A study released in June 2025 that looked at about 82,000 websites found that Google was responsible for almost ** percent of the traffic generated to these domains. Direct traffic corresponded to around **** percent of the investigated websites' traffic volume. While traditional search engines like Bing and social networks like Facebook represented larger shares, ChatGPT overtook Reddit and LinkedIn with a slightly larger share, indicating an increase in traffic from these platforms.

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A study released in March 2025 that looked at about 35,000 websites found that online search channels were responsible for almost ** percent of the traffic generated to these domains. By the time of this study, direct traffic corresponded to around **** percent of visits to the analyzed websites. Meanwhile, large language models (LLMs) like ChatGPT and Gemini corresponded to around *** percent of the verified traffic, representing a share just below e-mail platforms.

As of 2019, direct traffic accounts for the largest percentage of website traffic worldwide, with a share of 55 percent. Additionally, search traffic accounts for 29 percent of worldwide website traffic.

In May 2022, **** percent of e-commerce traffic in the Middle East region was generated through direct visits. Traffic generated through search witnessed a growth in the region in the years 2020 and 2021.

Mobile accounts for approximately half of web traffic worldwide. In the last quarter of 2024, mobile devices (excluding tablets) generated 62.54 percent of global website traffic. Mobiles and smartphones consistently hoovered around the 50 percent mark since the beginning of 2017, before surpassing it in 2020. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.

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?

The dataset provides 12 months (August 2016 to August 2017) of obfuscated Google Analytics 360 data from the Google Merchandise Store , a real ecommerce store that sells Google-branded merchandise, in BigQuery. It’s a great way analyze business data and learn the benefits of using BigQuery to analyze Analytics 360 data Learn more about the data The data includes The data is typical of what an ecommerce website would see and includes the following information:Traffic source data: information about where website visitors originate, including data about organic traffic, paid search traffic, and display trafficContent data: information about the behavior of users on the site, such as URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions on the Google Merchandise Store website.Limitations: All users have view access to the dataset. This means you can query the dataset and generate reports but you cannot complete administrative tasks. Data for some fields is obfuscated such as fullVisitorId, or removed such as clientId, adWordsClickInfo and geoNetwork. “Not available in demo dataset” will be returned for STRING values and “null” will be returned for INTEGER values when querying the fields containing no data.This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery

This statistic shows a ranking of online traffic sources towards the website of HelloFresh from 2017 to 2019. The numbers provided here concern desktop visits from the Netherlands. Direct traffic is deemed to be the most important source for web traffic, as ** percent of visits towards the site came in this way. Organic search results were less important: these made up ** percent of total desktop traffic.

Direct online search was the main channel for online marketplace traffic in Mexico, Colombia, Peru, and Chile in the third quarter of 2022. On average, **** percent of online visits to marketplaces in these countries came from searches made directly on their websites. Organic search was the leading referring source for online food retailers, at about **** percent.

Datos brings to market anonymized, at scale, consolidated privacy-secured datasets with a granularity rarely found in the market. Get 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.

The Datos Domain Traffic Feed reports on panelist visitation to domains, benchmarking the popularity of internet properties worldwide by country. Additionally, we offer the ability to track the availability of domains with respect to whether traffic is being sent to sites which are currently unregistered. Customers can elect to focus on specific domains, countries, or domain registration status.

Now available with Datos Low-Latency Feed This add-on ensures delivery of approximately 99% of all devices before markets open in New York (the lowest latency product on the market). Our clickstream data is made up of an array of upstream sources. The DLLF makes the daily output of these sources available as they arrive and are processed, rather than a once-daily batch.

Listen to this episode: About This Episode: Traffic sources can come from a number of places, but for most companies the largest source is Google. And things can get confusing when it comes to organic traffic versus paid ads. There are a number of things that can affect organic traffic and paid traffic in Google, […]

This statistic shows a ranking of online traffic sources towards the website of online retailer Bol.com from 2017 to 2019. The numbers provided here concern desktop visits from the Netherlands. Direct traffic is deemed to be the most important source for web traffic, as ** percent of visits towards the site came in this way. Organic search results were less important: these made up ** percent of total desktop traffic.

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This dataset gathers the most crucial SEO statistics for the year, providing an overview of the dominant trends and best practices in the field of search engine optimization. Aimed at digital marketing professionals, site owners, and SEO analysts, this collection of information serves as a guide to navigate the evolving SEO landscape with confidence and accuracy. Mode of Data Production: The statistics have been carefully selected and compiled from a variety of credible and recognized sources in the SEO industry, including research reports, web traffic data analytics, and consumer and marketing professional surveys. Each statistic was checked for reliability and relevance to current trends. Categories Included: User search behaviour: Statistics on the evolution of search modes, including voice and mobile search. Mobile Optimisation: Data on the importance of site optimization for mobile devices. Importance of Backlinks: Insights on the role of backlinks in SEO ranking and the need to prioritize quality. Content quality: Statistics highlighting the importance of relevant and engaging content for SEO. Search engine algorithms: Information on the impact of algorithm updates on SEO strategies. Usefulness of the Data: This dataset is designed to help users quickly understand current SEO dynamics and apply that knowledge in optimizing their digital marketing strategies. It provides a solid foundation for benchmarking, strategic planning, and informed decision-making in the field of SEO. Update and Accessibility: To ensure relevance and timeliness, the dataset will be regularly updated with new information and emerging trends in the SEO world.

Comprehensive alternative data sources for institutional investment research including search trends, social media sentiment, web traffic analytics, and proprietary datasets.

This statistic shows a ranking of online traffic sources towards the website of Wehkamp from 2017 to 2019. The numbers provided here concern desktop visits from the Netherlands. Direct traffic is deemed to be the most important source for web traffic, as ** percent of visits towards the site came in this way. Organic search results were less important: these made up ** percent of total desktop traffic.

This dataset contains estimates of the average number of vehicles that used roads throughout the City of Detroit in 2022. Each record indicates the Annual Average Daily Traffic (AADT) and Commercial Annual Average Daily Traffic (CAADT) for a road segment, where the road segment is located, and other characteristics. This data is derived from Michigan Department of Transportation's (MDOT) Open Data Portal. SEMCOG was the source for speed limits and number of lanes.The primary measure, Annual Average Daily Traffic (AADT), is the estimated mean daily traffic volume for all types of vehicles. Commercial Annual Average Daily Traffic (CAADT) is the estimated mean daily traffic volume for commercial vehicles, a subset of vehicles included in the AADT. The Route ID is an identifier for each road in Detroit (e.g., Woodward Ave). Routes are divided into segments by features such as cross streets, and Location ID's are used to uniquely identify those segments. Along with traffic volume, each record also states the number of lanes, the posted speed limit, and the type of road (e.g., Trunkline or Ramp) based on the Federal Highway Administration (FHWA) functional classification system.According to MDOT's Traffic Monitoring Program a commercial vehicle would be anything Class 4 and up in the FHWA vehicle classification system. This includes vehicles such as buses, semi-trucks, and personal recreational vehicles (i.e., RVs or campers). Methods used to determine traffic volume vary by site, and may rely on continuous monitoring or estimates based on short-term studies. Approaches to vehicle classification similarly vary, depending on the equipment used at a site, and may consider factors such as vehicle weight and length between axles.For more information, please visit MDOT Traffic Monitoring Program.

Dear Scientist!This database contains data collected due to conducting study: "Analysis of the route safety of abnormal vehicle from the perspective of traffic parameters and infrastructure characteristics with the use of web technologies and machine learning" funded by National Science Centre Poland (Grant reference 2021/05/X/ST8/01669). The structure of files is arising from the aims of the study and numerous of sources needed to tailor suitable data possible to use as an input layer for neural network. You can find a following folders and files:1. Road_Parameters_Data (.csv) - which is data colleced by author before the study (2021). Here you can find information about technical quality and types of main roads located in Mazovia province (Poland). The source of data was Polish General Directorate for National Roads and Motorways. 2. Google_Maps_Data (.json) - here you can find the data, which was collected using the authors’ webservice created using the Python language, which downloaded the said data in the Distance Matrix API service on Google Maps at two-hour intervals from 25 May 2022 to 22 June 2022. The application retrieved the TRAFFIC FACTOR parameter, which was a ratio of actual time of travel divided by historical time of travel for particular roads.3. Geocoding_Roads_Data (.json) - in this folder you can find data gained from reverse geocoding approach based on geographical coordinates and the request parameter latlng were employed. As a result, Google Maps returned a response containing the postal code for the field types defined as postal_code and the name of the lowest possible level of the territorial unit for the field administrative_area_level. 4. Population_Density_Data (.csv) - here you can find date for territorial units, which were assigned to individual records were used to search the database of the Polish Postal Service using the authors' original web service written in the Python programming language. The records which contained a postal code were assigned the name of the municipality which corresponded to it. Finally, postal codes and names of territorial units were compared with the database of the Statistics Poland (GUS) containing information on population density for individual municipalities and assigned to existing records from the database.5. Roads_Incidents_Data (.json) - in this folder you can find a data collected by a webservice, which was programmed in the Python language and used for analysing the reported obstructions available on the website of the General Directorate for National Roads and Motorways. In the event of traffic obstruction emergence in the Mazovia Province, the application, on the basis of the number and kilometre of the road on which it occurred, could associate it later with appropriate records based on the links parameters. The data was colleced from 26 May to 22 June 2022.6. Weather_For_Roads_Data (.json) - here you can find the data concerning the weather conditions on the roads occurring at days of the study. To make this feasible, a webservice was programmed in the Python language, by means of which the selected items from the response returned by the www.timeanddate.com server for the corresponding input parameters were retrieved – geographical coordinates of the midpoint between the nodes of the particular roads. The data was colleced for day between 27 May and 22 June 2022.7. data_v_1 (.csv) - collected only data for road parameters8. data_v_2 (.csv) - collected data for road parameters + population density9. data_v_3 (.json) - collected data for road parameters + population density + traffic10. data_v_4 (.json) - collected data for road parameters + population density + traffic + weather + road incidents11. data_v_5 (.csv) - collected VALIDATED and cleaned data for road parameters + population density + traffic + weather + road incidents. At this stage, the road sections for which the parameter traffic factor was assessed to have been estimated incorrectly were eliminated. These were combinations for which the value of the traffic factor remained the same regardless the time of day or which took several of the same values during the course of the whole study. Moreover, it was also assumed that the final database should consist of road sections for traffic factor less than 1.2 constitute at least 10% of all results. Thus, the sections with no tendency to become congested and characterized by a small number of road traffic users were eliminated.Good luck with your research!Igor Betkier, PhD

This interactive web application features both the on-road Maryland Level of Bicycle Stress (LTS) feature layer for all road centerlines in Maryland as well the Road-Separated feature layer of all road-separated bike routes throughout Maryland. An overview of the methodology and attribute data for the Maryland Level of Bicycle Stress (LTS) is provided below. For a detailed full report of the methodology, please view the PDF published by the Maryland Department of Transportation here. The Maryland Department of Transportation is transitioning from using the Bicycle Level of Comfort (BLOC) to using the Level of Traffic Stress (LTS) for measuring the “bikeability” of the roadway network. This transition is in coordination with the implementation of MDOT SHA’s Context Driven Design Guidelines and other national and departmental initiatives. LTS is preferred over BLOC as LTS requires fewer variables to calculate including: Average Annual Daily Traffic, Speed Limits, Presence of Bicycle Facilities, Shoulder, etc. Data LimitationsA principle of data governance MDOT strives to provide the best possible data products. While the initial LTS analysis of Maryland’s bicycle network has many uses, it should be used with a clear understanding of the current limitations the data presents.Assumptions - As noted earlier in this document, some of the metrics used to determine LTS score were estimated. Speed limits for many local roadways were not included in the original data and were assigned based on the functional classification of the roadway. Speed limits are also based on the posted speed limit, not the prevailing operating vehicle speeds which can vary greatly. Such discrepancies between actual and assumed conditions could introduce margins of error in some cases. As data quality improves with future iterations, the LTS scoring accuracy will also improve.Generalizations - MDOT’s LTS methodology follows industry standards but needs to account for varying roadway conditions and data reliability from various sources. The LTS methodology aims to accurately capture Maryland’s bicycle conditions and infrastructure but must consider data maintenance requirements. To limit data maintenance generalizations were made in the methodology so that a score could be assigned. Specifically, factors such as intersections, intersection approaches and bike lane blockages are not included in this initial analysis. LTS scores may be adjusted in the future based on MDOT review, updated industry standards, and additional LTS metrics being included in OMOC such as parking and buffer widths.Timestamped - As the LTS score is derived from a dynamic linear referencing system (LRS), any LTS analysis performed reflects the data available in OMOC. Each analysis must be considered ‘timestamped’ and becoming less reliable with age. As variables within OMOC change, whether through documented roadway construction, bikeway improvements or a speed limit reduction, LTS scores will also change. Fortunately, as this data is updated in the linear referencing system, the data becomes more reliable and LTS scores become more accurate.Presence and type of bicycle facilitySpeed limitNumber of Through Lanes/Traffic VolumeTraditionally, the Level of Traffic Stress (LTS) (scale “1” to “4”) is a measure for assessing the quality of the roadway network for its comfort with various bicycle users. The lower the LTS score, the more inviting the bicycle facility is for more audiences.LTS Methodology (Overview)MDOT’s LTS methodology is based on the metrics established by the Mineta Transportation Institute (MTI) Report 11-19 “Low-Stress Bicycling and Network Connectivity (May 2012) - additional criteria refined by Dr. Peter G. Furth (June 2017) below and Montgomery County's Revised Level of Traffic Stress.Shared-use Path Data Development and Complimentary Road Separated Bike Routes DatasetA complimentary dataset – Road Separated Bike Routes, was completed prior to the roadway dataset and is included in this application. It is also provided to the public via (https://maryland.maps.arcgis.com/home/item.html?id=1e12f2996e76447aba89099f41b14359). This first dataset is an inventory of all shared-use paths open to public, two-way bicycle access which contribute to the bicycle transportation network. Shared-use paths and sidepaths were assigned an LTS score of “0” to indicate minimal interaction with motor vehicle traffic. Many paved loop trails entirely within parks, which had no connection to the adjacent roadway network, were not included but may be included in future iterations. Sidepaths, where a shared-use path runs parallel to an adjacent roadway, are included in this complimentary Road Separated Bike Routes Dataset. Sidepaths do not have as an inviting biking environment as shared-use paths with an independent alignment due to the proximity of motor vehicle traffic in addition to greater likelihood of intersections with more roadways and driveways. Future iterations of the LTS will assign an LTS score of “1” to sidepaths. On-street Bicycle Facility Data DevelopmentThis second dataset includes all on-road bicycle facilities which have a designated roadway space for bicycle travel including bike lanes and protected bike lanes. Marked shared lanes in which bicycle and motor vehicle traffic share travel lanes were not included. Shared lanes, whether sharrows, bike boulevards or signed routes were inventoried but treated as mixed traffic for LTS analysis. The bicycle facilities included in the analysis include:Standard Bike Lanes – A roadway lane designated for bicycle travel at least 5-feet-wide. Bike lanes may be located against the curb or between a parking lane and a motor vehicle travel lane. Buffered bike lanes without vertical separation from motor vehicle traffic are included in this category. Following AASHTO and MDOT SHA design standards, bike lanes are assumed to be at least 5-feet-wide even through some existing bike lanes are less than 5-feet-wide.Protected Bike Lanes – lanes located within the street but are separated from motor vehicle travel lanes by a vertical buffer, whether by a row of parked cars, flex posts or concrete planters.Shoulders – Roadway shoulders are commonly used by bicycle traffic. As such, roadways with shoulders open to bicycle traffic were identified and rated for LTS in relation to adjacent traffic speeds and volumes as well as the shoulder width. Shoulders less than 5-feet-wide, the standard bike lane width, were excluded from analysis and these roadway segments were treated as mixed traffic.The Office of Highway Development at MDOT SHA provided the on-street bicycle facility inventory data for state roadways. The shared-use path inventory and on-street bicycle facility inventory was compiled from local jurisdiction’s open-source download or shared form the GIS/IT departments. Before integrating into OMOC, these datasets were verified by conducting desktop surveys and site visits, and by consulting with local officials and residents.-----------------------------------------------------------------------------------------------------------Inquiries? Contact Us!For Methodology: Contact Nate Evans (nevans1@mdot.maryland.gov)For GIS \ Data: Contact Andrew Bernish (abernish@mdot.maryland.gov)