'*******' was the most searched keyword on Google in the United States between January and March 2025, with an average monthly volume of ****** million searches over the researched period. "***" ranked second with a search volume of ***** million searches. The keyword for "nfl" came in third, making up to ***** million searches.

"Squid Game" was the keyword that had the highest maximum monthly search volume in 2021, with 101 million online searches worldwide in its peak month. The second most trending keyword based on its peak search volume was "Christian Eriksen." Eriksen is a Danish football player who experienced cardiac arrest during the Euro 2020 Denmark against Finland match, on June 12, 2021. Another popular keyword search on Google was Queen Elisabeth's late husband "Prince Phillip," with a peak of 37.2 million searches. Another popular topic related to the British royal family was Prince Harry and Meghan Markle's interview with Oprah Winfrey in March 2021. This was the first big interview after the couple decided to step back as senior royals, and queries on the topic went up to 1.2 million searches on Google in 2021.

You can check the fields description in the documentation: current Keyword database: https://docs.dataforseo.com/v3/databases/google/keywords/?bash; Historical Keyword database: https://docs.dataforseo.com/v3/databases/google/history/keywords/?bash. You don’t have to download fresh data dumps in JSON or CSV – we can deliver data straight to your storage or database. We send terrabytes of data to dozens of customers every month using Amazon S3, Google Cloud Storage, Microsoft Azure Blob, Eleasticsearch, and Google Big Query. Let us know if you’d like to get your data to any other storage or database.

DataForSEO Labs API offers three powerful keyword research algorithms and historical keyword data:

• Related Keywords from the “searches related to” element of Google SERP. • Keyword Suggestions that match the specified seed keyword with additional words before, after, or within the seed key phrase. • Keyword Ideas that fall into the same category as specified seed keywords. • Historical Search Volume with current cost-per-click, and competition values.

Based on in-market categories of Google Ads, you can get keyword ideas from the relevant Categories For Domain and discover relevant Keywords For Categories. You can also obtain Top Google Searches with AdWords and Bing Ads metrics, product categories, and Google SERP data.

You will find well-rounded ways to scout the competitors:

• Domain Whois Overview with ranking and traffic info from organic and paid search. • Ranked Keywords that any domain or URL has positions for in SERP. • SERP Competitors and the rankings they hold for the keywords you specify. • Competitors Domain with a full overview of its rankings and traffic from organic and paid search. • Domain Intersection keywords for which both specified domains rank within the same SERPs. • Subdomains for the target domain you specify along with the ranking distribution across organic and paid search. • Relevant Pages of the specified domain with rankings and traffic data. • Domain Rank Overview with ranking and traffic data from organic and paid search. • Historical Rank Overview with historical data on rankings and traffic of the specified domain from organic and paid search. • Page Intersection keywords for which the specified pages rank within the same SERP.

All DataForSEO Labs API endpoints function in the Live mode. This means you will be provided with the results in response right after sending the necessary parameters with a POST request.

The limit is 2000 API calls per minute, however, you can contact our support team if your project requires higher rates.

We offer well-rounded API documentation, GUI for API usage control, comprehensive client libraries for different programming languages, free sandbox API testing, ad hoc integration, and deployment support.

We have a pay-as-you-go pricing model. You simply add funds to your account and use them to get data. The account balance doesn't expire.

Here’s a breakdown of Semrush's keyword database growth since 2017.

"*******" was the most frequently searched keyword on Google worldwide, with over ***** million monthly online searches during the analyzed period of January to March in 2025. Furthermore, the search resulted in more than ***** million website visits, or more than **** percent of all traffic. With *** million monthly searches, "***" was the second most popular keyword, and "***********" came in third place with about ****** million searches per month.

You can check the fields description in the documentation: current Full database: https://docs.dataforseo.com/v3/databases/google/full/?bash; Historical Full database: https://docs.dataforseo.com/v3/databases/google/history/full/?bash.

Full Google Database is a combination of the Advanced Google SERP Database and Google Keyword Database.

Google SERP Database offers millions of SERPs collected in 67 regions with most of Google’s advanced SERP features, including featured snippets, knowledge graphs, people also ask sections, top stories, and more.

Google Keyword Database encompasses billions of search terms enriched with related Google Ads data: search volume trends, CPC, competition, and more.

This database is available in JSON format only.

You don’t have to download fresh data dumps in JSON – we can deliver data straight to your storage or database. We send terrabytes of data to dozens of customers every month using Amazon S3, Google Cloud Storage, Microsoft Azure Blob, Eleasticsearch, and Google Big Query. Let us know if you’d like to get your data to any other storage or database.

Abstract (our paper)

The frequency of a web search keyword generally reflects the degree of public interest in a particular subject matter. Search logs are therefore useful resources for trend analysis. However, access to search logs is typically restricted to search engine providers. In this paper, we investigate whether search frequency can be estimated from a different resource such as Wikipedia page views of open data. We found frequently searched keywords to have remarkably high correlations with Wikipedia page views. This suggests that Wikipedia page views can be an effective tool for determining popular global web search trends.

Data

personal-name.txt.gz:
The first column is the Wikipedia article id, the second column is the search keyword, the third column is the Wikipedia article title, and the fourth column is the total of page views from 2008 to 2014.

personal-name_data_google-trends.txt.gz, personal-name_data_wikipedia.txt.gz:
The first column is the period to be collected, the second column is the source (Google or Wikipedia), the third column is the Wikipedia article id, the fourth column is the search keyword, the fifth column is the date, and the sixth column is the value of search trend or page view.

Publication

This data set was created for our study. If you make use of this data set, please cite:
Mitsuo Yoshida, Yuki Arase, Takaaki Tsunoda, Mikio Yamamoto. Wikipedia Page View Reflects Web Search Trend. Proceedings of the 2015 ACM Web Science Conference (WebSci '15). no.65, pp.1-2, 2015.
http://dx.doi.org/10.1145/2786451.2786495
http://arxiv.org/abs/1509.02218 (author-created version)

Note

The raw data of Wikipedia page views is available in the following page.
http://dumps.wikimedia.org/other/pagecounts-raw/

Keyword Research Tools Market Outlook

The global keyword research tools market size was valued at approximately USD 500 million in 2023 and is projected to reach USD 1.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.5% during the forecast period. This robust growth is driven by the increasing importance of search engine optimization (SEO) and online marketing strategies across various industries.

One of the significant growth drivers in the keyword research tools market is the rising demand for digital marketing. With the proliferation of online businesses and the ever-increasing importance of having a strong online presence, companies are investing heavily in SEO and content marketing strategies. Keyword research tools are essential for identifying high-value keywords that can drive traffic and generate leads, making them indispensable in a marketer's toolkit. Moreover, the shift towards e-commerce and digital platforms, accelerated by the COVID-19 pandemic, has further emphasized the need for effective keyword research tools.

Technological advancements are another critical factor propelling the growth of the keyword research tools market. The integration of artificial intelligence (AI) and machine learning (ML) into these tools has significantly enhanced their functionality and usability. AI-powered keyword research tools can analyze vast amounts of data to provide more accurate and actionable insights. They can predict keyword trends, understand user intent, and suggest long-tail keywords, thereby optimizing the content creation process. These advancements are attracting more users to adopt sophisticated keyword research tools, thereby driving market growth.

The increasing adoption of content marketing strategies by businesses of all sizes is another major growth factor. Content marketing has become a fundamental aspect of a company's digital strategy, aiming to attract, engage, and retain customers by creating and sharing valuable content. Keyword research tools help marketers understand what their target audience is searching for, enabling them to create content that meets their needs and preferences. This targeted approach not only improves search engine rankings but also enhances customer engagement and conversion rates, fueling the demand for keyword research tools.

In the realm of digital marketing, understanding buyer intent is crucial for crafting effective strategies. Buyer Intent Tools have emerged as vital instruments in this context, enabling marketers to decipher the underlying motivations and needs of potential customers. By analyzing user behavior, search patterns, and engagement metrics, these tools provide insights into what drives consumer decisions. This understanding allows businesses to tailor their content and marketing efforts to align with the specific needs and preferences of their target audience, ultimately enhancing conversion rates and customer satisfaction. As the digital landscape becomes increasingly competitive, the ability to predict and respond to buyer intent is becoming a key differentiator for successful marketing campaigns.

Regionally, North America holds the largest market share in the keyword research tools market, driven by the high concentration of digital marketing agencies, advanced technological infrastructure, and early adoption of new marketing technologies. Europe follows closely, with significant growth driven by the increasing focus on online marketing and e-commerce. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, attributed to the rapid digital transformation, increasing internet penetration, and growing number of small and medium enterprises (SMEs) adopting digital marketing strategies. Latin America and the Middle East & Africa are also experiencing steady growth, supported by the expanding digital economy and increasing awareness of the benefits of SEO and content marketing.

Type Analysis

The keyword research tools market can be segmented by type into free and paid tools. Free keyword research tools are widely used by individual bloggers, freelancers, and small enterprises due to their cost-effectiveness. These tools provide basic functionalities such as keyword suggestions, search volume data, and competition analysis, which are sufficient for smaller-scale SEO and content marketing efforts. However, their limited features and capabilities can be a constraint for more comprehensive digital marketing s

Taichung City Open Data Platform keyword ranking statistics, monthly statistics

KEYWORD SEARCH IN TEXT CUBE: FINDING TOP-K RELEVANT CELLS BOLIN DING, YINTAO YU, BO ZHAO, CINDY XIDE LIN, JIAWEI HAN, AND CHENGXIANG ZHAI Abstract. We study the problem of keyword search in a data cube with text-rich dimension(s) (so-called text cube). The text cube is built on a multidimensional text database, where each row is associated with some text data (e.g., a document) and other structural dimensions (attributes). A cell in the text cube aggregates a set of documents with matching attribute values in a subset of dimensions. A cell document is the concatenation of all documents in a cell. Given a keyword query, our goal is to find the top-k most relevant cells (ranked according to the relevance scores of cell documents w.r.t. the given query) in the text cube. We define a keyword-based query language and apply IR-style relevance model for scoring and ranking cell documents in the text cube. We propose two efficient approaches to find the top-k answers. The proposed approaches support a general class of IR-style relevance scoring formulas that satisfy certain basic and common properties. One of them uses more time for pre-processing and less time for answering online queries; and the other one is more efficient in pre-processing and consumes more time for online queries. Experimental studies on the ASRS dataset are conducted to verify the efficiency and effectiveness of the proposed approaches.

As of February 2022, it appeared that mobile apps spotting specific target keywords in the titles ranked higher in the Google Play Store results page after a user prompt research. According to industry sources, while it is not clear what allows certain apps to appear among the first results on the Apple App Store results page, app store optimization practices can help developers increase their app visibility and users' interactions.

A dataset containing keywords, search volume, keyword difficulty (KD), and user intent for various HR services including executive search, staffing, HR outsourcing, payroll, and training.

Between June 2022 and March 2023, the traffic volume for the keyword "AI" has tripled, going from around 7.9 million monthly searches to more than 30.4 million during the last month of the measured period. General interest in artificial intelligence (AI) has exploded in markets like the United States by the end of 2022. Likewise, interest for the application programming interfaces (API's) and plugins of artificial intelligence solutions, especially those of ChatGPT, has also seen a major increase since the release of the tool in November of 2022.

The artificial intelligence market

Valued at around 142.3 billion U.S. dollars in 2022, the artificial intelligence market is one the most promising tech segments for the rest of the decade, with more than five billion U.S. dollars invested in startups - the most notable being the Californian company OpenAI and its flagship application ChatGPT. Disruptive as it is, the adoption of AI has already sparked an alert for several industries, likely to affect job markets and thus raising concerns about cybercrime and other online misdeeds.

The future of online search?

Of most industries, the impact of the new tool developed by OpenAI may be felt by the online search market like a global earthquake. With chatbots providing search results in a dialogue format, the trend of AI-powered search engines unleashed by ChatGPT threw giant companies like Google and Microsoft into a race with startups and other competitors to present the best candidate for this disruptive (and experimental) online solution.

This dataset contains the 500 most common keywords in Swedish Open Cultural Heritage (SOCH) with co-occurring counts in relation to each other. The following metadata fields appear in the dataset:keyword (text), keyword_id, external_keyword_id (the co-occurring keyword), shared_items (co-occurring count)The data were computed from SOCH in April 2018. No corrections were made to the keywords. The keyword were however extracted from the SOCH Solr index and therefore differ from the keywords in the original source.

First of all, Amazon product datasets are indispensable for reverse engineering your rivals. For example, you can collect a list of keywords you already rank for or want to, and go through DataForSEO Amazon Products Database to find other sellers appearing as the top results for these terms.

Next, you can narrow down the scope of your contenders to those performing the best. To do so, you can filter out sellers who won the “Amazon’s Choice” and those whose products got listed multiple times on the first page.

Once you’ve compiled the final list of your challengers, Amazon Products Database will help you to quickly examine product titles, descriptions, prices, images, and other details that will let you grasp the main contributors to your competitors’ success. Once you’ve figured that out, you can start optimizing your product listings and pricing strategies to increase conversions.

However, the number of use cases for Amazon product data isn’t limited to competitor analysis. It can be applied to monitoring product rankings, running price comparisons, and more.

The (unheralded) first step in many applications of automated text analysis involves selecting keywords to choose documents from a large text corpus for further study. Although all substantive results depend on this choice, researchers usually pick keywords in ad hoc ways that are far from optimal and usually biased. Most seem to think that keyword selection is easy, since they do Google searches every day, but we demonstrate that humans perform exceedingly poorly at this basic task. We offer a better approach, one that also can help with following conversations where participants rapidly innovate language to evade authorities, seek political advantage, or express creativity; generic web searching; eDiscovery; look-alike modeling; industry and intelligence analysis; and sentiment and topic analysis. We develop a computer-assisted (as opposed to fully automated or human-only) statistical approach that suggests keywords from available text without needing structured data as inputs. This framing poses the statistical problem in a new way, which leads to a widely applicable algorithm. Our specific approach is based on training classifiers, extracting information from (rather than correcting) their mistakes, and summarizing results with easy-to-understand Boolean search strings. We illustrate how the technique works with analyses of English texts about the Boston Marathon Bombings, Chinese social media posts designed to evade censorship, and others.

This file contains total hits per keyword expressed as percentage of total hits for the eight domains of the human well-being index. Additional categorical data is given for each community planning document based on publicly available demographic data for the community. These demographic data include population size, proportion of population in a series of categories: education level, median income, and race. Additional categorical variables are community assignment based on a community typology. A full description of the community typology can be found in the associated supplementary material. This dataset is associated with the following publication: Fulford, R., M. Russell, J. Harvey, and M. Harwell. Sustainability at the community level: Searching for common ground as a part of a national strategy for decision support. U.S. Environmental Protection Agency, Washington, DC, USA, 2016.

This dataset contains raw data and statistical summaries that reflect use of keywords in LTER Datasets in May 2018 and 2006. Specific summaries include: Number of uses and number sites by keyword (LTERVocabKeywordSummary.csv), Summary of keyword use by data package (LTERVocabDataPackageSummary.csv), Summary of Keyword Use by LTER Site in 2018(LTERVocabSiteSummary.csv), Summary of Keyword Use by LTER Site in 2006(KeyStats2006.csv). Raw data includes XML files containing the US LTER Thesaurus in Moodle format and the ResultSet containing the information for each dataset from the Environmental Data Initiative PASTA repository.

In this blog post, we'll go through the latest Semrush statistics to show why Semrush is so popular.