'*******' 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.

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.

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

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.

Throughout the second semester of 2021, "nails" was the most searched beauty-related keyword on Google in the United Kingdom (UK), based on total search volume through Google UK. According to the data, during this period, "nails" had a total number of over *** thousand searches. "nail designs" attracted another *** thousand searches each on Google UK.

In 2025, the keyword generating the most traffic to the gymshark.com website was 'gymshark' with nearly ************ searches in June of that year. 'gym shark' and 'gymshark canada' followed.

Ranked by Keyword the Web Search Data consists of:

• 25+ consumer categories
• Insights from Top Brands, Top Products, Click Share, Conversion Share, Product Competitors per Search Term and Technical Product Specifications
• 2+ years of historical coverage
• 13+ markets

As of January 2020, ** percent of all U.S. online search queries contained *** keywords. Three word search terms accounted for ***** percent of searches. Queries up to ***** words accounted for over ** percent of online searches in the United States.

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.

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.

An analysis conducted between November 2024 and April 2025 looked at the share of travel keywords that triggered AI Overviews on Google. According to the study, the share of travel keywords searched via desktop that triggered an AI Overview increased from *** percent in November 2024 to **** percent in April 2025. Over the same period, the share of keywords searched via mobile that generated AI Overviews grew by *** percentage points.

'*******' was the most searched keyword on Google in the United Kingdom between January and March 2025, with an average monthly volume of ***** million searches over the researched period. Over **** million website visits were generated by this keyword, which accounted for **** percent of the country's searches. '**************' ranked second, with ** million searches and **** million website visits per month during the same time period, followed by numerous queries related to football and the championship. Additionally, "*******" was the tenth most frequently searched keyword on Google in the United Kingdom.**********************************************************************************************************************************************************************

Keywords are widely recognized as pivotal in conveying the central idea of academic articles. In this article, we construct a weighted and dynamic keyword co-occurrence network and propose a latent space model for analyzing it. Our model has two special characteristics. First, it is applicable to weighted networks; however, most previous models were primarily designed for unweighted networks. Simply replacing the frequency of keyword co-occurrence with binary values would result in a significant loss of information. Second, our model can handle the situation where network nodes evolve over time, and assess the effect of new nodes on network connectivity. We use the projected gradient descent algorithm to estimate the latent positions and establish the theoretical properties of the estimators. In the real data application, we study the keyword co-occurrence network within the field of statistics. We identify popular keywords over the whole period as well as within each time period. For keyword pairs, our model provides a new way to assess the association between them. Finally, we observe that the interest of statisticians in emerging research areas has gradually grown in recent years. Supplementary materials for this article are available online.

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.

Datos brings to market anonymized, at scale, consolidated privacy-secured datasets with granularity rarely found in market. Datos offers access to the desktop and mobile browsing behavior for millions of users across the globe, packaged into clean, easy to understand data products and reports for use by our clients.

The Datos Keywords Feed is an aggregated accounting of all observed searches executed on up to nine major search properties worldwide, with both raw and projected statistics available.

According to Google's search data from 2024, the most common queries by Indians were ****** and ********. These common search queries provide an insight into the Indian content consumption patterns. Video content was sought actively, also evident by the fact that the video-sharing platform had the highest share of monthly social network users in the country that same year. Optimization of video streaming Not surprisingly, video content is witnessing exponential growth in recent years. It is evident in the fact that video streaming accounts for a major share of online mobile traffic across the nation. Recent trends suggest an increase in consumption of video over graphic or text content. Hence, a sound implementation of SEO in videos has become a necessity for a successful content creating channel. One of the major optimization strategies is to cater to the demographic of the nation, which incorporates efficient description, headline, and tag implementation. Keyword search trends Searches related to local preferences are gaining momentum, rendering local SEO invaluable to promoting visibility of the content. Phrases like “near me” and “close to me” have witnessed a significant increase in their frequency of appearances in queries. Since the coronavirus (COVID-19) outbreak, the latter part of 2020 has seen a significant rise in the usage of queries related to the pandemic. This is testament to the influence of recent events on keywords and optimized phrases for improved channel visibility.

This dataset presents resources that summarize agency workforce characteristics for classified and unclassified employees separately.

Keywords are commonly used to describe the main concepts of a journal article. Hence, do they follow a statistical distribution in terms of frequency of occurrence in the article? This work sets out to elucidate if frequency of occurrence of keywords follows a statistical distribution as well as whether the authors draw on the statistical occurrence of the concept to propose keywords. Using an in-house MATLAB machine reading software, the frequency of occurrence of keywords in the main text of 30 journal articles was tabulated. Results reveal that at least one of the authors’ proposed keywords is of high frequency of occurrence. However, occurrence frequency of other keywords largely depends on the scholastic style of the author where there is a substantial number of articles with keywords of low frequency of occurrence. These are likely important concepts of low frequency of occurrence.

A study conducted in Italy in 2024 shows the most searched product on Amazon.it within the skincare session. Facial masks and facial cleansers had the biggest numbers of searches, with ****** and ****** respectively. Korean products had over ****** searches.

This repository includes the data for reproducing the results of the paper: "Self-Learning for Personalized Keyword Spotting on Ultra-Low-Power Audio Sensors". In this work, we recorded a dataset of speech samples with our microphone sensor after replaying a subset of data from the HeySnips dataset using a speaker. The collected speech data (total of 400 samples) are split between a testset and a trainset, both including "Hey Snips" utterances and non-"Hey Snips" utterances. In particular, the data of the testset is composed by recordings from 20 random speakers from the original testset. After the recording, the data were fed to our DNN models deployed on devices. Initially, a per-speaker prototype vector is computed by feeding three audio recordings of the target keywords. Next, the audio tracks of the training set are processed with a sliding window approach to compute the distance with respect to the prototype and assign pseudo-labels for the self-learning task. The dataset is, therefore, composed of two main partitions. First, the "recorded_speech_data" includes the audio recordings. Note that this dataset is under restricted access to not violate the terms of access of the original dataset. Second, the "processed_outputs" includes the output of the processing, i.e. the measured distances. By using the dataset in combination with the associated code, every user will be able to reproduce the results of the paper.