# Compare two keywords on one Google Trends scale (US)

**Use case:** 

Runs Google Trends Data API in comparison mode for two keywords in the United States over the last 12 months, so both series share one 0 to 100 scale and can be read against each other. Rows carry the keyword, date, value and a scale label. Swap in your own two to five terms for a direct comparison.

## Input

```json
{
  "mode": "keywords",
  "keywords": [
    "air fryer",
    "espresso machine"
  ],
  "startUrls": [],
  "geo": "US",
  "timeRange": "today 12-m",
  "dataTypes": [
    "interest_over_time"
  ],
  "compareKeywords": true,
  "includeKeywordInsights": false,
  "searchType": "",
  "category": "0",
  "language": "en-US",
  "timezoneOffsetMinutes": 0,
  "trendingHours": "24",
  "trendStatus": "all",
  "maxItems": 120,
  "maxRunSeconds": 3600,
  "maxRetriesPerRequest": 8,
  "rotateAfterRequests": 24,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}
```

## Output

```json
{
  "keyword": {
    "label": "Keyword"
  },
  "term": {
    "label": "Trending term"
  },
  "dataType": {
    "label": "Dataset"
  },
  "date": {
    "label": "Date"
  },
  "dateLabel": {
    "label": "Date (Google label)"
  },
  "geoName": {
    "label": "Region"
  },
  "geoCode": {
    "label": "Region code"
  },
  "query": {
    "label": "Related query"
  },
  "rankType": {
    "label": "Top / Rising"
  },
  "value": {
    "label": "Value"
  },
  "formattedValue": {
    "label": "Formatted"
  },
  "valueScale": {
    "label": "Scale"
  },
  "searchVolume": {
    "label": "Search volume"
  },
  "growthPercent": {
    "label": "Growth %"
  },
  "isActive": {
    "label": "Active"
  },
  "breakdownQueries": {
    "label": "Breakdown queries"
  },
  "trendsUrl": {
    "label": "Trends URL"
  },
  "link": {
    "label": "Link"
  },
  "hasData": {
    "label": "Has data"
  },
  "geo": {
    "label": "Geo"
  },
  "timeRange": {
    "label": "Time range"
  },
  "categoryIds": {
    "label": "Category IDs"
  },
  "comparisonGroup": {
    "label": "Comparison group"
  },
  "startTimestamp": {
    "label": "Started (epoch)"
  },
  "endTimestamp": {
    "label": "Ended (epoch)"
  },
  "timestamp": {
    "label": "Epoch"
  },
  "newsArticleCount": {
    "label": "News articles"
  },
  "hours": {
    "label": "Window (h)"
  }
}
```

## About this Actor

This example demonstrates how to use [Google Trends Scraper and Data API](https://apify.com/danthedataman/google-trends-data-api.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/danthedataman/google-trends-data-api.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

This Task's input is already configured above. Use it as-is rather than inventing a new one.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/danthedataman/google-trends-data-api.md

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).