Google Trends is a valuable source of data on people’s needs and interests around the globe. Yet, if you’re working on a large project or planning to build proprietary software on top of Google Trends data, accessing it at scale may be challenging as there is no official API.
In this article, we will show you what solutions to this problem exist, and what’s the most reliable way to pull vast volumes of data from Google Trends. Besides that, we will describe a few use cases on how different businesses can take advantage of Google Trends data.
Contents:
What’s Google Trends and how does it work?
Is there a way to get Google Trends data with API?
How to use DataForSEO Google Trends API for search analytics?
What’s Google Trends and how does it work?
Google Trends is a free tool from Google that shows the popularity of search queries on different Google properties across various regions and languages. In particular, Google Trends allows analyzing keyword popularity on five types of search properties:
- Web (Google Search),
- Shopping,
- Images,
- News,
- YouTube.
Besides that, users can investigate search statistics for a certain location (e.g., worldwide, United States), category (e.g., health, travel, science, sports), and time range.
By the way, the timeframe of data available for analysis is one of the most fascinating things about Google Trends. For web search, users can plot search interest from 2004 to the present day, and for other types of search, they can trace keyword popularity back to 2008. For example, using Google Trends we can investigate how people’s interest in facebook changed from its founding in 2004 to the present day.
Another extremely valuable feature in Google Trends is the hour-to-hour illustration of the search interest trend. For instance, if you own a food delivery business in New York state, you can check at what time of the day people are most interested in food delivery there. In this way, you can quickly understand how the popularity of your services changes throughout the day and optimize your working hours and resources availability.
Overall, Google Trends is a valuable source of insights into the development of search interest around virtually any query. These insights help many industries, including e-commerce, marketing, media, and SEO, to enrich their business strategies with real-world data.
With that said, it is worth noting that search interest or search popularity indicated on Google Trends shouldn’t be confused with search volume.
Search volume (e.g., in the Google Keyword Planner tool) is a metric showing how many searches for a certain keyword took place within a certain timeframe. In contrast, Google Trends displays the popularity of a given keyword or topic on a 0-100% scale relative to total searches on Google over the same period of time.
In the Google News Lab article, Simon Rogers, Data journalist, and Data Editor at Google explains how Google Trends data is scaled and normalized:
“This means that when we look at search interest over time for a topic, we’re looking at that interest as a proportion of all searches on all topics on Google at that time and location. When we look at regional search interest for a topic, we’re looking at the search interest for that topic in a given region as a proportion of all searches on all topics on Google in that same place and time.”
In other words, Google Trends shows scaled data as a ratio between a keyword’s search volume and the sum total of all Google search volumes for the same time and location.
Now that you know everything about Google Trends and the data it provides, let’s take a look at how large-scale projects can extract this data to power their own applications and search analytics.
Is there a way to get Google Trends data with API?
In case you’re dealing with substantial datasets, automating and scaling data extraction will save you impressive amounts of time and effort. One of the most convenient and scalable ways to pull data is by using an API.
Yet, unfortunately, there’s no publicly available official Google Trends API that would allow accessing Google’s data and developing on top of it.
While there is an unofficial Pytrends API for scraping data from Google Trends with Python, it’s more of a method to automate report downloads. Sadly, Pytrends API goes out of order every time once Google Trends backend is updated and, unfortunately, the API has a low request limit. For these reasons, Pytrends is impractical for large-scale projects or adoption in software development.
However, this does not mean that an efficient solution for extracting Google Trends data does not exist. If you prioritize availability and reliability, you can opt for commercial APIs that ensure stable data supply.
For example, DataForSEO offers a powerful Google Trends API which allows you to make up to 2000 API calls per minute, with each POST call containing up to 100 tasks. We guarantee 99.95% availability and offer 24/7 live chat customer support and free integration assistance.
Importantly, DataForSEO Google Trends API integrates with almost all programming languages. We have clear and easy-to-understand documentation with code examples, which helps both developers and non-developers to efficiently use our API.
Our Google Trends API documentation contains code examples for Python, PHP, NodeJS, and cSharp.
DataForSEO Google Trends API is also an affordable option. Analyzing 10,000 keywords (5 keywords in one POST request) will cost only $22.5. To learn more about Google Trends API pricing, visit our pricing page.
In the following part, we’ll show you how to apply DataForSEO Google Trends API for several most common use cases.
How to use DataForSEO Google Trends API for search analytics?
1. Leverage vast historical data on search trends
While keyword search volume can reveal the past 12 months of data, sometimes you need a bigger picture that even historical search volume cannot reveal. Besides that, historical search volume usually reflects Google web search stats only.
In contrast, with far-reaching data from Google Trends, you can explore separate keyword popularity rates not only for web search but also for Google Shopping, Youtube, and other Google’s properties.
For example, if you sell some seasonal products such as Christmas decorations, Google Trends data can help you investigate when exactly people start searching for your product using Google Shopping.
Knowing this, you can optimize your advertising campaigns according to the search pattern. You can also forecast when your product will be trending and thus anticipate during what period its cost per click in Google Ads is likely to be higher. In this way, you can make sure you allocate enough budget to a related shopping campaign.
You might also want to use Google web search trends to find the best time for updating your blog posts on seasonal topics. For example, if searches for advice on your product start growing and peak within the same dates every year, you can leverage this data to refresh your related articles a few weeks in advance. This strategy will help to skyrocket your content in SERPs. Similarly, you could use past insights to predict what content will be in demand for an upcoming period, and produce new blog posts.
Below, we will show you how to obtain stats for Google Shopping and for web search respectively using DataForSEO Google Trends API. The responses from our API will contain data on each point from the search interest graph.
POST: https://api.dataforseo.com/v3/keywords_data/google_trends/explore/live
Request for Google Shopping search
[
{
"time_range": "2008_present",
"type": "froogle",
"keywords": [
"Christmas Decorations"
]
}
]
{
"version": "0.1.20220201",
"status_code": 20000,
"status_message": "Ok.",
"time": "7.0941 sec.",
"cost": 0.009,
"tasks_count": 1,
"tasks_error": 0,
"tasks": [
{
"id": "02071437-1535-0173-0000-03b695e91729",
"status_code": 20000,
"status_message": "Ok.",
"time": "7.0491 sec.",
"cost": 0.009,
"result_count": 1,
"path": [
"v3",
"keywords_data",
"google_trends",
"explore",
"live"
],
"data": {
"api": "keywords_data",
"function": "explore",
"se": "google_trends",
"time_range": "2008_present",
"type": "froogle",
"keywords": [
"Christmas Decorations"
]
},
"result": [
{
"keywords": [
"Christmas Decorations"
],
"type": "trends",
"location_code": 0,
"language_code": "en",
"check_url": "https://trends.google.com/trends/explore?hl=en&gprop=froogle&date=all_2008&q=Christmas%20Decorations",
"datetime": "2022-02-07 12:37:54 +00:00",
"items_count": 4,
"items": [
{
"position": 1,
"type": "google_trends_graph",
"title": "Interest over time",
"keywords": [
"Christmas Decorations"
],
"data": [
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"timestamp": 1640995200,
"values": [
6
]
},
{
"date_from": "2022-02-01",
"date_to": "2022-02-07",
"timestamp": 1643673600,
"values": [
2
]
}
],
"averages": []
},
{...},
{...},
{...}
]
}
]
}
]
}
Another practical use case for Google Trends data is comparing the progress in popularity for your business vs competitors by using business names as input. Just like in the web interface, using DataForSEO Google Trends API, you can compare the popularity of up to 5 keywords at once. These could also be the products you sell vs the ones your competitors offer. Here’s how to structure your request to run a comparison.
POST: https://api.dataforseo.com/v3/keywords_data/google_trends/explore/live
Example request
[
{
"location_name": "United States",
"time_range": "2004_present",
"type": "web",
"keywords": [
"Air fryer",
"Instant Pot"
]
}
]
{
"version": "0.1.20220201",
"status_code": 20000,
"status_message": "Ok.",
"time": "3.0531 sec.",
"cost": 0.009,
"tasks_count": 1,
"tasks_error": 0,
"tasks": [
{
"id": "02071446-1535-0173-0000-ecc204156b14",
"status_code": 20000,
"status_message": "Ok.",
"time": "3.0320 sec.",
"cost": 0.009,
"result_count": 1,
"path": [
"v3",
"keywords_data",
"google_trends",
"explore",
"live"
],
"data": {
"api": "keywords_data",
"function": "explore",
"se": "google_trends",
"location_name": "United States",
"time_range": "2004_present",
"type": "web",
"keywords": [
"Air fryer",
"Instant Pot"
]
},
"result": [
{
"keywords": [
"Air fryer",
"Instant Pot"
],
"type": "trends",
"location_code": 2840,
"language_code": "en",
"check_url": "https://trends.google.com/trends/explore?hl=en&geo=US&date=all&q=Air%20fryer%2CInstant%20Pot",
"datetime": "2022-02-07 12:46:16 +00:00",
"items_count": 6,
"items": [
{
"position": 1,
"type": "google_trends_graph",
"title": "Interest over time",
"keywords": [
"Air fryer",
"Instant Pot"
],
"data": [
{
"date_from": "2004-01-01",
"date_to": "2004-01-31",
"timestamp": 1072915200,
"values": [
null,
null
]
},
{
"date_from": "2004-02-01",
"date_to": "2004-02-29",
"timestamp": 1075593600,
"values": [
0,
null
]
},
{
"date_from": "2004-03-01",
"date_to": "2004-03-31",
"timestamp": 1078099200,
"values": [
null,
null
]
},
{
"date_from": "2004-04-01",
"date_to": "2004-04-30",
"timestamp": 1080777600,
"values": [
null,
null
]
},
{
"date_from": "2004-05-01",
"date_to": "2004-05-31",
"timestamp": 1083369600,
"values": [
null,
null
]
},
{
"date_from": "2004-06-01",
"date_to": "2004-06-30",
"timestamp": 1086048000,
"values": [
null,
null
]
},
{
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"timestamp": 1088640000,
"values": [
0,
null
]
},
{
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]
},
{
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null,
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]
},
{
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"timestamp": 1096588800,
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null,
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]
},
{
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"timestamp": 1099267200,
"values": [
null,
0
]
},
{
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"timestamp": 1101859200,
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null,
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]
},
{
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]
},
{
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]
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{...},
{...},
{...},
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]
}
]
}
]
}
Besides the use cases mentioned above, there’s one more very practical and cost-saving application for search interest data from Google Trends. You can use these search stats to display search volume graphs in a keyword research tool preview for unregistered users.
The thing is, data from Google Ads is more expensive than Google Trends data, which makes the latter a budget-friendly option for lead generation. At the same time, search volume data from Google Ads and search interest stats from Google Trends make for very close visual representations.
To illustrate this, let’s take a look at the graphs we’ve built using DataForSEO Google Sheets Connector. The upper part of the screenshot shows a graph based on our Google Ads API data, while the lower part contains a graph filled with search interest from our Google Trends API.
As you can see, both graphs clearly demonstrate the same search trend. So, for example, if you blur the dates and values to display bare line graphs, Google Trends data is a perfect low-cost substitute for search volume.
2. Observe search interest by region and subregion
One of the most useful parts of Google Trends is the possibility to review search interest by region. You could benefit from this data in several ways.
To begin with, regional and subregional search statistics are very helpful for understanding where your target audience is.
Let’s say you own a bike rental service in Florida and you want to expand to new states. Accordingly, you need to know which ones would be most interested in your services. To find that out, you could use Google Trends to check subregional search interest for rent a bike in the United States.
Taking this further, you could also turn to Google Trends map before extending your advertising campaign to new states. Besides targeting these locations through paid campaigns, you could outline a regional SEO strategy to better rank organically. Last but not least, if own a retail business, Google Trends map data could give you insights on optimizing your stock levels based on product popularity in different regions.
Whatever use case for Google Trends data you have in mind, DataForSEO API can help you easily pull regional search interest data at scale. You can also compare up to five terms across different subregions at once.
The API response will contain google_trends_map elements with comparative map data for all keywords as well as separate map data for search interest in each individual term.
POST: https://api.dataforseo.com/v3/keywords_data/google_trends/explore/live
Example request
[
{
"location_name": "United States",
"time_range": "past_12_months",
"type": "web",
"keywords": [
"rent a bike",
"bike rentals near me"
]
}
]
{
"version": "0.1.20220201",
"status_code": 20000,
"status_message": "Ok.",
"time": "3.1476 sec.",
"cost": 0.009,
"tasks_count": 1,
"tasks_error": 0,
"tasks": [
{
"id": "02071458-1535-0173-0000-9c259b500a64",
"status_code": 20000,
"status_message": "Ok.",
"time": "3.0432 sec.",
"cost": 0.009,
"result_count": 1,
"path": [
"v3",
"keywords_data",
"google_trends",
"explore",
"live"
],
"data": {
"api": "keywords_data",
"function": "explore",
"se": "google_trends",
"location_name": "United States",
"time_range": "past_12_months",
"type": "web",
"keywords": [
"rent a bike",
"bike rentals near me"
]
},
"result": [
{
"keywords": [
"rent a bike",
"bike rentals near me"
],
"type": "trends",
"location_code": 2840,
"language_code": "en",
"check_url": "https://trends.google.com/trends/explore?hl=en&geo=US&q=rent%20a%20bike%2Cbike%20rentals%20near%20me",
"datetime": "2022-02-07 12:58:57 +00:00",
"items_count": 6,
"items": [
{...},
{
"position": 2,
"type": "google_trends_map",
"title": "Compared breakdown by subregion",
"keywords": [
"rent a bike",
"bike rentals near me"
],
"data": [
{
"geo_id": "US-HI",
"geo_name": "Hawaii",
"values": [
null,
100
],
"max_value_index": 1
},
{
"geo_id": "US-DE",
"geo_name": "Delaware",
"values": [
null,
100
],
"max_value_index": 1
},
{
"geo_id": "US-VT",
"geo_name": "Vermont",
"values": [
null,
100
],
"max_value_index": 1
},
{
"geo_id": "US-SC",
"geo_name": "South Carolina",
"values": [
8,
92
],
"max_value_index": 1
},
{
"geo_id": "US-FL",
"geo_name": "Florida",
"values": [
25,
75
],
"max_value_index": 1
},
{
"geo_id": "US-MT",
"geo_name": "Montana",
"values": [
null,
100
],
"max_value_index": 1
},
{
"geo_id": "US-CO",
"geo_name": "Colorado",
"values": [
22,
78
],
"max_value_index": 1
},
{
"geo_id": "US-ID",
"geo_name": "Idaho",
"values": [
null,
100
],
"max_value_index": 1
},
{
"geo_id": "US-UT",
"geo_name": "Utah",
"values": [
31,
69
],
"max_value_index": 1
},
{
"geo_id": "US-OR",
"geo_name": "Oregon",
"values": [
32,
68
],
"max_value_index": 1
},
{
"geo_id": "US-ME",
"geo_name": "Maine",
"values": [
null,
100
],
"max_value_index": 1
},
{
"geo_id": "US-NJ",
"geo_name": "New Jersey",
"values": [
27,
73
],
"max_value_index": 1
},
{
"geo_id": "US-MA",
"geo_name": "Massachusetts",
"values": [
26,
74
],
"max_value_index": 1
},
{
"geo_id": "US-RI",
"geo_name": "Rhode Island",
"values": [
null,
100
],
"max_value_index": 1
},
{
"geo_id": "US-AZ",
"geo_name": "Arizona",
"values": [
31,
69
],
"max_value_index": 1
},
{
"geo_id": "US-DC",
"geo_name": "District of Columbia",
"values": [
40,
60
],
"max_value_index": 1
},
{
"geo_id": "US-WA",
"geo_name": "Washington",
"values": [
31,
69
],
"max_value_index": 1
},
{
"geo_id": "US-MD",
"geo_name": "Maryland",
"values": [
32,
68
],
"max_value_index": 1
},
{
"geo_id": "US-CA",
"geo_name": "California",
"values": [
45,
55
],
"max_value_index": 1
},
{
"geo_id": "US-WI",
"geo_name": "Wisconsin",
"values": [
41,
59
],
"max_value_index": 1
},
{
"geo_id": "US-VA",
"geo_name": "Virginia",
"values": [
31,
69
],
"max_value_index": 1
},
{
"geo_id": "US-NV",
"geo_name": "Nevada",
"values": [
42,
58
],
"max_value_index": 1
},
{
"geo_id": "US-AL",
"geo_name": "Alabama",
"values": [
null,
100
],
"max_value_index": 1
},
{
"geo_id": "US-NC",
"geo_name": "North Carolina",
"values": [
40,
60
],
"max_value_index": 1
},
{
"geo_id": "US-GA",
"geo_name": "Georgia",
"values": [
48,
52
],
"max_value_index": 1
},
{
"geo_id": "US-NY",
"geo_name": "New York",
"values": [
62,
38
],
"max_value_index": 0
},
{
"geo_id": "US-PA",
"geo_name": "Pennsylvania",
"values": [
48,
52
],
"max_value_index": 1
},
{
"geo_id": "US-CT",
"geo_name": "Connecticut",
"values": [
67,
33
],
"max_value_index": 0
},
{
"geo_id": "US-IL",
"geo_name": "Illinois",
"values": [
35,
65
],
"max_value_index": 1
},
{
"geo_id": "US-MN",
"geo_name": "Minnesota",
"values": [
43,
57
],
"max_value_index": 1
},
{
"geo_id": "US-TX",
"geo_name": "Texas",
"values": [
53,
47
],
"max_value_index": 0
},
{
"geo_id": "US-TN",
"geo_name": "Tennessee",
"values": [
56,
44
],
"max_value_index": 0
},
{
"geo_id": "US-LA",
"geo_name": "Louisiana",
"values": [
56,
44
],
"max_value_index": 0
},
{
"geo_id": "US-MO",
"geo_name": "Missouri",
"values": [
33,
67
],
"max_value_index": 1
},
{
"geo_id": "US-MI",
"geo_name": "Michigan",
"values": [
50,
50
],
"max_value_index": 0
},
{
"geo_id": "US-OH",
"geo_name": "Ohio",
"values": [
43,
57
],
"max_value_index": 1
},
{
"geo_id": "US-IN",
"geo_name": "Indiana",
"values": [
33,
67
],
"max_value_index": 1
},
{
"geo_id": "US-NM",
"geo_name": "New Mexico",
"values": [
null,
null
],
"max_value_index": 0
},
{
"geo_id": "US-WY",
"geo_name": "Wyoming",
"values": [
null,
null
],
"max_value_index": 1
},
{
"geo_id": "US-WV",
"geo_name": "West Virginia",
"values": [
null,
null
],
"max_value_index": 0
},
{
"geo_id": "US-AR",
"geo_name": "Arkansas",
"values": [
null,
null
],
"max_value_index": 0
},
{
"geo_id": "US-AK",
"geo_name": "Alaska",
"values": [
null,
null
],
"max_value_index": 0
},
{
"geo_id": "US-KY",
"geo_name": "Kentucky",
"values": [
null,
null
],
"max_value_index": 0
},
{
"geo_id": "US-NE",
"geo_name": "Nebraska",
"values": [
null,
null
],
"max_value_index": 0
},
{
"geo_id": "US-IA",
"geo_name": "Iowa",
"values": [
null,
null
],
"max_value_index": 0
},
{
"geo_id": "US-NH",
"geo_name": "New Hampshire",
"values": [
null,
null
],
"max_value_index": 0
},
{
"geo_id": "US-SD",
"geo_name": "South Dakota",
"values": [
null,
null
],
"max_value_index": 1
},
{
"geo_id": "US-MS",
"geo_name": "Mississippi",
"values": [
null,
null
],
"max_value_index": 0
},
{
"geo_id": "US-KS",
"geo_name": "Kansas",
"values": [
null,
null
],
"max_value_index": 0
},
{
"geo_id": "US-OK",
"geo_name": "Oklahoma",
"values": [
null,
null
],
"max_value_index": 1
},
{
"geo_id": "US-ND",
"geo_name": "North Dakota",
"values": [
null,
null
],
"max_value_index": 0
}
]
},
{
"position": 3,
"type": "google_trends_map",
"title": "Interest by subregion",
"keywords": [
"rent a bike"
],
"data": [
{
"geo_id": "US-CA",
"geo_name": "California",
"values": [
100
],
"max_value_index": 0
},
{
"geo_id": "US-FL",
"geo_name": "Florida",
"values": [
100
],
"max_value_index": 0
},
{
"geo_id": "US-OR",
"geo_name": "Oregon",
"values": [
95
],
"max_value_index": 0
},
{
"geo_id": "US-UT",
"geo_name": "Utah",
"values": [
94
],
"max_value_index": 0
},
{
"geo_id": "US-NY",
"geo_name": "New York",
"values": [
90
],
"max_value_index": 0
},
{
"geo_id": "US-DC",
"geo_name": "District of Columbia",
"values": [
88
],
"max_value_index": 0
},
{
"geo_id": "US-GA",
"geo_name": "Georgia",
"values": [
83
],
"max_value_index": 0
},
{
"geo_id": "US-WI",
"geo_name": "Wisconsin",
"values": [
79
],
"max_value_index": 0
},
{
"geo_id": "US-CO",
"geo_name": "Colorado",
"values": [
79
],
"max_value_index": 0
},
{
"geo_id": "US-NV",
"geo_name": "Nevada",
"values": [
74
],
"max_value_index": 0
},
{
"geo_id": "US-CT",
"geo_name": "Connecticut",
"values": [
71
],
"max_value_index": 0
},
{
"geo_id": "US-PA",
"geo_name": "Pennsylvania",
"values": [
70
],
"max_value_index": 0
},
{
"geo_id": "US-TX",
"geo_name": "Texas",
"values": [
69
],
"max_value_index": 0
},
{
"geo_id": "US-NC",
"geo_name": "North Carolina",
"values": [
64
],
"max_value_index": 0
},
{
"geo_id": "US-TN",
"geo_name": "Tennessee",
"values": [
64
],
"max_value_index": 0
},
{
"geo_id": "US-LA",
"geo_name": "Louisiana",
"values": [
63
],
"max_value_index": 0
},
{
"geo_id": "US-AZ",
"geo_name": "Arizona",
"values": [
61
],
"max_value_index": 0
},
{
"geo_id": "US-MD",
"geo_name": "Maryland",
"values": [
57
],
"max_value_index": 0
},
{
"geo_id": "US-WA",
"geo_name": "Washington",
"values": [
56
],
"max_value_index": 0
},
{
"geo_id": "US-NJ",
"geo_name": "New Jersey",
"values": [
53
],
"max_value_index": 0
},
{
"geo_id": "US-MN",
"geo_name": "Minnesota",
"values": [
53
],
"max_value_index": 0
},
{
"geo_id": "US-MI",
"geo_name": "Michigan",
"values": [
52
],
"max_value_index": 0
},
{
"geo_id": "US-VA",
"geo_name": "Virginia",
"values": [
51
],
"max_value_index": 0
},
{
"geo_id": "US-MA",
"geo_name": "Massachusetts",
"values": [
49
],
"max_value_index": 0
},
{
"geo_id": "US-IL",
"geo_name": "Illinois",
"values": [
39
],
"max_value_index": 0
},
{
"geo_id": "US-OH",
"geo_name": "Ohio",
"values": [
36
],
"max_value_index": 0
},
{
"geo_id": "US-SC",
"geo_name": "South Carolina",
"values": [
30
],
"max_value_index": 0
},
{
"geo_id": "US-MO",
"geo_name": "Missouri",
"values": [
26
],
"max_value_index": 0
},
{
"geo_id": "US-IN",
"geo_name": "Indiana",
"values": [
16
],
"max_value_index": 0
},
{
"geo_id": "US-NM",
"geo_name": "New Mexico",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-HI",
"geo_name": "Hawaii",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-ME",
"geo_name": "Maine",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-WY",
"geo_name": "Wyoming",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-WV",
"geo_name": "West Virginia",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-AR",
"geo_name": "Arkansas",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-AK",
"geo_name": "Alaska",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-KY",
"geo_name": "Kentucky",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-NE",
"geo_name": "Nebraska",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-IA",
"geo_name": "Iowa",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-NH",
"geo_name": "New Hampshire",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-SD",
"geo_name": "South Dakota",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-ID",
"geo_name": "Idaho",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-MS",
"geo_name": "Mississippi",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-MT",
"geo_name": "Montana",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-DE",
"geo_name": "Delaware",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-KS",
"geo_name": "Kansas",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-AL",
"geo_name": "Alabama",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-OK",
"geo_name": "Oklahoma",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-ND",
"geo_name": "North Dakota",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-RI",
"geo_name": "Rhode Island",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-VT",
"geo_name": "Vermont",
"values": [
null
],
"max_value_index": 0
}
]
},
{...},
{
"position": 5,
"type": "google_trends_map",
"title": "Interest by subregion",
"keywords": [
"bike rentals near me"
],
"data": [
{
"geo_id": "US-HI",
"geo_name": "Hawaii",
"values": [
100
],
"max_value_index": 0
},
{
"geo_id": "US-SC",
"geo_name": "South Carolina",
"values": [
60
],
"max_value_index": 0
},
{
"geo_id": "US-DE",
"geo_name": "Delaware",
"values": [
60
],
"max_value_index": 0
},
{
"geo_id": "US-CO",
"geo_name": "Colorado",
"values": [
46
],
"max_value_index": 0
},
{
"geo_id": "US-FL",
"geo_name": "Florida",
"values": [
40
],
"max_value_index": 0
},
{
"geo_id": "US-ID",
"geo_name": "Idaho",
"values": [
37
],
"max_value_index": 0
},
{
"geo_id": "US-AK",
"geo_name": "Alaska",
"values": [
25
],
"max_value_index": 0
},
{
"geo_id": "US-RI",
"geo_name": "Rhode Island",
"values": [
25
],
"max_value_index": 0
},
{
"geo_id": "US-NJ",
"geo_name": "New Jersey",
"values": [
24
],
"max_value_index": 0
},
{
"geo_id": "US-MT",
"geo_name": "Montana",
"values": [
22
],
"max_value_index": 0
},
{
"geo_id": "US-UT",
"geo_name": "Utah",
"values": [
21
],
"max_value_index": 0
},
{
"geo_id": "US-OR",
"geo_name": "Oregon",
"values": [
21
],
"max_value_index": 0
},
{
"geo_id": "US-MA",
"geo_name": "Massachusetts",
"values": [
20
],
"max_value_index": 0
},
{
"geo_id": "US-NC",
"geo_name": "North Carolina",
"values": [
19
],
"max_value_index": 0
},
{
"geo_id": "US-AL",
"geo_name": "Alabama",
"values": [
18
],
"max_value_index": 0
},
{
"geo_id": "US-DC",
"geo_name": "District of Columbia",
"values": [
18
],
"max_value_index": 0
},
{
"geo_id": "US-VA",
"geo_name": "Virginia",
"values": [
17
],
"max_value_index": 0
},
{
"geo_id": "US-AZ",
"geo_name": "Arizona",
"values": [
16
],
"max_value_index": 0
},
{
"geo_id": "US-PA",
"geo_name": "Pennsylvania",
"values": [
15
],
"max_value_index": 0
},
{
"geo_id": "US-GA",
"geo_name": "Georgia",
"values": [
15
],
"max_value_index": 0
},
{
"geo_id": "US-NH",
"geo_name": "New Hampshire",
"values": [
14
],
"max_value_index": 0
},
{
"geo_id": "US-WI",
"geo_name": "Wisconsin",
"values": [
14
],
"max_value_index": 0
},
{
"geo_id": "US-CA",
"geo_name": "California",
"values": [
14
],
"max_value_index": 0
},
{
"geo_id": "US-LA",
"geo_name": "Louisiana",
"values": [
14
],
"max_value_index": 0
},
{
"geo_id": "US-MD",
"geo_name": "Maryland",
"values": [
13
],
"max_value_index": 0
},
{
"geo_id": "US-VT",
"geo_name": "Vermont",
"values": [
11
],
"max_value_index": 0
},
{
"geo_id": "US-WA",
"geo_name": "Washington",
"values": [
11
],
"max_value_index": 0
},
{
"geo_id": "US-MN",
"geo_name": "Minnesota",
"values": [
11
],
"max_value_index": 0
},
{
"geo_id": "US-ME",
"geo_name": "Maine",
"values": [
11
],
"max_value_index": 0
},
{
"geo_id": "US-IL",
"geo_name": "Illinois",
"values": [
9
],
"max_value_index": 0
},
{
"geo_id": "US-TN",
"geo_name": "Tennessee",
"values": [
9
],
"max_value_index": 0
},
{
"geo_id": "US-AR",
"geo_name": "Arkansas",
"values": [
8
],
"max_value_index": 0
},
{
"geo_id": "US-MI",
"geo_name": "Michigan",
"values": [
8
],
"max_value_index": 0
},
{
"geo_id": "US-NY",
"geo_name": "New York",
"values": [
7
],
"max_value_index": 0
},
{
"geo_id": "US-TX",
"geo_name": "Texas",
"values": [
7
],
"max_value_index": 0
},
{
"geo_id": "US-NV",
"geo_name": "Nevada",
"values": [
6
],
"max_value_index": 0
},
{
"geo_id": "US-MO",
"geo_name": "Missouri",
"values": [
6
],
"max_value_index": 0
},
{
"geo_id": "US-IN",
"geo_name": "Indiana",
"values": [
5
],
"max_value_index": 0
},
{
"geo_id": "US-OH",
"geo_name": "Ohio",
"values": [
5
],
"max_value_index": 0
},
{
"geo_id": "US-CT",
"geo_name": "Connecticut",
"values": [
2
],
"max_value_index": 0
},
{
"geo_id": "US-WY",
"geo_name": "Wyoming",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-SD",
"geo_name": "South Dakota",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-IA",
"geo_name": "Iowa",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-NM",
"geo_name": "New Mexico",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-MS",
"geo_name": "Mississippi",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-WV",
"geo_name": "West Virginia",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-NE",
"geo_name": "Nebraska",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-KS",
"geo_name": "Kansas",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-OK",
"geo_name": "Oklahoma",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-KY",
"geo_name": "Kentucky",
"values": [
null
],
"max_value_index": 0
},
{
"geo_id": "US-ND",
"geo_name": "North Dakota",
"values": [
null
],
"max_value_index": 0
}
]
},
{...}
]
}
]
}
]
}
3. Capture related search terms and topics
Besides the sections we described above, namely, Interest over time and Interest by region, Google Trends has two more sections – Related topics and Related queries – which we’ll discuss in this part.
Related topics show broader subjects searchers for your term are interested in. Alongside that, Related queries present keywords that searchers for your term were also looking for, that is, offers keywords thematically related to your term.
Data from these parts of the tool is particularly helpful for keyword research and content creation. For example, using suggested topics, you can generate ideas for blog content or develop categories for your product pages. At the same time, related queries can be used as a source of new keyword ideas for blog post optimization or to fill in the gaps in your ad campaigns’ coverage.
Now let’s take a look at how Related topics and Related queries appear in the web interface. You can find these sections below Interest by region/subregion.
As you can see, both Related topics and Related queries contain subsections – Top and Rising.
For Top, you will see the following type of scoring: 100 for the most commonly searched term or topic, 50 for a term or topic searched half as often as the most popular one, and so on.
At the same time, Rising will show you an increase in search frequency since the last time period in percentages. Some results may have a “Breakout” mark instead, which means that the term or topic had an immense increase in popularity. This mark may also suggest that a term or topic is new or had very few to zero searches earlier.
Using DataForSEO Google Trends API, you will receive related queries and topics as two separate objects with complete data from the corresponding top and rising subsections.
POST: https://api.dataforseo.com/v3/keywords_data/google_trends/explore/live
Example request
[
{
"location_name": "United States",
"time_range": "past_12_months",
"type": "web",
"keywords": [
"waterproof jacket"
]
}
]
{
"version": "0.1.20220201",
"status_code": 20000,
"status_message": "Ok.",
"time": "4.0618 sec.",
"cost": 0.009,
"tasks_count": 1,
"tasks_error": 0,
"tasks": [
{
"id": "02071504-1535-0173-0000-957f50c586a9",
"status_code": 20000,
"status_message": "Ok.",
"time": "4.0357 sec.",
"cost": 0.009,
"result_count": 1,
"path": [
"v3",
"keywords_data",
"google_trends",
"explore",
"live"
],
"data": {
"api": "keywords_data",
"function": "explore",
"se": "google_trends",
"location_name": "United States",
"time_range": "past_12_months",
"type": "web",
"keywords": [
"waterproof jacket"
]
},
"result": [
{
"keywords": [
"waterproof jacket"
],
"type": "trends",
"location_code": 2840,
"language_code": "en",
"check_url": "https://trends.google.com/trends/explore?hl=en&geo=US&q=waterproof%20jacket",
"datetime": "2022-02-07 13:04:03 +00:00",
"items_count": 4,
"items": [
{...},
{...},
{...},
{
"position": 4,
"type": "google_trends_queries_list",
"title": "Related queries",
"keywords": [
"waterproof jacket"
],
"data": {
"top": [
{
"query": "waterproof jacket mens",
"value": 100
},
{
"query": "waterproof rain jacket",
"value": 92
},
{
"query": "rain jacket",
"value": 90
},
{
"query": "womens waterproof jacket",
"value": 90
},
{
"query": "women waterproof jacket",
"value": 45
},
{
"query": "north face waterproof jacket",
"value": 38
},
{
"query": "columbia waterproof jacket",
"value": 34
},
{
"query": "waterproof running jacket",
"value": 29
},
{
"query": "lightweight waterproof jacket",
"value": 27
},
{
"query": "waterproof ski jacket",
"value": 27
},
{
"query": "waterproof down jacket",
"value": 22
},
{
"query": "carhartt",
"value": 22
},
{
"query": "carhartt jacket",
"value": 20
},
{
"query": "carhartt waterproof jacket",
"value": 17
},
{
"query": "the north face",
"value": 15
},
{
"query": "patagonia waterproof jacket",
"value": 15
},
{
"query": "waterproof shell jacket",
"value": 14
},
{
"query": "rei",
"value": 12
},
{
"query": "mens waterproof rain jacket",
"value": 10
},
{
"query": "waterproof hunting jacket",
"value": 9
},
{
"query": "boys waterproof jacket",
"value": 4
}
],
"rising": [
{
"query": "women waterproof jacket",
"value": 90
},
{
"query": "waterproof down jacket",
"value": 70
},
{
"query": "rei",
"value": 70
},
{
"query": "waterproof jacket mens",
"value": 70
},
{
"query": "mens waterproof rain jacket",
"value": 70
},
{
"query": "waterproof ski jacket",
"value": 50
}
]
}
}
]
}
]
}
]
}
Conclusion
Whether you’re a business owner, an SEO expert, or a software developer, Google Trends has useful data for you. In particular, it can help you get:
1 Vast historical search trends,
2 Search interest by region and subregion,
3 Related search terms and topics.
When it comes to extracting this data for voluminous projects, however, Google Trends does not offer an official solution. Turning to open-source unofficial APIs does not prove efficient as they are unreliable and prone to error.
Yet, DataForSEO has a quality alternative for you. With DataForSEO Google Trends API, you don’t need to worry about system availability or request limits. Our system allows making 2000 API calls per minute, but we can also raise this limit upon request.
If that isn’t enough, DataForSEO Google Trends API integrates with almost all programming languages. See our documentation and code examples by this link or register now to try our Google Trends API for free.