# 📈 Google Trends Scraper — Keywords & Interest (`nexgendata/google-trends-scraper`) Actor Extract Google Trends data — search interest over time, related queries, geographic breakdown & trending topics. Build market research, content calendars & SEO strategies. Pay per query. - **URL**: https://apify.com/nexgendata/google-trends-scraper.md - **Developed by:** [Stephan Corbeil](https://apify.com/nexgendata) (community) - **Categories:** SEO tools, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 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 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 ## Google Trends Scraper by nexgendata Extract search interest data from Google Trends for any keyword or set of keywords. This actor uses the Google Trends platform to retrieve interest-over-time data, related queries, rising search terms, and regional interest breakdowns. Compare up to five keywords simultaneously, filter by country, and select time ranges from the past hour to the past five years. Built for SEO professionals, market researchers, content strategists, and anyone who needs quantified search demand data. Google Trends is the only public source for relative search volume data across Google's search engine, which processes over 8.5 billion searches daily. The interest scores Google provides (scaled 0-100) reveal how search demand for any topic changes over time and varies by geography. This data drives decisions about content timing, market entry, product naming, ad spend allocation, and competitive positioning. This actor packages that data into structured JSON so you can analyze trends programmatically instead of manually reading charts on the Google Trends website. ### How It Works Provide up to five keywords and the actor queries Google Trends for comparative interest data. The results include three data types. Interest over time gives you a time series showing how search interest for each keyword has changed across your selected time range, with each data point containing the date, keyword, and interest score (0-100 where 100 is peak popularity). Related queries show what other searches people perform alongside your keywords, split into top queries (consistently popular) and rising queries (experiencing rapid growth). Regional interest breaks down search popularity by state, country, or metro area depending on your geographic scope. Time range options span from the past hour (for real-time trend monitoring) to the past five years (for long-term market analysis). Country filtering lets you focus on a specific market or compare global patterns. When you provide multiple keywords, Google Trends normalizes the data so you can directly compare relative search interest between terms — essential for keyword prioritization and competitive analysis. ### Who Uses This SEO professionals use Google Trends data to prioritize keywords by actual search demand, identify seasonal patterns in search behavior, and discover emerging search terms before they become competitive. A content team deciding between two article topics can compare their Google Trends data to choose the one with growing search interest rather than declining demand. Market researchers track Google Trends data to measure brand awareness relative to competitors, identify emerging consumer interests, and validate market size assumptions. When Google searches for "electric bikes" triple in a specific region, that signals a concrete demand shift that retail, manufacturing, and logistics businesses need to know about. Product teams use trend data to time launches, name features, and identify geographic markets where demand is strongest. Media buyers and advertising teams align campaign timing with search interest patterns. Running ads for "tax software" in January when search interest begins climbing gets better results than flat spending across the year. Content publishers schedule articles to coincide with rising search trends rather than publishing into declining interest. ### Pricing This actor costs $5 per 1,000 data points. A single keyword analysis over 12 months (52 weekly data points plus related queries and regional data) typically generates 100-200 data points, costing about $0.50-1.00. A comprehensive competitive analysis comparing 5 keywords with all data types runs roughly $2-3. Monthly monitoring of 20 keyword sets costs approximately $15-25/month depending on the breadth of related queries returned. # Actor input Schema ## `keywords` (type: `array`): List of keywords to get trend data for (up to 5). Google Trends compares relative interest between keywords. ## `geo` (type: `string`): Two-letter country code to filter results (e.g. US, GB, DE). Leave empty for worldwide data. ## `timeframe` (type: `string`): Time range for trend data: today 1-m (past month), today 3-m (past 3 months), today 12-m (past year), today 5-y (past 5 years). ## Actor input object example ```json { "keywords": [ "artificial intelligence", "machine learning" ], "geo": "US", "timeframe": "today 12-m" } ```` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "keywords": [ "artificial intelligence", "machine learning" ] }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/google-trends-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "keywords": [ "artificial intelligence", "machine learning", ] } # Run the Actor and wait for it to finish run = client.actor("nexgendata/google-trends-scraper").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 '{ "keywords": [ "artificial intelligence", "machine learning" ] }' | apify call nexgendata/google-trends-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/google-trends-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "📈 Google Trends Scraper — Keywords & Interest", "description": "Extract Google Trends data — search interest over time, related queries, geographic breakdown & trending topics. Build market research, content calendars & SEO strategies. Pay per query.", "version": "0.0", "x-build-id": "2vCTjGG8lfvMFIKEq" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~google-trends-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-google-trends-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~google-trends-scraper/runs": { "post": { "operationId": "runs-sync-nexgendata-google-trends-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~google-trends-scraper/run-sync": { "post": { "operationId": "run-sync-nexgendata-google-trends-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keywords" ], "properties": { "keywords": { "title": "Keywords", "type": "array", "description": "List of keywords to get trend data for (up to 5). Google Trends compares relative interest between keywords.", "items": { "type": "string" } }, "geo": { "title": "Country Code", "type": "string", "description": "Two-letter country code to filter results (e.g. US, GB, DE). Leave empty for worldwide data.", "default": "US" }, "timeframe": { "title": "Time Range", "enum": [ "now 1-H", "now 4-H", "now 1-d", "now 7-d", "today 1-m", "today 3-m", "today 12-m", "today 5-y" ], "type": "string", "description": "Time range for trend data: today 1-m (past month), today 3-m (past 3 months), today 12-m (past year), today 5-y (past 5 years).", "default": "today 12-m" } } }, "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 } } } } } } } } } } ```