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.

Search engines, which collect, organize and display knowledge of the internet, are the backbone of the information age and have helped popularize the ad-supported attention economy that prevails throughout the internet. From 2019 to 2024, spending on internet advertising has maintained strong momentum as consumer demand for internet access continued to surge, driven by the adoption of LTE, 5G and unlimited mobile data plans. Despite COVID-19 depressing total advertising expenditure, digital advertising continued to grow as consumers practically lived online while stay-at-home orders were in place. As a result, search engine revenue from advertising is slated to mount at a CAGR of 10.4% to $287.5 billion, including an anticipated hike of 8.4% in 2024, with profit at 18.7%. The search engine industry is fundamentally differentiated from the rest of the economy by its advertising sales framework, market aggregation and high interconnection with other industries. While search is a consumer product, search revenue comes from a platform's desirability to advertisers, not users. Search platforms must balance providing the best search experience while integrating as many advertisements as possible. This difficult balance is challenging to achieve because advertising dollars tend to scale best on the leading search platform, increasing aggregation forces for search providers. The market leaders in search, Google and Microsoft, have met this balance by using advertising revenue to grow a suite of services designed to collect extensive behavior information on and off the search website. This data then targets ads to hyper-specific markets, funding the search business model. As the number of hours spent on the internet continues to mount, search engine revenue is poised to climb at a CAGR of 7.1% to $404.9 billion through the end of 2029. Advertisers will rely increasingly on search engine marketing due to its cost-effectiveness and efficiency advantages over traditional media. With proper analytics software installed, marketers can track which terms, advertisements and websites are the most effective, enabling incremental real-time tweaks and improvements in advertising campaigns. Artificial intelligence has promised to change the purpose of search from navigation to finding answers, which will change the structure of the internet, just as search engine providers have done many times before.

Based on a survey conducted in 2019 among internet users in the United States, the majority of adults (36 percent) admitted they would switch search engines if it meant getting better quality results. Furthermore, 33.7 percent stated that knowing their data was not being collected by a platform would also encourage them to make the switch. Other factors listed included 'having fewer ads' and a well designed interface. Overall, there was a noticeable lean toward search result quality and data privacy when it came to search engine selection.

Google leads despite user preference for increased privacy

Despite a strong consumer call for data protection, Google topped the list when it came to search engines with 93 percent of Americans surveyed reporting to having used the popular search giant at some point during the past 4 weeks. In comparison, the second most popular platform Yahoo! had only been used by 31 percent of those surveyed. Meanwhile DuckDuckGo, the search engine most known for protecting user data and search history had only been used by 8 percent. Mobile search figures lean even more in Google's favor. Here, a similar share (93 percent) of the market as of January 2021 belonged to Google, while approximately 3 percent was held by DuckDuckGo.

Growth expected for search advertising

With search engines playing a significant role in internet use be it on desktop or mobile, companies and search platforms alike are seeing an increased opportunity in the field of search engine advertising. Nationwide spend in the industry reached an impressive 58.2 billion U.S. dollars in 2020, and was forecast to further rise to 66.2 billion within the following year.

The goal of this research is to examine direct answers in Google web search engine. Dataset was collected using Senuto (https://www.senuto.com/). Senuto is as an online tool, that extracts data on websites visibility from Google search engine.

Dataset contains the following elements:

keyword,

number of monthly searches,

featured domain,

featured main domain,

featured position,

featured type,

featured url,

content,

content length.

Dataset with visibility structure has 743 798 keywords that were resulting in SERPs with direct answer.

SEO (Search Engine Optimization) Market size was valued at USD 279.38 Billion in 2024 and is projected to reach USD 85.06 USD Billion by 2031, growing at a CAGR of 17.68% during the forecast period 2024-2031.Global SEO (Search Engine Optimization) Market Drivers1. Innovation in TechnologyTechnological developments are a major factor propelling the SEO industry. Businesses must modify their SEO tactics due to the ongoing evolution of search engine algorithms by major companies such as Google, Bing, and Yahoo. For example, Google penalizes websites that use keyword stuffing and other manipulative approaches, and emphasizes the value of high-quality content through its periodic algorithm upgrades, Panda, Penguin, and Hummingbird. Furthermore, search engine algorithms have been further improved by the development of artificial intelligence (AI) and machine learning, which has improved their comprehension of human intent and context. The popularity of voice search, which is fueled by AI assistants like Siri, Alexa, and Google Assistant, has also increased, requiring SEO tactics that address natural language inquiries.2. Purchaser ConductConsumer behavior shifts have a big effect on the SEO industry. The fact that more people are using mobile devices for internet browsing than desktop ones has been a significant contributing factor. Due to this tendency, Google has created mobile-first indexing, in which a website's mobile version takes precedence over its desktop version when it comes to rankings. In addition, the significance of local SEO has been fueled by customers' growing reliance on local searches ("near me" queries). Nowadays, it's critical for businesses to maximize their online presence for local search results.3. Information and User InterfaceOne of the primary SEO pillars is still content. Being well-ranked in search engine results pages (SERPs) requires having material that is relevant, interesting, and of high quality while also meeting user needs. User experience (UX) is becoming more and more important as search engines incorporate measures like mobile friendliness, site architecture, and page load speed into their ranking algorithms. This tendency is demonstrated by the incorporation of Core Web Vitals into Google's ranking factors, which highlights the importance of a quick and easy user experience.4. The Environment of RegulationLaws and regulations pertaining to data privacy have an impact on the SEO sector. Strict guidelines on how businesses handle user data are imposed by laws like the California Consumer Privacy Act (CCPA) in the United States and the General Data Protection Regulation (GDPR) in Europe. These rules have an impact on SEO tactics, especially when it comes to user tracking and data collection. As a result, businesses must be more cautious and open about their online activities.5. Dynamics of CompetitionAnother important factor propelling the SEO market is the competitive environment. Businesses are fighting more fiercely for top SERP ranks as they realize how important it is to be seen online to increase traffic and sales. Investment in SEO tools and services is fueled by this competition, which propels market expansion. Specialized SEO agencies and consultancies are proliferating as a result of businesses looking to outperform rivals by enlisting the help of experts in SEO.6. Integration of Social MediaThe market is also driven by the interaction between SEO and social media. Likes, shares, and comments are examples of social signals that can indirectly affect search rankings by increasing traffic and content visibility. Combining SEO with social media tactics can improve a business's online visibility, which makes it a crucial component of all-encompassing digital marketing initiatives.

As of March 2025, Google represented 79.1 percent of the global online search engine market on desktop devices. Despite being much ahead of its competitors, this represents the lowest share ever recorded by the search engine in these devices for over two decades. Meanwhile, its long-time competitor Bing accounted for 12.21 percent, as tools like Yahoo and Yandex held shares of over 2.9 percent each. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of 2.02 trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly 348.16 billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than 63 percent of internet users in Russia used Yandex, whereas Google users represented little over 33 percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over 21 percent of users in Mexico said they used Yahoo.

Welcome to APISCRAPY, where our comprehensive SERP Data solution reshapes your digital insights. SERP, or Search Engine Results Page, data is the pivotal information generated when users query search engines such as Google, Bing, Yahoo, Baidu, and more. Understanding SERP Data is paramount for effective digital marketing and SEO strategies.

Key Features:

Comprehensive Search Insights: APISCRAPY's SERP Data service delivers in-depth insights into search engine results across major platforms. From Google SERP Data to Bing Data and beyond, we provide a holistic view of your online presence.

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Real-time Updates: Stay ahead of online search trends with our real-time updates. APISCRAPY ensures you have the latest SERP Data to adapt your strategies and capitalize on emerging opportunities.

Use Cases:

SEO Optimization: Refine your SEO strategies with precision using APISCRAPY's SERP Data. Understand Google SERP Data and other key insights, monitor your search engine rankings, and optimize content for maximum visibility.

Competitor Analysis: Gain a competitive edge by analyzing competitor rankings and strategies across Google, Bing, and other search engines. Benchmark against industry leaders and fine-tune your approach.

Keyword Research: Unlock the power of effective keyword research with comprehensive insights from APISCRAPY's SERP Data. Target the right terms for your audience and enhance your SEO efforts.

Content Strategy Enhancement: Develop data-driven content strategies by understanding what resonates on search engines. Identify content gaps and opportunities to enhance your online presence and SEO performance.

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Top Browsers Supported:

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Safari: Utilize Safari browser data to refine your digital strategies and tailor your content to a diverse audience. APISCRAPY's data scraping ensures Safari insights contribute to your comprehensive analysis.

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In summary, APISCRAPY's SERP Data solution empowers you with a diverse set of tools, from SERP API to Web Scraping, to unlock the full potential of online search trends. With top browser compatibility, real-time updates, and a comprehensive feature set, our solution is designed to elevate your digital strategies across various search engines. Stay ahead in the ever-evolving online landscape with APISCRAPY – where SEO Data, SERP API, and Web Scraping converge for unparalleled insights.

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Search Engine Market size was valued at USD 167 Billion in 2024 and is projected to reach USD 531.2 Billion by 2031, growing at a CAGR of 11.1% during the forecast period 2024-2031.

Global Search Engine Market Drivers

The market drivers for the Search Engine Market can be influenced by various factors. These may include:

Growth in Internet Penetration: Increase in internet accessibility worldwide, with more individuals and businesses going online.

Rising Mobile Device Usage: Surge in smartphone and tablet usage, leading to more searches conducted via mobile devices.

E-commerce Expansion: Growth in online shopping boosts search engine usage as consumers look for products and services online.

Technological Advancements: Innovations in artificial intelligence (AI), machine learning, and natural language processing enhance search engine functionalities.

Marketing and Advertising Needs: Increased demand for digital marketing and search engine optimization (SEO) as companies seek to improve online visibility.

Big Data Analytics: Use of big data to refine search algorithms and provide more personalized search results.

Voice Search and Virtual Assistants: Rising popularity of voice-activated searches through devices like Amazon Echo and Google Home.

Local Search Optimization: Growth in localized searches as businesses focus on targeting specific geographic areas.

Content Digitalization: Increasing volumes of digital content available on the internet, making search engines critical tools for information retrieval.

Improvement in User Experience: Enhanced user interfaces and faster search results improve user satisfaction and drive more frequent usage.

The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (http://rampanalytics.org), consisting of data from all participating repositories for the calendar year 2017. For a description of the data collection, processing, and output methods, please see the "methods" section below.

Methods RAMP Data Documentation – January 1, 2017 through August 18, 2018

Data Collection

RAMP data are downloaded for participating IR from Google Search Console (GSC) via the Search Console API. The data consist of aggregated information about IR pages which appeared in search result pages (SERP) within Google properties (including web search and Google Scholar).

Data from January 1, 2017 through August 18, 2018 were downloaded in one dataset per participating IR. The following fields were downloaded for each URL, with one row per URL:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
country: The country from which the corresponding search originated.
device: The device used for the search.
date: The date of the search.

Following data processing describe below, on ingest into RAMP an additional field, citableContent, is added to the page level data.

Note that no personally identifiable information is downloaded by RAMP. Google does not make such information available.

More information about click-through rates, impressions, and position is available from Google's Search Console API documentation: https://developers.google.com/webmaster-tools/search-console-api-original/v3/searchanalytics/query and https://support.google.com/webmasters/answer/7042828?hl=en

Data Processing

Upon download from GSC, data are processed to identify URLs that point to citable content. Citable content is defined within RAMP as any URL which points to any type of non-HTML content file (PDF, CSV, etc.). As part of the daily download of statistics from Google Search Console (GSC), URLs are analyzed to determine whether they point to HTML pages or actual content files. URLs that point to content files are flagged as "citable content." In addition to the fields downloaded from GSC described above, following this brief analysis one more field, citableContent, is added to the data which records whether each URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

Processed data are then saved in a series of Elasticsearch indices. From January 1, 2017, through August 18, 2018, RAMP stored data in one index per participating IR.

About Citable Content Downloads

Data visualizations and aggregations in RAMP dashboards present information about citable content downloads, or CCD. As a measure of use of institutional repository content, CCD represent click activity on IR content that may correspond to research use.

CCD information is summary data calculated on the fly within the RAMP web application. As noted above, data provided by GSC include whether and how many times a URL was clicked by users. Within RAMP, a "click" is counted as a potential download, so a CCD is calculated as the sum of clicks on pages/URLs that are determined to point to citable content (as defined above).

For any specified date range, the steps to calculate CCD are:

Filter data to only include rows where "citableContent" is set to "Yes."
Sum the value of the "clicks" field on these rows.

Output to CSV

Published RAMP data are exported from the production Elasticsearch instance and converted to CSV format. The CSV data consist of one "row" for each page or URL from a specific IR which appeared in search result pages (SERP) within Google properties as described above.

The data in these CSV files include the following fields:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
country: The country from which the corresponding search originated.
device: The device used for the search.
date: The date of the search.
citableContent: Whether or not the URL points to a content file (ending with pdf, csv, etc.) rather than HTML wrapper pages. Possible values are Yes or No.
index: The Elasticsearch index corresponding to page click data for a single IR.
repository_id: This is a human readable alias for the index and identifies the participating repository corresponding to each row. As RAMP has undergone platform and version migrations over time, index names as defined for the index field have not remained consistent. That is, a single participating repository may have multiple corresponding Elasticsearch index names over time. The repository_id is a canonical identifier that has been added to the data to provide an identifier that can be used to reference a single participating repository across all datasets. Filtering and aggregation for individual repositories or groups of repositories should be done using this field.

Filenames for files containing these data follow the format 2017-01_RAMP_all.csv. Using this example, the file 2017-01_RAMP_all.csv contains all data for all RAMP participating IR for the month of January, 2017.

References

Google, Inc. (2021). Search Console APIs. Retrieved from https://developers.google.com/webmaster-tools/search-console-api-original.

Relevancy ranking is an important component of making a data repository's search system
responsive to data seekers’ needs. The "https://www.rd-alliance.org/groups/data-discovery-paradigms-ig">Research Data Alliance (RDA) Data Discovery Paradigms
Interest Group is a collaborative activity within our data community which aims to improve data
searchability. This survey is intended to gather information about the current practices and lessons
learnt by data repositories in implementing relevancy ranking in search systems. We expect that
analysis of the survey results will:

• Help data repositories choose appropriate technologies when implementing or improving their search functionality;
• Provide a means for sharing experiences in improving relevancy ranking;
• Capture the aspirations, successes and challenges encountered from research data repository managers;
• Help the Data Discovery Paradigms Interest group align future activities on data search improvement with the interests of data search service providers.

For the above the purpose, we designed a survey instrument to answer the following topics (the numbers in brackets indicate the number of questions asked per topic):

1. What are characteristics of each repositories (5)?
2. What are system configurations (e.g., ranking model, index methods, query methods) (7)?
3. Evaluation methods and benchmark (10)
   • What has been evaluated?
   • What evaluation methods have been applied?
   • How was the evaluation collection built?
   • What is approximate performance range of search systems with certain configuration?
4. What methods have been used to boost searchability to web search engines (e.g., Google, Bing) (2)
5. What other technologies or system configurations have been employed (5)?
6. Wish list for future activities for the RDA relevance task force (2)?

Notes: Survey instruments from Version 1 and Version 2 have same questions, but order questions slightly different. Version 2 has the one as instrumented to participants.

Version update: The originally uploaded versions of the CSV files in this dataset included an extra column, "Unnamed: 0," which is not RAMP data and was an artifact of the process used to export the data to CSV format. This column has been removed from the revised dataset. The data are otherwise the same as in the first version.

The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (http://rampanalytics.org), consisting of data from all participating repositories for the calendar year 2020. For a description of the data collection, processing, and output methods, please see the "methods" section below.

Methods Data Collection

RAMP data are downloaded for participating IR from Google Search Console (GSC) via the Search Console API. The data consist of aggregated information about IR pages which appeared in search result pages (SERP) within Google properties (including web search and Google Scholar).

Data are downloaded in two sets per participating IR. The first set includes page level statistics about URLs pointing to IR pages and content files. The following fields are downloaded for each URL, with one row per URL:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Following data processing describe below, on ingest into RAMP a additional field, citableContent, is added to the page level data.

The second set includes similar information, but instead of being aggregated at the page level, the data are grouped based on the country from which the user submitted the corresponding search, and the type of device used. The following fields are downloaded for combination of country and device, with one row per country/device combination:

country: The country from which the corresponding search originated.
device: The device used for the search.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Note that no personally identifiable information is downloaded by RAMP. Google does not make such information available.

More information about click-through rates, impressions, and position is available from Google's Search Console API documentation: https://developers.google.com/webmaster-tools/search-console-api-original/v3/searchanalytics/query and https://support.google.com/webmasters/answer/7042828?hl=en

Data Processing

Upon download from GSC, the page level data described above are processed to identify URLs that point to citable content. Citable content is defined within RAMP as any URL which points to any type of non-HTML content file (PDF, CSV, etc.). As part of the daily download of page level statistics from Google Search Console (GSC), URLs are analyzed to determine whether they point to HTML pages or actual content files. URLs that point to content files are flagged as "citable content." In addition to the fields downloaded from GSC described above, following this brief analysis one more field, citableContent, is added to the page level data which records whether each page/URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

The data aggregated by the search country of origin and device type do not include URLs. No additional processing is done on these data. Harvested data are passed directly into Elasticsearch.

Processed data are then saved in a series of Elasticsearch indices. Currently, RAMP stores data in two indices per participating IR. One index includes the page level data, the second index includes the country of origin and device type data.

About Citable Content Downloads

Data visualizations and aggregations in RAMP dashboards present information about citable content downloads, or CCD. As a measure of use of institutional repository content, CCD represent click activity on IR content that may correspond to research use.

CCD information is summary data calculated on the fly within the RAMP web application. As noted above, data provided by GSC include whether and how many times a URL was clicked by users. Within RAMP, a "click" is counted as a potential download, so a CCD is calculated as the sum of clicks on pages/URLs that are determined to point to citable content (as defined above).

For any specified date range, the steps to calculate CCD are:

Filter data to only include rows where "citableContent" is set to "Yes."
Sum the value of the "clicks" field on these rows.

Output to CSV

Published RAMP data are exported from the production Elasticsearch instance and converted to CSV format. The CSV data consist of one "row" for each page or URL from a specific IR which appeared in search result pages (SERP) within Google properties as described above. Also as noted above, daily data are downloaded for each IR in two sets which cannot be combined. One dataset includes the URLs of items that appear in SERP. The second dataset is aggregated by combination of the country from which a search was conducted and the device used.

As a result, two CSV datasets are provided for each month of published data:

page-clicks:

The data in these CSV files correspond to the page-level data, and include the following fields:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.
citableContent: Whether or not the URL points to a content file (ending with pdf, csv, etc.) rather than HTML wrapper pages. Possible values are Yes or No.
index: The Elasticsearch index corresponding to page click data for a single IR.
repository_id: This is a human readable alias for the index and identifies the participating repository corresponding to each row. As RAMP has undergone platform and version migrations over time, index names as defined for the previous field have not remained consistent. That is, a single participating repository may have multiple corresponding Elasticsearch index names over time. The repository_id is a canonical identifier that has been added to the data to provide an identifier that can be used to reference a single participating repository across all datasets. Filtering and aggregation for individual repositories or groups of repositories should be done using this field.

Filenames for files containing these data end with “page-clicks”. For example, the file named 2020-01_RAMP_all_page-clicks.csv contains page level click data for all RAMP participating IR for the month of January, 2020.

country-device-info:

The data in these CSV files correspond to the data aggregated by country from which a search was conducted and the device used. These include the following fields:

country: The country from which the corresponding search originated.
device: The device used for the search.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.
index: The Elasticsearch index corresponding to country and device access information data for a single IR.
repository_id: This is a human readable alias for the index and identifies the participating repository corresponding to each row. As RAMP has undergone platform and version migrations over time, index names as defined for the previous field have not remained consistent. That is, a single participating repository may have multiple corresponding Elasticsearch index names over time. The repository_id is a canonical identifier that has been added to the data to provide an identifier that can be used to reference a single participating repository across all datasets. Filtering and aggregation for individual repositories or groups of repositories should be done using this field.

Filenames for files containing these data end with “country-device-info”. For example, the file named 2020-01_RAMP_all_country-device-info.csv contains country and device data for all participating IR for the month of January, 2020.

References

Google, Inc. (2021). Search Console APIs. Retrieved from https://developers.google.com/webmaster-tools/search-console-api-original.

You can check the fields description in the documentation: regular SERP: https://docs.dataforseo.com/v3/databases/google/serp_regular/?bash; Advanced SERP: https://docs.dataforseo.com/v3/databases/google/serp_advanced/?bash; Historical SERP: https://docs.dataforseo.com/v3/databases/google/history/serp_advanced/?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.

This data package includes two shapefiles and their associated attribute tables. The file, GFB_producers_2021-02-18.zip, contains all internet-discoverable (at the time of data collection, July-August 2020; with minor edits/additions circa June 2022) grass-fed beef producers in the Southwest and Southern Plains of the U.S. (Arizona, California, Colorado, Kansas, Nevada, New Mexico, Oklahoma, Texas, Utah). This dataset is a shapefile containing locations of grass-fed beef retailers in the Southwest and Southern Plains of the U.S. (Arizona, California, Colorado, Kansas, Nevada, New Mexico, Oklahoma, Texas, Utah), compiled through an internet search. The data were collected in August of 2020 using publicly available information from Google search engine and Google map searches with the intention of informing members of the Sustainable Southwest Beef Project (USDA NIFA grant #2019-69012-29853) team about existing grass-fed beef retailers in the study area.

The second shape file, GFB_retailers_2021-02-18.zip, contains
locations of grass-fed beef retailers in the Southwest and Southern
Plains of the U.S. (Arizona, California, Colorado, Kansas, Nevada,
New Mexico, Oklahoma, Texas, Utah), compiled through an internet
search. The data were collected in August of 2020 using publicly
available information from Google search engine and Google map
searches with the intention of informing members of the Sustainable
Southwest Beef Project (USDA NIFA grant #2019-69012-29853) team
about existing grass-fed beef retailers in the study area.

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.

Belarus Internet Usage: Search Engine Market Share: Desktop: StartPagina (Google) data was reported at 0.000 % in 09 Mar 2025. This records a decrease from the previous number of 0.030 % for 08 Mar 2025. Belarus Internet Usage: Search Engine Market Share: Desktop: StartPagina (Google) data is updated daily, averaging 0.000 % from Mar 2025 (Median) to 09 Mar 2025, with 9 observations. The data reached an all-time high of 0.070 % in 05 Mar 2025 and a record low of 0.000 % in 09 Mar 2025. Belarus Internet Usage: Search Engine Market Share: Desktop: StartPagina (Google) data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Belarus – Table BY.SC.IU: Internet Usage: Search Engine Market Share.

In April 2025, Google accounted for 86.71 percent of the search market in the United States across all devices. Bing followed as the second leading search provider in the United States during the last examined month, with a share of around 7.5 percent, among the engine's highest quotas registered in the country to date.

Andorra Internet Usage: Search Engine Market Share: Desktop: StartPagina (Google) data was reported at 0.000 % in 06 Oct 2024. This stayed constant from the previous number of 0.000 % for 05 Oct 2024. Andorra Internet Usage: Search Engine Market Share: Desktop: StartPagina (Google) data is updated daily, averaging 0.000 % from Jun 2024 (Median) to 06 Oct 2024, with 18 observations. The data reached an all-time high of 0.360 % in 07 Jun 2024 and a record low of 0.000 % in 06 Oct 2024. Andorra Internet Usage: Search Engine Market Share: Desktop: StartPagina (Google) data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Andorra – Table AD.SC.IU: Internet Usage: Search Engine Market Share.

Mauritius Internet Usage: Search Engine Market Share: Desktop: StartPagina (Google) data was reported at 0.000 % in 08 Mar 2025. This stayed constant from the previous number of 0.000 % for 07 Mar 2025. Mauritius Internet Usage: Search Engine Market Share: Desktop: StartPagina (Google) data is updated daily, averaging 0.000 % from Apr 2024 (Median) to 08 Mar 2025, with 18 observations. The data reached an all-time high of 0.060 % in 19 Apr 2024 and a record low of 0.000 % in 08 Mar 2025. Mauritius Internet Usage: Search Engine Market Share: Desktop: StartPagina (Google) data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Mauritius – Table MU.SC.IU: Internet Usage: Search Engine Market Share.

The social media search engine market is experiencing robust growth, driven by the increasing reliance on social platforms for information gathering and the evolution of sophisticated search algorithms within these platforms. The market size in 2025 is estimated at $15 billion, considering the overall digital advertising market size and the significant portion allocated to social media. A Compound Annual Growth Rate (CAGR) of 15% is projected for the period 2025-2033, indicating a substantial expansion of this sector. Key drivers include the rising user base of social media platforms, increased integration of search functionalities within these platforms, and the growing demand for targeted advertising on social media. Furthermore, the continuous development of AI-powered search algorithms promises enhanced user experience and increased effectiveness of advertising. While data privacy concerns and the evolving regulatory landscape pose potential restraints, the market's growth trajectory remains positive. Segmentation by application (individual vs. business users) and search type (word, image, video) provides valuable insights into specific market opportunities. Business users are expected to drive a significant portion of the market growth owing to increased reliance on social media for market research, brand monitoring, and targeted advertising campaigns. The competitive landscape is highly concentrated, with established players like Google, Facebook, and others holding substantial market share. However, the emergence of new, specialized social media search engines, particularly those focusing on niche areas like video or image search, presents opportunities for disruptive innovation. Geographic analysis reveals significant variations in market penetration. North America and Europe are expected to maintain substantial market dominance in the near term, though rapid growth is anticipated in Asia Pacific regions like India and China due to their expanding internet and social media penetration. The focus on optimizing user experience through personalized search results and improved relevance is expected to remain a key differentiator in this increasingly competitive landscape. The incorporation of advanced analytics and data visualization tools is also driving market growth, facilitating effective marketing campaigns and deeper consumer insights.

According to Cognitive Market Research, the global Enterprise Search Engine market size will be USD 4358.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 9.70% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 1743.28 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.9% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 1307.46 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 1002.39 million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.7% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 217.91 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.1% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 87.16 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.4% from 2024 to 2031.
The Solution category is the fastest growing segment of the Enterprise Search Engine industry

Market Dynamics of Enterprise Search Engine Market

Key Drivers for Enterprise Search Engine Market

Increasing Data Volume to Boost Market Growth

The increasing volume of data generated by organizations is a primary driver of the Enterprise Search Engine Market. As businesses accumulate vast amounts of structured and unstructured data from various sources—such as emails, documents, social media, and databases—the need for efficient retrieval and management becomes critical. Enterprise search engines enable organizations to sift through this data quickly, providing employees with timely access to information that can enhance decision-making and productivity. Additionally, the proliferation of big data technologies and cloud storage solutions contributes to data growth, necessitating robust search capabilities to ensure that valuable insights are not lost. This demand for streamlined access to comprehensive information continues to fuel the expansion of the enterprise search engine market. For instance, Google launched local search functionalities that were previewed earlier this year. These features enable users to explore their environment using their smartphone camera. Additionally, Google has added an option to search for restaurants by specific dishes and introduced new search capabilities within the Live View feature of Google Maps.

Increasing Demand for Data-Driven Decision-Making to Drive Market Growth

The rising demand for data-driven decision-making is significantly driving the Enterprise Search Engine Market. Organizations increasingly recognize the value of leveraging data analytics to inform strategic decisions, enhance operational efficiency, and improve customer experiences. As businesses strive to become more agile and responsive to market changes, they require quick access to relevant data across various departments and sources. Enterprise search engines facilitate this by enabling employees to efficiently retrieve and analyze critical information, thus supporting informed decision-making processes. Moreover, the integration of advanced analytics and artificial intelligence into enterprise search solutions further empowers organizations to derive actionable insights from their data. This trend towards a data-centric approach in business operations continues to propel the growth of the enterprise search engine market.

Restraint Factor for the Enterprise Search Engine Market

High Implementation Costs will Limit Market Growth

High implementation costs are a significant restraint on the growth of the Enterprise Search Engine Market. Deploying enterprise search solutions often involves substantial initial investments in software, hardware, and integration services. Organizations must consider expenses related to customizing the search engine to fit their unique data architectures and user needs. Additionally, ongoing maintenance, updates, and training for staff can contribute to overall costs, making it challenging for smaller businesses or those with limited budgets to adopt these systems. This financial barrier can hinder organizations from fully realizing the benefits of enterprise search engines, leading to under...