A Versatile Alternative to Google Trends: Exploring the Power of DataForSEO Trends API
Search trends data is a treasure trove of insights into people’s interests in certain products, services, and information. This data reflects the popularity of search queries on various properties of a search engine in a certain period. Why is it so valuable and beneficial?
Search queries and keywords are depersonalized, making it difficult to discover details about the users who enter a particular query in Google and their actual interests. However, search trends data can provide a glimpse into user behavior and needs. It can also offer insights into the popularity of products and services across different regions, ages, and genders.
This data is invaluable for SEO specialists and marketers seeking to gain a deeper understanding of their target audiences. Furthermore, search trends data is highly beneficial for creators of keyword research tools, as they can provide additional features for their clients and enhance the value proposition of their products. In this article, we explore how you can access search trends data, particularly through the DataForSEO Trends API, and how you can seamlessly integrate this data into your solutions.
Contents:
What is DataForSEO Trends API and how does it work?
Comparing Data ForSEO Trends API with Google Trends API
How to leverage the power of DataForSEO Trends API? Top use cases to consider
What is DataForSEO Trends API and how does it work?
There are various tools and methods available to estimate the popularity of search queries. For example, you can utilize Google Ads or Bing Ads to obtain lists of desired keywords along with their precise search volume data. This allows you to compare search volume changes for specific keywords over time and make projections about how their popularity might change in the future. However, relying solely on Google Ads or Bing Ads may not be the most cost-effective solution.
Another approach to understanding search trends involves conducting market research by directly engaging with consumers. This can include methods such as surveys, interviews, and focus groups to gather insights from respondents. By monitoring changes in consumer preferences, industry trends, and market dynamics through these channels, businesses can attempt to anticipate possible shifts in search behavior. The downside of this approach is that manual research consumes a lot of time and the quality of search trends estimates may vary significantly.
Additionally, you can get search trends from Google Trends. This tool allows you to access actual trends data related to specific locations, insights into related topics, and information on search queries. However, due to the limitations of Google Trends, such as inconsistent data retrieval and restrictions on simultaneous requests, you can’t create a reliable system for automating the retrieval of search trends data.
Analyzing these existing approaches and their ups and downs, we created a completely new solution for fetching search interest data – DataForSEO Trends API. It retrieves search trends from Google Search, Google Shopping, and Google News in a convenient format. Alongside the popularity of search queries, this API can provide you with the following information:
- Search trends data across specific regions and subregions;
- Information on search queries popularity by age and gender;
- Merged data that provides a comprehensive overview of keyword popularity within specified timestamps, regions, subregions, and age and gender demographics;
- Comparison of search interest over time for multiple keywords simultaneously.
The most distinguishing feature of DataForSEO Trends API is our proprietary algorithm behind it. Let’s delve into how it works to give you a clearer idea of what our new API can do.
First, we pay meticulous attention to data collection to make our algorithm work. We utilize a sophisticated approach that leverages multiple data sources to provide accurate and comprehensive search trend estimates. These sources are utilized in different proportions depending on what query you enter into the API.
The core source of our search trends data is the vast array of content we have indexed from across the internet. We gather information from search engine results, shopping listings, and web pages to create a comprehensive overview of search query popularity. Moreover, our process extends beyond merely scanning billions of pages and saving the text and content. We also collect data on the backlinks between websites. This massive dataset, comprising substantive content and backlink information, is securely stored in our database.
But that’s not all. To make our results even better, we take advantage of clickstream data – information collected when a person navigates the Internet. By carefully collecting and analyzing anonymized clickstream data from search engines, we can spot content that matches what users really want when they enter a query. To make the results more accurate, the data is aggregated and certain information, like duplicated searches and searches with special characters, is excluded.
Then, we use the data collected to estimate keyword popularity values. These values indicate how frequently a particular keyword or phrase is being searched for by users and how its popularity changes over time. Keyword popularity values are not the same thing as keyword search volume. The search volume depicts the exact number of searches made by a certain keyword or phrase. Keyword popularity, instead, is represented in estimated values that provide a relative measure of how popular a search term is compared to other terms. These values don’t show the absolute search volume numbers, but allow ranking keywords by their popularity over time.
Here are the detailed steps our algorithm follows to process the data and generate keyword popularity values:
1 When you enter a keyword query into API, the algorithm counts the number of keyword queries that were made by users in search engines to determine popularity.
2 After that, the algorithm searches the mentions of this keyword query on different pages of the internet and also counts these mentions.
3 Then, the algorithm merges these two results depending on the presence of data in these two sources and the input parameters of the API request.
4 The popularity scores are normalized and scaled to a standardized range, from 0 to 100 percent.
5 To determine keyword popularity in different regions and subregions, popularity scores are blended with the approximate location data of the users.
6 Following the same logic, the algorithm estimates the users’ age and gender.
For example, let’s see how it works in the Interest by Subregion feature of our DataForSEO Trends Tool. It is a user-friendly and free-to-use tool, built atop the DataForSEO Trends API that allows you to test the capabilities of our API solution.
If you specify one keyword, the popularity values from this section will give you an insight into the popularity rate of a keyword between certain locations. To calculate the result, we determine the highest popularity value for the relevant keywords across all locations and express all other values as a percentage of the highest keyword popularity value.
If you specify multiple keywords, keyword popularity values will help you understand how popular each keyword is – compared to other keywords across all given locations. In this case, we determine the highest popularity value across all keywords across all locations specified. Then we express the popularity rate of each keyword as a percentage of the highest keyword popularity value.
You can experiment with the keywords and try to get some search interest info by yourself, just try out our DataForSEO Trends Tool here, it is free!
Comparing DataForSEO Trends API with Google Trends API
You may wonder about the difference between our new DataForSEO Trends API and our Google Trends API. At first sight, these APIs and the data they provide may seem alike, but certain differences make DataForSEO Trends API more versatile and effective for your goals.
First, DataForSEO Trends API stands out for its reliability compared to Google Trends API. Although Google Trends offers highly accurate keyword popularity values, the platform itself can be inconsistent and unreliable at times. Google Trends has outages and downtime where the service is unavailable or provides incomplete data. This makes it difficult to retrieve large amounts of historical trends data through frequent API calls.
DataForSEO Trends API, on the other hand, doesn’t have such flaws and can consistently process requests and deliver data. It has been engineered as a reliable and scalable solution for retrieving keyword trends and popularity metrics. Moreover, we purposely designed the DataForSEO Trends API with maximum parity to legacy parameters from the Google Trends API. This ensures a smooth transition for any users previously working with the Google Trends API.
The next difference lies in the methods our APIs employ. Google Trends API provides data based on the “Explore” feature of Google Trends and can supply you with information on keyword popularity rate over time, location-specific popularity rate, as well as related topics & queries for keywords. Additionally, you can specify the Google Trends search category code to obtain more precise results, while also having the ability to retrieve data for the specific timestamps you require.
On the other hand, DataForSEO Trends API has a completely different algorithm, as we mentioned before. It analyses the vast amounts of data from different sources and, most importantly, takes into account content popularity metrics and web performance indicators of the relevant pages. Moreover, the algorithm also processes clickstream data to get important insights into user’s browsing behavior.
One of the biggest advantages of our algorithm is that it allows you to get the search interest broken down by gender & age and instantly compare the estimated results. Google Trends API on its own doesn’t have such an opportunity. To do that, we introduced four separate endpoints in DataForSEO Trends API:
- Endpoint “Explore” which provides data on keyword popularity over a certain time range;
- Endpoint “Subregion Interests” for measuring to measure and compare keyword popularity within a certain location or across all locations;
- The “Demography” endpoint gives a breakdown of keyword popularity data by age and gender;
- Finally, the “Merged Data” endpoint combines data provided by the endpoints above to create a comprehensive picture of search interest trends.
Each endpoint has objects that compare keyword popularity data and provide relative results. Also, compared to Google Trends API, DataForSEO Trends API supports only the “Live” method of data retrieval. This means that you will always get fresh results with each request.
If you want to learn more about the specifics of DataForSEO Trends API, check out our documentation, where everything is described to the smallest detail.
To summarise, below are the main differences between DataForSEO Google Trends API and DataForSEO Trends API.
➤ Google Trends API:
- Popularity data is usually more accurate;
- rovides data based on the “Explore” feature of Google Trends;
- Gives you related topics and queries to keywords you specify;
- API supports the “Standard” and “Live” data retrieval methods.
➤ DataForSEO Trends API:
- Based on our proprietary algorithm that follows specific data collection and processing steps;
- Works consistently and properly, compared to Google Trends;
- Can provide an estimated demographic breakdown by age and gender;
- Instant comparison of the retrieved search trends data results;
- Supports only the “Live” data retrieval method.
- Less expensive option to retrieve search trends data.
It is important to mention that DataForSEO Trends API delivers only estimated data, based on the research and analysis conducted by our algorithm. It doesn’t provide search volume data for keywords and data for other keyword metrics. To get precise search volume data, consider using the “Search Volume” endpoint of our Keywords Data API.
Now you know what makes DataForSEO Trends API a versatile and scalable solution for retrieving search trends data. But how can you leverage its capabilities to drive tangible value for your projects and tools? Our powerful API opens up various opportunities for data-driven professionals and companies. Let’s delve into some of the top strategic use cases that showcase the potential of harnessing search trends data with DataForSEO Trends API.
How to leverage the power of DataForSEO Trends API? Top use cases to consider
1Access historical keyword trends data at affordable pricing
If you are looking for the option to get keyword data for infographics and demonstration of changes in search trends over time, DataForSEO Trends API may be the best and most cost-effective way to do that. You don’t need to retrieve historical search volume data directly from Google Ads or Bing Ads. Instead, you can estimate the search volume by using search trends data.
For these purposes, the DataForSEO Trends API stands out for its affordability and versatility. To illustrate, let’s compare the cost of retrieving data for 10,000 keywords across our Trends API, Google Trends API, and Google & Bing Ads APIs. Obtaining data on 10,000 keywords via the Google Ads API in Live mode would cost $0.75 (10 tasks with 1,000 keywords each). In Bing Ads API, the cost would be $3.75 for 50 tasks with 200 keywords each. While these prices seem affordable, estimating keyword popularity values requires manually calculating and comparing the results.
Using the Google Trends API to deliver keyword popularity data for 10,000 keywords in “Live” mode would cost $18 (2,000 tasks with 5 keywords each). However, as mentioned before, Google Trends doesn’t always function reliably, which can cause problems when retrieving massive amounts of keyword data. On the other hand, for the same number of keywords, you would pay only $2 using the DataForSEO Trends API “Explore” endpoint. Moreover, it retrieves data in Live mode and compares the results instantly.
| API | Pricing |
|---|---|
| Google Ads API | $0,75 for data on 10,000 keywords. |
| Bing Ads API | $3,75 for data on 10,000 keywords. |
| Google Trends API | $18 for data on 10,000 keywords. Instant keyword popularity values estimation and comparison. |
| DataForSEO Trends API | $2 for data on 10,000 keywords. Instant keyword popularity values estimation and comparison. |
As you can see, DataForSEO Trends API allows you to access keyword trends data with maximum cost-efficiency. To learn more about DataForSEO Trends API pricing, visit our pricing page.
Here is an example of retrieving historical keyword popularity data with DataForSEO Trends API. Let’s assume you want to compare how search interest for “iPhone” and “Samsung” has changed over the past 90 days in the United States. The graph itself may look like this:
On the graph, you can observe how the popularity values for the search queries “iphone” and “samsung” fluctuated day-by-day over the 90-day period.
To generate the following graph, we need to make a request to the “Explore” endpoint of the DataForSEO Trends API.
POST https://api.dataforseo.com/v3/keywords_data/dataforseo_trends/explore/live
Example request
[
{
"keywords": [
"iphone",
"samsung"
],
"location_name": "United States",
"date_from": "2024-01-11",
"date_to": "2024-04-06",
"type": "web"
}
]
The result will return as follows
{
"version": "0.1.20240313",
"status_code": 20000,
"status_message": "Ok.",
"time": "0.6720 sec.",
"cost": 0.001,
"tasks_count": 1,
"tasks_error": 0,
"tasks": [
{
"id": "04101608-1535-0570-0000-7037e2c7e5ec",
"status_code": 20000,
"status_message": "Ok.",
"time": "0.6149 sec.",
"cost": 0.001,
"result_count": 1,
"path": [
"v3",
"keywords_data",
"dataforseo_trends",
"explore",
"live"
],
"data": {
"api": "keywords_data",
"function": "explore",
"se": "dataforseo_trends",
"keywords": [
"iphone",
"samsung"
],
"location_name": "United States",
"date_from": "2024-01-11",
"date_to": "2024-04-06",
"type": "web"
},
"result": [
{
"keywords": [
"iphone",
"samsung"
],
"type": "trends",
"location_code": 2840,
"language_code": null,
"datetime": "2024-04-10 13:08:27 +00:00",
"items_count": 1,
"items": [
{
"position": 1,
"type": "dataforseo_trends_graph",
"keywords": [
"iphone",
"samsung"
],
"data": [
{
"date_from": "2024-01-11",
"date_to": "2024-01-11",
"timestamp": 1704931200,
"values": [
88,
63
]
},
{
"date_from": "2024-01-12",
"date_to": "2024-01-12",
"timestamp": 1705017600,
"values": [
88,
54
]
},
{
"date_from": "2024-01-13",
"date_to": "2024-01-13",
"timestamp": 1705104000,
"values": [
90,
53
]
},
{
"date_from": "2024-01-14",
"date_to": "2024-01-14",
"timestamp": 1705190400,
"values": [
86,
60
]
},
{
"date_from": "2024-01-15",
"date_to": "2024-01-15",
"timestamp": 1705276800,
"values": [
82,
58
]
},
{
"date_from": "2024-01-16",
"date_to": "2024-01-16",
"timestamp": 1705363200,
"values": [
92,
52
]
},
{
"date_from": "2024-01-17",
"date_to": "2024-01-17",
"timestamp": 1705449600,
"values": [
87,
93
]
},
{
"date_from": "2024-01-18",
"date_to": "2024-01-18",
"timestamp": 1705536000,
"values": [
80,
100
]
},
{
"date_from": "2024-01-19",
"date_to": "2024-01-19",
"timestamp": 1705622400,
"values": [
86,
84
]
},
{
"date_from": "2024-01-20",
"date_to": "2024-01-20",
"timestamp": 1705708800,
"values": [
91,
72
]
},
{
"date_from": "2024-01-21",
"date_to": "2024-01-21",
"timestamp": 1705795200,
"values": [
75,
79
]
},
{
"date_from": "2024-01-22",
"date_to": "2024-01-22",
"timestamp": 1705881600,
"values": [
89,
73
]
},
{
"date_from": "2024-01-23",
"date_to": "2024-01-23",
"timestamp": 1705968000,
"values": [
75,
56
]
},
{
"date_from": "2024-01-24",
"date_to": "2024-01-24",
"timestamp": 1706054400,
"values": [
74,
54
]
},
{
"date_from": "2024-01-25",
"date_to": "2024-01-25",
"timestamp": 1706140800,
"values": [
79,
58
]
},
{
"date_from": "2024-01-26",
"date_to": "2024-01-26",
"timestamp": 1706227200,
"values": [
76,
53
]
},
{
"date_from": "2024-01-27",
"date_to": "2024-01-27",
"timestamp": 1706313600,
"values": [
79,
74
]
},
{
"date_from": "2024-01-28",
"date_to": "2024-01-28",
"timestamp": 1706400000,
"values": [
83,
58
]
},
{
"date_from": "2024-01-29",
"date_to": "2024-01-29",
"timestamp": 1706486400,
"values": [
79,
62
]
},
{
"date_from": "2024-01-30",
"date_to": "2024-01-30",
"timestamp": 1706572800,
"values": [
70,
56
]
},
{
"date_from": "2024-01-31",
"date_to": "2024-01-31",
"timestamp": 1706659200,
"values": [
60,
54
]
},
{
"date_from": "2024-02-01",
"date_to": "2024-02-01",
"timestamp": 1706745600,
"values": [
60,
48
]
},
{
"date_from": "2024-02-02",
"date_to": "2024-02-02",
"timestamp": 1706832000,
"values": [
62,
49
]
},
{
"date_from": "2024-02-03",
"date_to": "2024-02-03",
"timestamp": 1706918400,
"values": [
68,
58
]
},
{
"date_from": "2024-02-04",
"date_to": "2024-02-04",
"timestamp": 1707004800,
"values": [
62,
42
]
},
{
"date_from": "2024-02-05",
"date_to": "2024-02-05",
"timestamp": 1707091200,
"values": [
66,
63
]
},
{
"date_from": "2024-02-06",
"date_to": "2024-02-06",
"timestamp": 1707177600,
"values": [
61,
60
]
},
{
"date_from": "2024-02-07",
"date_to": "2024-02-07",
"timestamp": 1707264000,
"values": [
73,
47
]
},
{
"date_from": "2024-02-08",
"date_to": "2024-02-08",
"timestamp": 1707350400,
"values": [
68,
50
]
},
{
"date_from": "2024-02-09",
"date_to": "2024-02-09",
"timestamp": 1707436800,
"values": [
68,
44
]
},
{
"date_from": "2024-02-10",
"date_to": "2024-02-10",
"timestamp": 1707523200,
"values": [
67,
55
]
},
{
"date_from": "2024-02-11",
"date_to": "2024-02-11",
"timestamp": 1707609600,
"values": [
67,
43
]
},
{
"date_from": "2024-02-12",
"date_to": "2024-02-12",
"timestamp": 1707696000,
"values": [
69,
59
]
},
{
"date_from": "2024-02-13",
"date_to": "2024-02-13",
"timestamp": 1707782400,
"values": [
57,
43
]
},
{
"date_from": "2024-02-14",
"date_to": "2024-02-14",
"timestamp": 1707868800,
"values": [
65,
43
]
},
{
"date_from": "2024-02-15",
"date_to": "2024-02-15",
"timestamp": 1707955200,
"values": [
67,
49
]
},
{
"date_from": "2024-02-16",
"date_to": "2024-02-16",
"timestamp": 1708041600,
"values": [
70,
49
]
},
{
"date_from": "2024-02-17",
"date_to": "2024-02-17",
"timestamp": 1708128000,
"values": [
85,
55
]
},
{
"date_from": "2024-02-18",
"date_to": "2024-02-18",
"timestamp": 1708214400,
"values": [
68,
50
]
},
{
"date_from": "2024-02-19",
"date_to": "2024-02-19",
"timestamp": 1708300800,
"values": [
79,
56
]
},
{
"date_from": "2024-02-20",
"date_to": "2024-02-20",
"timestamp": 1708387200,
"values": [
71,
47
]
},
{
"date_from": "2024-02-21",
"date_to": "2024-02-21",
"timestamp": 1708473600,
"values": [
75,
54
]
},
{
"date_from": "2024-02-22",
"date_to": "2024-02-22",
"timestamp": 1708560000,
"values": [
92,
45
]
},
{
"date_from": "2024-02-23",
"date_to": "2024-02-23",
"timestamp": 1708646400,
"values": [
81,
44
]
},
{
"date_from": "2024-02-24",
"date_to": "2024-02-24",
"timestamp": 1708732800,
"values": [
79,
47
]
},
{
"date_from": "2024-02-25",
"date_to": "2024-02-25",
"timestamp": 1708819200,
"values": [
83,
40
]
},
{
"date_from": "2024-02-26",
"date_to": "2024-02-26",
"timestamp": 1708905600,
"values": [
78,
51
]
},
{
"date_from": "2024-02-27",
"date_to": "2024-02-27",
"timestamp": 1708992000,
"values": [
68,
45
]
},
{
"date_from": "2024-02-28",
"date_to": "2024-02-28",
"timestamp": 1709078400,
"values": [
79,
53
]
},
{
"date_from": "2024-02-29",
"date_to": "2024-02-29",
"timestamp": 1709164800,
"values": [
60,
35
]
},
{
"date_from": "2024-03-01",
"date_to": "2024-03-01",
"timestamp": 1709251200,
"values": [
68,
45
]
},
{
"date_from": "2024-03-02",
"date_to": "2024-03-02",
"timestamp": 1709337600,
"values": [
62,
52
]
},
{
"date_from": "2024-03-03",
"date_to": "2024-03-03",
"timestamp": 1709424000,
"values": [
74,
58
]
},
{
"date_from": "2024-03-04",
"date_to": "2024-03-04",
"timestamp": 1709510400,
"values": [
78,
47
]
},
{
"date_from": "2024-03-05",
"date_to": "2024-03-05",
"timestamp": 1709596800,
"values": [
66,
48
]
},
{
"date_from": "2024-03-06",
"date_to": "2024-03-06",
"timestamp": 1709683200,
"values": [
76,
53
]
},
{
"date_from": "2024-03-07",
"date_to": "2024-03-07",
"timestamp": 1709769600,
"values": [
79,
45
]
},
{
"date_from": "2024-03-08",
"date_to": "2024-03-08",
"timestamp": 1709856000,
"values": [
63,
52
]
},
{
"date_from": "2024-03-09",
"date_to": "2024-03-09",
"timestamp": 1709942400,
"values": [
69,
46
]
},
{
"date_from": "2024-03-10",
"date_to": "2024-03-10",
"timestamp": 1710028800,
"values": [
71,
43
]
},
{
"date_from": "2024-03-11",
"date_to": "2024-03-11",
"timestamp": 1710115200,
"values": [
61,
35
]
},
{
"date_from": "2024-03-12",
"date_to": "2024-03-12",
"timestamp": 1710201600,
"values": [
58,
40
]
},
{
"date_from": "2024-03-13",
"date_to": "2024-03-13",
"timestamp": 1710288000,
"values": [
76,
48
]
},
{
"date_from": "2024-03-14",
"date_to": "2024-03-14",
"timestamp": 1710374400,
"values": [
72,
39
]
},
{
"date_from": "2024-03-15",
"date_to": "2024-03-15",
"timestamp": 1710460800,
"values": [
71,
47
]
},
{
"date_from": "2024-03-16",
"date_to": "2024-03-16",
"timestamp": 1710547200,
"values": [
73,
51
]
},
{
"date_from": "2024-03-17",
"date_to": "2024-03-17",
"timestamp": 1710633600,
"values": [
63,
49
]
},
{
"date_from": "2024-03-18",
"date_to": "2024-03-18",
"timestamp": 1710720000,
"values": [
63,
43
]
},
{
"date_from": "2024-03-19",
"date_to": "2024-03-19",
"timestamp": 1710806400,
"values": [
75,
49
]
},
{
"date_from": "2024-03-20",
"date_to": "2024-03-20",
"timestamp": 1710892800,
"values": [
58,
38
]
},
{
"date_from": "2024-03-21",
"date_to": "2024-03-21",
"timestamp": 1710979200,
"values": [
65,
46
]
},
{
"date_from": "2024-03-22",
"date_to": "2024-03-22",
"timestamp": 1711065600,
"values": [
79,
56
]
},
{
"date_from": "2024-03-23",
"date_to": "2024-03-23",
"timestamp": 1711152000,
"values": [
73,
51
]
},
{
"date_from": "2024-03-24",
"date_to": "2024-03-24",
"timestamp": 1711238400,
"values": [
74,
57
]
},
{
"date_from": "2024-03-25",
"date_to": "2024-03-25",
"timestamp": 1711324800,
"values": [
83,
52
]
},
{
"date_from": "2024-03-26",
"date_to": "2024-03-26",
"timestamp": 1711411200,
"values": [
73,
49
]
},
{
"date_from": "2024-03-27",
"date_to": "2024-03-27",
"timestamp": 1711497600,
"values": [
22,
21
]
},
{
"date_from": "2024-03-28",
"date_to": "2024-03-28",
"timestamp": 1711584000,
"values": [
1,
7
]
},
{
"date_from": "2024-03-29",
"date_to": "2024-03-29",
"timestamp": 1711670400,
"values": [
5,
12
]
},
{
"date_from": "2024-03-30",
"date_to": "2024-03-30",
"timestamp": 1711756800,
"values": [
null,
null
]
},
{
"date_from": "2024-03-31",
"date_to": "2024-03-31",
"timestamp": 1711843200,
"values": [
9,
9
]
},
{
"date_from": "2024-04-01",
"date_to": "2024-04-01",
"timestamp": 1711929600,
"values": [
2,
7
]
},
{
"date_from": "2024-04-02",
"date_to": "2024-04-02",
"timestamp": 1712016000,
"values": [
2,
2
]
},
{
"date_from": "2024-04-03",
"date_to": "2024-04-03",
"timestamp": 1712102400,
"values": [
0,
9
]
},
{
"date_from": "2024-04-04",
"date_to": "2024-04-04",
"timestamp": 1712188800,
"values": [
4,
10
]
},
{
"date_from": "2024-04-05",
"date_to": "2024-04-06",
"timestamp": 1712275200,
"values": [
0,
15
]
}
],
"averages": [
65,
48
]
}
]
}
]
}
]
}
In the items array, you will find the dataforseo_trends_graph element, which contains the keywords you specified. This element itself includes the data array, where you can locate the relative keyword popularity rates at specific timestamps within the given time range. Additionally, at the end of the request, there is an averages array that provides estimated keyword popularity values averaged over the entire time range.
2Enrich your keyword research tools with up-to-date trends data
Estimated search interest data can be a valuable addition to your existing marketing tools and product offerings. API trends data can be seamlessly integrated into existing SEO tools, plugins, and keyword rank-tracking solutions. In addition to providing data on search volume and other keyword metrics, you can empower your customers to monitor search trends in real time and gain insights into regional search interest and demographic breakdowns.
For instance, you could create a preview keyword research tool based on the keyword trends data. This tool would allow your customers to analyze the popularity of certain search queries across different regions, age groups, and genders before purchasing a full-scale product.
To illustrate how this could work, let’s examine an example of diagrams generated from an API request.
The first diagram shows which states in the US had the highest popularity for the search query “sushi delivery” during the last 90 days. The second diagram displays the distribution of search interest for this query between male and female users across various age groups.
Obtaining the values for both diagrams is possible with the “Merged Data” endpoint of the DataForSEO API.
POST https://api.dataforseo.com/v3/keywords_data/dataforseo_trends/merged_data/live
Example request
[
{
"keywords": [
"sushi delivery"
],
"location_name": "United States",
"date_from": "2024-01-11",
"date_to": "2024-04-06",
"type": "web"
}
]
Below is a response example you will get
{
"version": "0.1.20240313",
"status_code": 20000,
"status_message": "Ok.",
"time": "4.8994 sec.",
"cost": 0.005,
"tasks_count": 1,
"tasks_error": 0,
"tasks": [
{
"id": "04101640-1535-0575-0000-a8964098467b",
"status_code": 20000,
"status_message": "Ok.",
"time": "4.8385 sec.",
"cost": 0.005,
"result_count": 1,
"path": [
"v3",
"keywords_data",
"dataforseo_trends",
"merged_data",
"live"
],
"data": {
"api": "keywords_data",
"function": "merged_data",
"se": "dataforseo_trends",
"keywords": [
"sushi delivery"
],
"location_name": "United States",
"date_from": "2024-01-11",
"date_to": "2024-04-06",
"type": "web"
},
"result": [
{
"keywords": [
"sushi delivery"
],
"type": "trends",
"location_code": 2840,
"language_code": null,
"datetime": "2024-04-10 13:40:08 +00:00",
"items_count": 3,
"items": [
{
"position": 1,
"type": "dataforseo_trends_graph",
"keywords": [
"sushi delivery"
],
"data": [
{
"date_from": "2024-01-11",
"date_to": "2024-01-11",
"timestamp": 1704931200,
"values": [
2
]
},
{
"date_from": "2024-01-12",
"date_to": "2024-01-12",
"timestamp": 1705017600,
"values": [
2
]
},
{
"date_from": "2024-01-13",
"date_to": "2024-01-13",
"timestamp": 1705104000,
"values": [
10
]
},
{
"date_from": "2024-01-14",
"date_to": "2024-01-14",
"timestamp": 1705190400,
"values": [
7
]
},
{
"date_from": "2024-01-15",
"date_to": "2024-01-15",
"timestamp": 1705276800,
"values": [
9
]
},
{
"date_from": "2024-01-16",
"date_to": "2024-01-16",
"timestamp": 1705363200,
"values": [
4
]
},
{
"date_from": "2024-01-17",
"date_to": "2024-01-17",
"timestamp": 1705449600,
"values": [
3
]
},
{
"date_from": "2024-01-18",
"date_to": "2024-01-18",
"timestamp": 1705536000,
"values": [
18
]
},
{
"date_from": "2024-01-19",
"date_to": "2024-01-19",
"timestamp": 1705622400,
"values": [
9
]
},
{
"date_from": "2024-01-20",
"date_to": "2024-01-20",
"timestamp": 1705708800,
"values": [
3
]
},
{
"date_from": "2024-01-21",
"date_to": "2024-01-21",
"timestamp": 1705795200,
"values": [
2
]
},
{
"date_from": "2024-01-22",
"date_to": "2024-01-22",
"timestamp": 1705881600,
"values": [
4
]
},
{
"date_from": "2024-01-23",
"date_to": "2024-01-23",
"timestamp": 1705968000,
"values": [
2
]
},
{
"date_from": "2024-01-24",
"date_to": "2024-01-24",
"timestamp": 1706054400,
"values": [
3
]
},
{
"date_from": "2024-01-25",
"date_to": "2024-01-25",
"timestamp": 1706140800,
"values": [
14
]
},
{
"date_from": "2024-01-26",
"date_to": "2024-01-26",
"timestamp": 1706227200,
"values": [
4
]
},
{
"date_from": "2024-01-27",
"date_to": "2024-01-27",
"timestamp": 1706313600,
"values": [
2
]
},
{
"date_from": "2024-01-28",
"date_to": "2024-01-28",
"timestamp": 1706400000,
"values": [
0
]
},
{
"date_from": "2024-01-29",
"date_to": "2024-01-29",
"timestamp": 1706486400,
"values": [
21
]
},
{
"date_from": "2024-01-30",
"date_to": "2024-01-30",
"timestamp": 1706572800,
"values": [
4
]
},
{
"date_from": "2024-01-31",
"date_to": "2024-01-31",
"timestamp": 1706659200,
"values": [
2
]
},
{
"date_from": "2024-02-01",
"date_to": "2024-02-01",
"timestamp": 1706745600,
"values": [
2
]
},
{
"date_from": "2024-02-02",
"date_to": "2024-02-02",
"timestamp": 1706832000,
"values": [
2
]
},
{
"date_from": "2024-02-03",
"date_to": "2024-02-03",
"timestamp": 1706918400,
"values": [
2
]
},
{
"date_from": "2024-02-04",
"date_to": "2024-02-04",
"timestamp": 1707004800,
"values": [
5
]
},
{
"date_from": "2024-02-05",
"date_to": "2024-02-05",
"timestamp": 1707091200,
"values": [
12
]
},
{
"date_from": "2024-02-06",
"date_to": "2024-02-06",
"timestamp": 1707177600,
"values": [
3
]
},
{
"date_from": "2024-02-07",
"date_to": "2024-02-07",
"timestamp": 1707264000,
"values": [
13
]
},
{
"date_from": "2024-02-08",
"date_to": "2024-02-08",
"timestamp": 1707350400,
"values": [
5
]
},
{
"date_from": "2024-02-09",
"date_to": "2024-02-09",
"timestamp": 1707436800,
"values": [
3
]
},
{
"date_from": "2024-02-10",
"date_to": "2024-02-10",
"timestamp": 1707523200,
"values": [
6
]
},
{
"date_from": "2024-02-11",
"date_to": "2024-02-11",
"timestamp": 1707609600,
"values": [
23
]
},
{
"date_from": "2024-02-12",
"date_to": "2024-02-12",
"timestamp": 1707696000,
"values": [
19
]
},
{
"date_from": "2024-02-13",
"date_to": "2024-02-13",
"timestamp": 1707782400,
"values": [
5
]
},
{
"date_from": "2024-02-14",
"date_to": "2024-02-14",
"timestamp": 1707868800,
"values": [
3
]
},
{
"date_from": "2024-02-15",
"date_to": "2024-02-15",
"timestamp": 1707955200,
"values": [
2
]
},
{
"date_from": "2024-02-16",
"date_to": "2024-02-16",
"timestamp": 1708041600,
"values": [
3
]
},
{
"date_from": "2024-02-17",
"date_to": "2024-02-17",
"timestamp": 1708128000,
"values": [
1
]
},
{
"date_from": "2024-02-18",
"date_to": "2024-02-18",
"timestamp": 1708214400,
"values": [
3
]
},
{
"date_from": "2024-02-19",
"date_to": "2024-02-19",
"timestamp": 1708300800,
"values": [
9
]
},
{
"date_from": "2024-02-20",
"date_to": "2024-02-20",
"timestamp": 1708387200,
"values": [
3
]
},
{
"date_from": "2024-02-21",
"date_to": "2024-02-21",
"timestamp": 1708473600,
"values": [
2
]
},
{
"date_from": "2024-02-22",
"date_to": "2024-02-22",
"timestamp": 1708560000,
"values": [
1
]
},
{
"date_from": "2024-02-23",
"date_to": "2024-02-23",
"timestamp": 1708646400,
"values": [
3
]
},
{
"date_from": "2024-02-24",
"date_to": "2024-02-24",
"timestamp": 1708732800,
"values": [
2
]
},
{
"date_from": "2024-02-25",
"date_to": "2024-02-25",
"timestamp": 1708819200,
"values": [
4
]
},
{
"date_from": "2024-02-26",
"date_to": "2024-02-26",
"timestamp": 1708905600,
"values": [
15
]
},
{
"date_from": "2024-02-27",
"date_to": "2024-02-27",
"timestamp": 1708992000,
"values": [
10
]
},
{
"date_from": "2024-02-28",
"date_to": "2024-02-28",
"timestamp": 1709078400,
"values": [
5
]
},
{
"date_from": "2024-02-29",
"date_to": "2024-02-29",
"timestamp": 1709164800,
"values": [
2
]
},
{
"date_from": "2024-03-01",
"date_to": "2024-03-01",
"timestamp": 1709251200,
"values": [
2
]
},
{
"date_from": "2024-03-02",
"date_to": "2024-03-02",
"timestamp": 1709337600,
"values": [
2
]
},
{
"date_from": "2024-03-03",
"date_to": "2024-03-03",
"timestamp": 1709424000,
"values": [
2
]
},
{
"date_from": "2024-03-04",
"date_to": "2024-03-04",
"timestamp": 1709510400,
"values": [
3
]
},
{
"date_from": "2024-03-05",
"date_to": "2024-03-05",
"timestamp": 1709596800,
"values": [
2
]
},
{
"date_from": "2024-03-06",
"date_to": "2024-03-06",
"timestamp": 1709683200,
"values": [
2
]
},
{
"date_from": "2024-03-07",
"date_to": "2024-03-07",
"timestamp": 1709769600,
"values": [
4
]
},
{
"date_from": "2024-03-08",
"date_to": "2024-03-08",
"timestamp": 1709856000,
"values": [
47
]
},
{
"date_from": "2024-03-09",
"date_to": "2024-03-09",
"timestamp": 1709942400,
"values": [
7
]
},
{
"date_from": "2024-03-10",
"date_to": "2024-03-10",
"timestamp": 1710028800,
"values": [
9
]
},
{
"date_from": "2024-03-11",
"date_to": "2024-03-11",
"timestamp": 1710115200,
"values": [
6
]
},
{
"date_from": "2024-03-12",
"date_to": "2024-03-12",
"timestamp": 1710201600,
"values": [
1
]
},
{
"date_from": "2024-03-13",
"date_to": "2024-03-13",
"timestamp": 1710288000,
"values": [
1
]
},
{
"date_from": "2024-03-14",
"date_to": "2024-03-14",
"timestamp": 1710374400,
"values": [
2
]
},
{
"date_from": "2024-03-15",
"date_to": "2024-03-15",
"timestamp": 1710460800,
"values": [
20
]
},
{
"date_from": "2024-03-16",
"date_to": "2024-03-16",
"timestamp": 1710547200,
"values": [
3
]
},
{
"date_from": "2024-03-17",
"date_to": "2024-03-17",
"timestamp": 1710633600,
"values": [
1
]
},
{
"date_from": "2024-03-18",
"date_to": "2024-03-18",
"timestamp": 1710720000,
"values": [
1
]
},
{
"date_from": "2024-03-19",
"date_to": "2024-03-19",
"timestamp": 1710806400,
"values": [
2
]
},
{
"date_from": "2024-03-20",
"date_to": "2024-03-20",
"timestamp": 1710892800,
"values": [
2
]
},
{
"date_from": "2024-03-21",
"date_to": "2024-03-21",
"timestamp": 1710979200,
"values": [
3
]
},
{
"date_from": "2024-03-22",
"date_to": "2024-03-22",
"timestamp": 1711065600,
"values": [
3
]
},
{
"date_from": "2024-03-23",
"date_to": "2024-03-23",
"timestamp": 1711152000,
"values": [
2
]
},
{
"date_from": "2024-03-24",
"date_to": "2024-03-24",
"timestamp": 1711238400,
"values": [
0
]
},
{
"date_from": "2024-03-25",
"date_to": "2024-03-25",
"timestamp": 1711324800,
"values": [
1
]
},
{
"date_from": "2024-03-26",
"date_to": "2024-03-26",
"timestamp": 1711411200,
"values": [
1
]
},
{
"date_from": "2024-03-27",
"date_to": "2024-03-27",
"timestamp": 1711497600,
"values": [
4
]
},
{
"date_from": "2024-03-28",
"date_to": "2024-03-28",
"timestamp": 1711584000,
"values": [
4
]
},
{
"date_from": "2024-03-29",
"date_to": "2024-03-29",
"timestamp": 1711670400,
"values": [
1
]
},
{
"date_from": "2024-03-30",
"date_to": "2024-03-30",
"timestamp": 1711756800,
"values": [
0
]
},
{
"date_from": "2024-03-31",
"date_to": "2024-03-31",
"timestamp": 1711843200,
"values": [
2
]
},
{
"date_from": "2024-04-01",
"date_to": "2024-04-01",
"timestamp": 1711929600,
"values": [
28
]
},
{
"date_from": "2024-04-02",
"date_to": "2024-04-02",
"timestamp": 1712016000,
"values": [
17
]
},
{
"date_from": "2024-04-03",
"date_to": "2024-04-03",
"timestamp": 1712102400,
"values": [
91
]
},
{
"date_from": "2024-04-04",
"date_to": "2024-04-04",
"timestamp": 1712188800,
"values": [
100
]
},
{
"date_from": "2024-04-05",
"date_to": "2024-04-06",
"timestamp": 1712275200,
"values": [
17
]
}
],
"averages": [
8
]
},
{
"position": 2,
"type": "subregion_interests",
"keywords": [
"sushi delivery"
],
"interests": [
{
"keyword": "sushi delivery",
"values": [
{
"geo_id": null,
"geo_name": "California",
"value": 9
},
{
"geo_id": null,
"geo_name": "Florida",
"value": 24
},
{
"geo_id": null,
"geo_name": "Georgia",
"value": 21
},
{
"geo_id": null,
"geo_name": "Illinois",
"value": 22
},
{
"geo_id": null,
"geo_name": "Iowa",
"value": 62
},
{
"geo_id": null,
"geo_name": "Kansas",
"value": 100
},
{
"geo_id": null,
"geo_name": "Louisiana",
"value": 81
},
{
"geo_id": null,
"geo_name": "Maryland",
"value": 36
},
{
"geo_id": null,
"geo_name": "New York",
"value": 26
},
{
"geo_id": null,
"geo_name": "Pennsylvania",
"value": 15
},
{
"geo_id": null,
"geo_name": "Texas",
"value": 11
}
]
}
],
"interests_comparison": null
},
{
"position": 3,
"type": "demography",
"keywords": [
"sushi delivery"
],
"demography": {
"age": [
{
"keyword": "sushi delivery",
"values": [
{
"type": "18-24",
"value": 100
},
{
"type": "25-34",
"value": 80
},
{
"type": "35-44",
"value": 51
},
{
"type": "45-54",
"value": 48
}
]
}
],
"gender": [
{
"keyword": "sushi delivery",
"values": [
{
"type": "female",
"value": 83
},
{
"type": "male",
"value": 100
}
]
}
]
},
"demography_comparison": null
}
]
}
]
}
]
}
In the items array of the response, you will find the subregion_interests element, which contains subregional keyword popularity data for each specified term, as well as the names of the corresponding subregions. Additionally, within the demography element, you can locate the age array that provides the distribution of keyword popularity across different age groups. Furthermore, the gender array presents the breakdown of keyword popularity values by gender.
As you can observe, a single straightforward request allows you to obtain all the essential search trends data you require, presented in a convenient and accessible format.
Conclusion
If you’re seeking detailed and unique insights into search trends data that go beyond Google Trends’ capabilities, the DataForSEO Trends API is an excellent alternative to consider. It is particularly valuable if you require extensive keyword popularity data for large-scale projects at an affordable price point. Moreover, incorporating search trends insights can significantly enhance the value proposition of your existing keyword research tools and related offerings.
Besides, with DataForSEO trends API you can get bulk trends data swiftly and don`t worry about request limits and system availability. You can make up to 2000 API calls per minute and if you want to make more, we will raise the limit on your demand.
Finally, you can easily integrate DataForSEO Trends API with your website or application – it is compatible with almost all programming languages and everything is explained in detail in our documentation. If you want to test the capabilities of our API, try it for free and get acquainted with the DataForSEO Trends Tool as well.
