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.

Google Search Statistics 2023

• Google is the most searched website in the World.
• Google receives more visitors than any other site. Google is accessed 89.3 trillion times per month.
• Google is used by billions of people every day to conduct their searches. Google is much more than a simple search engine.
• Google provides many other services. Google Shopping and Google News also feature. Google Mail, Google's popular email service, is included.
• Google organic search traffic is 16.3% of the total US searches.

As of the fourth quarter of 2019, 49 percent of Yahoo's U.S. organic search traffic originated from mobile devices. Google had the highest share of organic mobile search traffic with 61 percent, slightly ahead of DuckDuckGo with 58 percent.

This study compiles data from multiple sources (Ahrefs, Amsive, Pew Research, Semrush) to evaluate the impact of Google AI Overview on website traffic across different sectors.

Web traffic statistics for the several City-Parish websites, brla.gov, city.brla.gov, Red Stick Ready, GIS, Open Data etc. Information provided by Google Analytics.

A dataset explaining organic traffic, its importance for SEO, and methods to track it in Google Analytics 4.

google.com is ranked #1 in US with 98.22B Traffic. Categories: Online Services. Learn more about website traffic, market share, and more!

blog.google is ranked #3348 in US with 10.14M Traffic. Categories: Online Services. Learn more about website traffic, market share, and more!

The Search Engine industry is highly concentrated, with three companies controlling almost the entire industry; the largest company, Alphabet Inc., has a market share greater than 96%. Search engines provide web portals that generate and maintain extensive databases of internet addresses. Industry companies generate most, if not all, of their revenue from advertising. Technological growth has resulted in more households being connected to the Internet and a boom in e-commerce has made the industry increasingly innovative. A climb in the proportion of households with internet access has supported revenue growth, while expanding technological integration with daily life has boosted demand for web search. A greater proportion of transactions being carried out online has driven innovation in targeted digital advertising, with declines in rival advertising formats like print media and television expanding the focus on digital marketing as a core strategy. Industry revenue is expected to jump at a compound annual rate of 3.8%, to reach £5.4 billion over the five years through 2025-26. Revenue is forecast to climb by 3.5% in 2025-26. Industry profit has remained high and expanded alongside a surge in search and display advertising, with total UK digital ad spend. The rise of the mobile advertising market and the proliferation of mobile devices mean there are plenty of opportunities for search engines, which are expected to capitalise on these trends further moving forward. While continued growth in localised digital marketing and rising overall UK marketing budgets are set to propel industry revenues, Google faces mounting regulatory scrutiny. The Digital Markets, Competition and Consumers Act 2024, with the impending Strategic Market Status designation for Google, is poised to shake up the landscape by curtailing Google’s market power and fostering greater transparency. Search engines will need to innovate to fend off rising competition from social media platforms, which are attracting advertisers through advanced targeting capabilities. Although niche, privacy-centric search engines could capture incremental market share as consumer privacy concerns intensify, the industry’s overwhelming concentration, with Google’s unmatched user base and ad inventory, means transformative change will likely be incremental. Nonetheless, technological advancements that incorporate user data are anticipated to make it easier to tailor advertisements and develop new ways of using consumer data. Industry revenue is forecast to jump at a compound annual rate of 5.9% over the five years through 2030-31, to reach £7.2 billion.

google.com.mx is ranked #23 in MX with 47.65M Traffic. Categories: Education. Learn more about website traffic, market share, and more!

Search engines, which collect, organize and display knowledge of the internet, remain central to the digital economy but are entering a period of rapid transformation driven by AI and shifting user behavior. Over the past five years, internet advertising spending maintained strong momentum, propelled by growing mobile internet access and consumer screen time. Consequently, industry revenue is expected to climb at a CAGR of 9.4% to $316.8 billion, including an anticipated rise of 7.7% in 2025, with profit at 18.6%. The industry stands apart from most in the tech sector, because of its platform-based revenue model, aggregation dynamics and deep integration with the broader digital ecosystem. While user engagement fuels relevance, it is advertiser demand that sustains revenue, requiring a careful balance between utility and monetization. This landscape has been reshaped by the rise of generative AI. Conversational tools and AI-generated summaries are reducing user interaction with traditional search results, challenging established SEO practices and disrupting referral-based traffic flows. Meanwhile, search engines are reconfiguring their ad models to prioritize quality and contextual relevance, moving away from legacy monetization strategies. These trends signal a broader shift in how search platforms operate, less as navigational tools and more as integrated, AI-driven environments. As digital behavior fragments and users seek information across apps like Amazon, TikTok and ChatGPT, industry revenue is still projected to climb at a CAGR of 7.3% to $449.9 billion through 2030. Advertisers are expected to continue investing in search, drawn by the format’s performance insights and optimization capabilities. However, AI is redefining search from a navigational tool into a task-oriented solution engine, where users expect conversational, multimodal and predictive answers instead of traditional results pages. To stay relevant, incumbent platforms must evolve into embedded AI utilities that power experiences across devices and enterprise workflows.

The global website visitor tracking software market is experiencing robust growth, driven by the increasing need for businesses to understand online customer behavior and optimize their digital strategies. The market, estimated at $5 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. This expansion is fueled by several key factors, including the rising adoption of digital marketing strategies, the growing importance of data-driven decision-making, and the increasing sophistication of website visitor tracking tools. Cloud-based solutions dominate the market due to their scalability, accessibility, and cost-effectiveness, particularly appealing to Small and Medium-sized Enterprises (SMEs). However, large enterprises continue to invest significantly in on-premise solutions for enhanced data security and control. The market is highly competitive, with numerous established players and emerging startups offering a range of features and functionalities. Technological advancements, such as AI-powered analytics and enhanced integration with other marketing tools, are shaping the future of the market. The market's geographical distribution reflects the global digital landscape. North America, with its mature digital economy and high adoption rates, holds a significant market share. However, regions like Asia-Pacific are showing rapid growth, driven by increasing internet penetration and digitalization across various industries. Despite the overall positive outlook, challenges such as data privacy regulations and the increasing complexity of website tracking technology are influencing market dynamics. The ongoing competition among vendors necessitates continuous innovation and the development of more user-friendly and insightful tools. The future growth of the website visitor tracking software market is promising, fueled by the continuing importance of data-driven decision-making within marketing and business strategies. A key factor will be the ongoing adaptation to evolving privacy regulations and user expectations.

google.it is ranked #7 in IT with 151.27M Traffic. Categories: Education. Learn more about website traffic, market share, and more!

This data set is related to Traffic. History of traffic data since 2013, indicating the latter for each measurement point, the passing vehicles. The infrastructure of measurement points, available in the city of Madrid corresponds to: 7,360 vehicle detectors with the following characteristics: 71 include number plate reading devices 158 have optical machine vision systems with control from the Mobility Management Center 1,245 are specific to fast roads and access to the city and the rest of the 5,886, with basic traffic light control systems. More than 4,000 measuring points : 253 with systems for speed control, characterization of vehicles and double reading loop 70 of them make up the stations of taking specific seats of the city. Automatic control systems of all the information obtained from the detectors with continuous contrast with expected behavior patterns, as well as the follow-up of the instructions marked by the Technical Committee for Standardization AEN/CTN 199; and in particular SC3 specific applications relating to “Detectors and data collection stations” and SC15 relating to “Data quality”. In this same portal you can find other related data sets such as: Traffic. Real-time traffic data . With real-time information (updated every 5 minutes) Traffic. Map of traffic intensity plots, with the same information in KML format, and with the possibility of viewing it in Google Maps or Google Earth. And other traffic-related data sets. You can search for them by putting the word 'Traffic' in the search engine (top right).

google.com.ai is ranked #13976 in GB with 153.84K Traffic. Categories: . Learn more about website traffic, market share, and more!

A dataset comparing features, pricing, and ratings of the top sites to buy website traffic in 2025: Google Ads, Facebook Ads, PropellerAds, and SparkTraffic.

Google dominated the Egyptian web traffic. As of November 2022, close to **** percent of the web traffic was referred via this search engine. Bing was its closest competitor, with only *** percent. Yahoo! came in third place, with a share of almost *** percent.

The website traffic analysis tool market is experiencing robust growth, driven by the increasing reliance of businesses, both large and small, on digital marketing strategies. The demand for data-driven decision-making and performance optimization across various online channels is fueling the adoption of these tools. The market, estimated at $15 billion in 2025, is projected to grow at a compound annual growth rate (CAGR) of 15% through 2033, reaching approximately $45 billion. This growth is fueled by several key trends: the rise of cloud-based solutions offering greater scalability and accessibility, increasing sophistication of analytics capabilities (including AI-powered insights), and a growing need for comprehensive website performance monitoring. While the market exhibits strong growth potential, businesses face challenges including the increasing complexity of website analytics, the need for skilled personnel to interpret data effectively, and the rising costs associated with premium features and advanced analytics platforms. The segmentation reveals a significant presence of both SMEs and large enterprises leveraging the technology, with a clear preference toward cloud-based solutions due to their flexibility and cost-effectiveness. Key players such as Semrush, Ahrefs, Google Analytics, and others are actively shaping the market through continuous innovation and expansion into new markets. The geographical distribution of the market reflects a strong presence in North America and Europe, driven by higher digital maturity and adoption rates within these regions. However, significant growth opportunities exist in Asia Pacific and other emerging markets, as digital infrastructure expands and businesses increasingly prioritize online presence. The competitive landscape is characterized by a mix of established players and emerging startups, leading to continuous innovation and price competition, benefiting end users. This intense competition drives the development of advanced features such as real-time analytics, predictive modeling, and integration with other marketing tools. The ongoing evolution of digital marketing itself is a major driver, requiring the constant refinement and improvement of these analytics tools to keep pace with changes in SEO, social media, and online advertising practices. This creates a dynamic environment conducive to further market expansion.

Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

Code:

Packet_Features_Generator.py & Features.py

To run this code:

pkt_features.py [-h] -i TXTFILE [-x X] [-y Y] [-z Z] [-ml] [-s S] -j

-h, --help show this help message and exit -i TXTFILE input text file -x X Add first X number of total packets as features. -y Y Add first Y number of negative packets as features. -z Z Add first Z number of positive packets as features. -ml Output to text file all websites in the format of websiteNumber1,feature1,feature2,... -s S Generate samples using size s. -j

Purpose:

Turns a text file containing lists of incomeing and outgoing network packet sizes into separate website objects with associative features.

Uses Features.py to calcualte the features.

startMachineLearning.sh & machineLearning.py

To run this code:

bash startMachineLearning.sh

This code then runs machineLearning.py in a tmux session with the nessisary file paths and flags

Options (to be edited within this file):

--evaluate-only to test 5 fold cross validation accuracy

--test-scaling-normalization to test 6 different combinations of scalers and normalizers

Note: once the best combination is determined, it should be added to the data_preprocessing function in machineLearning.py for future use

--grid-search to test the best grid search hyperparameters - note: the possible hyperparameters must be added to train_model under 'if not evaluateOnly:' - once best hyperparameters are determined, add them to train_model under 'if evaluateOnly:'

Purpose:

Using the .ml file generated by Packet_Features_Generator.py & Features.py, this program trains a RandomForest Classifier on the provided data and provides results using cross validation. These results include the best scaling and normailzation options for each data set as well as the best grid search hyperparameters based on the provided ranges.

Data

Encrypted network traffic was collected on an isolated computer visiting different Wikipedia and New York Times articles, different Google search queres (collected in the form of their autocomplete results and their results page), and different actions taken on a Virtual Reality head set.

Data for this experiment was stored and analyzed in the form of a txt file for each experiment which contains:

First number is a classification number to denote what website, query, or vr action is taking place.

The remaining numbers in each line denote:

The size of a packet,

and the direction it is traveling.

negative numbers denote incoming packets

positive numbers denote outgoing packets

Figure 4 Data

This data uses specific lines from the Virtual Reality.txt file.

The action 'LongText Search' refers to a user searching for "Saint Basils Cathedral" with text in the Wander app.

The action 'ShortText Search' refers to a user searching for "Mexico" with text in the Wander app.

The .xlsx and .csv file are identical

Each file includes (from right to left):

The origional packet data,

each line of data organized from smallest to largest packet size in order to calculate the mean and standard deviation of each packet capture,

and the final Cumulative Distrubution Function (CDF) caluclation that generated the Figure 4 Graph.