparency data by longevity and recent activity using explainable signals. - **URL**: https://apify.com/eltropa/google-ads-longevity-activity-ranker.md - **Developed by:** [Geovanis Lopez](https://apify.com/eltropa) (community) - **Categories:** Automation, Lead generation, SEO tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 results 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Google Ads Competitor Intelligence Ranker Don’t manually review 100 competitor ads. See which ones deserve your attention first. This Actor turns public Google Ads Transparency data into a prioritized competitor ad analysis. It is built for Google Ads competitor intelligence, not for raw scraping and not for unsupported performance claims. Use it to spot: - ad longevity, - active ads, - recent visibility, - public preview availability, - verified advertisers when present. ### What It Does - Ranks competitor ads from Google Ads Transparency by observable signal strength. - Highlights long-running creatives and newly active ads. - Helps you prioritize which competitor ads to review first. ### Who It's For - Performance marketers - Agencies - DTC teams - Founders doing competitor ad analysis ### Input Enter a competitor domain, for example `nike.com`. - `domain`: competitor website domain. - `country`: ISO-2 market code. - `maxAds`: 1–500 ads. ### Output The default dataset returns a ranked list with these real fields: - `rank` - `signalScore` - `signalLevel` - `advertiser` - `advertiserId` - `creativeId` - `adUrl` - `adAccessUrl` - `format` - `firstShown` - `lastShown` - `daysObserved` - `daysSinceLastSeen` - `isVerified` - `previewAvailable` - `previewUrl` - `region` - `whyItMatters` `whyItMatters` is a list of short explanatory reasons, not a single text field. `isVerified` is preserved as `true`, `false`, or `null` when the source does not provide a value. `adAccessUrl` points to the best available public page for inspection. ### How to Interpret the Ranking - `HIGH_ATTENTION`: strongest longevity and/or recent activity signals. - `WATCH`: useful signal, but not strong enough to treat as top priority. - `LOW_EVIDENCE`: public evidence is limited or stale. Longevity and activity are signals, not proof of profitability, ROAS, conversions, or campaign performance. ### Typical Cost Examples Approximate costs based on the current pricing configuration: - Actor start: `$0.05` - Result: `$0.001` per item Examples: - 10 results ≈ `$0.06` - 100 results ≈ `$0.15` - 500 results ≈ `$0.55` These are approximate examples, not a fixed promise, and they assume the current pricing configuration stays unchanged. ### Why It Matters Instead of a raw dump of competitor ads, you get a ranked view of public Google Ads Transparency signals so you can focus on the ads most worth manual review. # Actor input Schema ## `domain` (type: `string`): Example: nike.com ## `country` (type: `string`): ISO 2-letter code, e.g. US, ES, GB ## `maxAds` (type: `integer`): Maximum number of ads to analyze in a single run. ## Actor input object example ```json { "domain": "nike.com", "country": "US", "maxAds": 100 } ```` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "domain": "nike.com" }; // Run the Actor and wait for it to finish const run = await client.actor("eltropa/google-ads-longevity-activity-ranker").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "domain": "nike.com" } # Run the Actor and wait for it to finish run = client.actor("eltropa/google-ads-longevity-activity-ranker").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 '{ "domain": "nike.com" }' | apify call eltropa/google-ads-longevity-activity-ranker --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=eltropa/google-ads-longevity-activity-ranker", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Ads Competitor Intelligence Ranker", "description": "Prioritize which competitor Google Ads deserve your attention first. Rank public Google Ads Transparency data by longevity and recent activity using explainable signals.", "version": "0.1", "x-build-id": "bsupXOYpasfVNoBJA" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/eltropa~google-ads-longevity-activity-ranker/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-eltropa-google-ads-longevity-activity-ranker", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/eltropa~google-ads-longevity-activity-ranker/runs": { "post": { "operationId": "runs-sync-eltropa-google-ads-longevity-activity-ranker", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/eltropa~google-ads-longevity-activity-ranker/run-sync": { "post": { "operationId": "run-sync-eltropa-google-ads-longevity-activity-ranker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "domain" ], "properties": { "domain": { "title": "Competitor domain", "type": "string", "description": "Example: nike.com" }, "country": { "title": "Country", "type": "string", "description": "ISO 2-letter code, e.g. US, ES, GB", "default": "US" }, "maxAds": { "title": "Maximum ads", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of ads to analyze in a single run.", "default": 100 } } }, "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 } } } } } } } } } } ```