Websites in the energy, utilities, and construction sector averaged the largest amount of visits per online session worldwide. In the fourth quarter of 2022, desktop users in that segment visited around ***** pages per online session. Travel and hospitality ranked second, with an average of almost *** pages visited. In terms of mobile users, travel and hospitality registered the highest number of page views, followed by retail.

In November 2024, Google.com was the most popular website worldwide with approximately 6.25 billion unique monthly visitors. YouTube.com was ranked second with an estimated 3.64 billion unique monthly visitors. Both websites are among the most visited websites worldwide.

A complete list of live websites using the Visit technology, compiled through global website indexing conducted by WebTechSurvey.

In November 2024, Google.com was the most visited website in the United States, with over 25 billion total visits. YouTube.com came in second with 12 billion total visits. Reddit.com and Amazon.com counted approximately 3.12 billion and 2.89 monthly visits each from U.S. online audiences.

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.

A complete list of live websites using the Page Visit Counter technology, compiled through global website indexing conducted by WebTechSurvey.

From September to November 2024, Facebook.com was the most-visited website in Vietnam with an average of **** pages per visit. The same report also revealed that Google.com was the website with the highest number of unique visits.

This dataset contains the Number of visits to the MCPL website by month. Update frequency : Annually

In April 2025, most visits to the travel and tourism website tripadvisor.com came from the United States. During that month, website visits from the U.S. accounted for over half of total visits to Tripadvisor's web page. In 2025, tripadvisor.com was one of the most visited travel and tourism websites worldwide.

In April 2025, the United States accounted for the highest share of visits to the travel and tourism website booking.com. During that month, website visits from the U.S. represented over ** percent of total visits to Booking's web page. In 2025, booking.com was the most visited travel and tourism website worldwide.

In March 2024, Google.com was the leading website in the United States. The search platform accounted for over 19 percent of desktop web traffic in the United States, ahead of second-ranked YouTube.com with 10.71 percent.

In May 2025, booking.com was the most visited travel and tourism website worldwide. That month, Booking’s web page recorded around *** million visits. Tripadvisor.com and airbnb.com followed in the ranking, with roughly *** million and ** million visits, respectively. Popular online travel agencies in the U.S. Online travel agencies (OTAs), such as Booking.com and Expedia, offer a wide variety of services, including online hotel bookings, flight reservations, and car rentals. According to the Statista Consumer Insights Global survey, when looking at flight search engine online bookings by brand in the United States, Booking.com and Expedia were the most popular options when it came to making online flight reservations in 2025. When focusing on hotel and private accommodation online bookings in the U.S., Booking.com was again the most popular brand, followed by Airbnb, Expedia, and Hotels.com. Booking Holdings vs. Expedia Group Booking.com is one of the most popular sites of the online travel group Booking Holdings, the leading online travel agency worldwide based on revenue, that also owns brands like Priceline, Kayak, and Agoda. In 2024, Booking Holdings' revenue amounted to almost ** billion U.S. dollars, the highest figure reported by the company to date. Meanwhile, global revenue of Expedia Group, which manages brands like Expedia, Hotels.com, and Vrbo, reached nearly ** billion U.S. dollars that year.

In July 2025, the number of visits to the travel and tourism website booking.com increased over the previous month, totaling approximately *** million. In 2025, Booking's web page was the most visited travel and tourism website worldwide.

In November 2024, WhatsApp.com was the most popular website in Spain by time per visit, with an average session length of approximately ** minutes and ** seconds. YouTube.com ranked second, with an average of ** minutes and ** seconds per visit. Despite being the leading website by total visits and unique visitors in the country, Google.com ranked fourth in engagement time, with ** minutes and ** seconds per session.

Vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.

Visitor statistics of the pages of the website of the City of Brussels with the address of the page (after www.brussels.be), the number of pages viewed, the number of unique visits, average time spent on the page. Source: Google Analytics.

Libraries Statistics: Results by type of library and type of access / number of visits to the website/services accessible through the Internet. National.

Countries and territories of the visitors of the website of the City of Brussels (2012), determined by the geographical area of their IP address. Source: Google Analytics.

In November 2024, Google.com was the leading website in Mexico by unique visitors, with around 121 million single accesses to the URL during that period. Social video platform YouTube.com followed, with 66.1 million unique monthly visitors. Additionally, Facebook.com was ranked third with over 56 million unique monthly visitors.

Context

Uplift modeling is an important yet novel area of research in machine learning which aims to explain and to estimate the causal impact of a treatment at the individual level. In the digital advertising industry, the treatment is exposure to different ads and uplift modeling is used to direct marketing efforts towards users for whom it is the most efficient . The data is a collection collection of 13 million samples from a randomized control trial, scaling up previously available datasets by a healthy 590x factor.

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Content

The dataset was created by The Criteo AI Lab .The dataset consists of 13M rows, each one representing a user with 12 features, a treatment indicator and 2 binary labels (visits and conversions). Positive labels mean the user visited/converted on the advertiser website during the test period (2 weeks). The global treatment ratio is 84.6%. It is usual that advertisers keep only a small control population as it costs them in potential revenue.

Following is a detailed description of the features:

• f0, f1, f2, f3, f4, f5, f6, f7, f8, f9, f10, f11: feature values (dense, float)
• treatment: treatment group (1 = treated, 0 = control)
• conversion: whether a conversion occured for this user (binary, label)
• visit: whether a visit occured for this user (binary, label)
• exposure: treatment effect, whether the user has been effectively exposed (binary)

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Context

Uplift modeling is an important yet novel area of research in machine learning which aims to explain and to estimate the causal impact of a treatment at the individual level. In the digital advertising industry, the treatment is exposure to different ads and uplift modeling is used to direct marketing efforts towards users for whom it is the most efficient . The data is a collection collection of 13 million samples from a randomized control trial, scaling up previously available datasets by a healthy 590x factor.

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Content

The dataset was created by The Criteo AI Lab .The dataset consists of 13M rows, each one representing a user with 12 features, a treatment indicator and 2 binary labels (visits and conversions). Positive labels mean the user visited/converted on the advertiser website during the test period (2 weeks). The global treatment ratio is 84.6%. It is usual that advertisers keep only a small control population as it costs them in potential revenue.

Following is a detailed description of the features:

• f0, f1, f2, f3, f4, f5, f6, f7, f8, f9, f10, f11: feature values (dense, float)
• treatment: treatment group (1 = treated, 0 = control)
• conversion: whether a conversion occured for this user (binary, label)
• visit: whether a visit occured for this user (binary, label)
• exposure: treatment effect, whether the user has been effectively exposed (binary)

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Starter Kernels

• HistGradientBoostingClassifier Base Model

Acknowledgement

The data provided for paper: "A Large Scale Benchmark for Uplift Modeling"

https://s3.us-east-2.amazonaws.com/criteo-uplift-dataset/large-scale-benchmark.pdf

• Eustache Diemert CAIL e.diemert@criteo.com
• Artem Betlei CAIL & Université Grenoble Alpes a.betlei@criteo.com
• Christophe Renaudin CAIL c.renaudin@criteo.com
• Massih-Reza Amini Université Grenoble Alpes massih-reza.amini@imag.fr

For privacy reasons the data has been sub-sampled non-uniformly so that the original incrementality level cannot be deduced from the dataset while preserving a realistic, challenging benchmark. Feature names have been anonymized and their values randomly projected so as to keep predictive power while making it practically impossible to recover the original features or user context.

Inspiration

We can foresee related usages such as but not limited to:

• Uplift modeling
• Interactions between features and treatment
• Heterogeneity of treatment

More Readings

• Supercharging customer touchpoints with uplift modeling
• CasualML
• PyLift

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