As of the last quarter of 2023, 31.57 percent of web traffic in the United States originated from mobile devices, down from 49.51 percent in the fourth quarter of 2022. In comparison, over half of web traffic worldwide was generated via mobile in the last examined period.
A breakdown of the Food Hygiene Ratings website traffic data showing total number of visits, unique visitors and page views.
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General data recollected for the studio " Analysis of the Quantitative Impact of Social Networks on Web Traffic of Cybermedia in the 27 Countries of the European Union".
Four research questions are posed: what percentage of the total web traffic generated by cybermedia in the European Union comes from social networks? Is said percentage higher or lower than that provided through direct traffic and through the use of search engines via SEO positioning? Which social networks have a greater impact? And is there any degree of relationship between the specific weight of social networks in the web traffic of a cybermedia and circumstances such as the average duration of the user's visit, the number of page views or the bounce rate understood in its formal aspect of not performing any kind of interaction on the visited page beyond reading its content?
To answer these questions, we have first proceeded to a selection of the cybermedia with the highest web traffic of the 27 countries that are currently part of the European Union after the United Kingdom left on December 31, 2020. In each nation we have selected five media using a combination of the global web traffic metrics provided by the tools Alexa (https://www.alexa.com/), which ceased to be operational on May 1, 2022, and SimilarWeb (https:// www.similarweb.com/). We have not used local metrics by country since the results obtained with these first two tools were sufficiently significant and our objective is not to establish a ranking of cybermedia by nation but to examine the relevance of social networks in their web traffic.
In all cases, cybermedia whose property corresponds to a journalistic company have been selected, ruling out those belonging to telecommunications portals or service providers; in some cases they correspond to classic information companies (both newspapers and televisions) while in others they refer to digital natives, without this circumstance affecting the nature of the research proposed.
Below we have proceeded to examine the web traffic data of said cybermedia. The period corresponding to the months of October, November and December 2021 and January, February and March 2022 has been selected. We believe that this six-month stretch allows possible one-time variations to be overcome for a month, reinforcing the precision of the data obtained.
To secure this data, we have used the SimilarWeb tool, currently the most precise tool that exists when examining the web traffic of a portal, although it is limited to that coming from desktops and laptops, without taking into account those that come from mobile devices, currently impossible to determine with existing measurement tools on the market.
It includes:
Web traffic general data: average visit duration, pages per visit and bounce rate Web traffic origin by country Percentage of traffic generated from social media over total web traffic Distribution of web traffic generated from social networks Comparison of web traffic generated from social netwoks with direct and search procedures
In June 2025, DoorDash's website, doordash.com, had just under 72 million visitors globally, recording a bounce rate of approximately 34.2 percent. For comparison, web traffic figures of UberEats show lower monthly visits.
Árukereső was the most popular price comparison portal in Hungary in 2021, based on the traffic share measured by SimilarWeb. Árgép was the second most visited price comparison site over the same time period.
Across popular online marketplace websites visited by users in Australia in February 2025, temu.com registered the highest growth in its website traffic compared to the previous year, with an annual growth of over 56 percent. In comparison, ebay.com.au saw a decrease in its website traffic compared to the previous year, with an annual decrease of around 11.9 percent.
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.
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Difference uses Google Analytics as the Baseline. Results based on Paired t-Test for Hypotheses Supported.
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Comparison of definitions of total visits, unique visitors, bounce rate, and session duration conceptually and for the two analytics platforms: Google Analytics and SimilarWeb.
DataForSEO Labs API offers three powerful keyword research algorithms and historical keyword data:
• Related Keywords from the “searches related to” element of Google SERP. • Keyword Suggestions that match the specified seed keyword with additional words before, after, or within the seed key phrase. • Keyword Ideas that fall into the same category as specified seed keywords. • Historical Search Volume with current cost-per-click, and competition values.
Based on in-market categories of Google Ads, you can get keyword ideas from the relevant Categories For Domain and discover relevant Keywords For Categories. You can also obtain Top Google Searches with AdWords and Bing Ads metrics, product categories, and Google SERP data.
You will find well-rounded ways to scout the competitors:
• Domain Whois Overview with ranking and traffic info from organic and paid search. • Ranked Keywords that any domain or URL has positions for in SERP. • SERP Competitors and the rankings they hold for the keywords you specify. • Competitors Domain with a full overview of its rankings and traffic from organic and paid search. • Domain Intersection keywords for which both specified domains rank within the same SERPs. • Subdomains for the target domain you specify along with the ranking distribution across organic and paid search. • Relevant Pages of the specified domain with rankings and traffic data. • Domain Rank Overview with ranking and traffic data from organic and paid search. • Historical Rank Overview with historical data on rankings and traffic of the specified domain from organic and paid search. • Page Intersection keywords for which the specified pages rank within the same SERP.
All DataForSEO Labs API endpoints function in the Live mode. This means you will be provided with the results in response right after sending the necessary parameters with a POST request.
The limit is 2000 API calls per minute, however, you can contact our support team if your project requires higher rates.
We offer well-rounded API documentation, GUI for API usage control, comprehensive client libraries for different programming languages, free sandbox API testing, ad hoc integration, and deployment support.
We have a pay-as-you-go pricing model. You simply add funds to your account and use them to get data. The account balance doesn't expire.
This statistic shows a comparison of webpage traffic sources of Slack and Salesforce in April 2019. According to data collected by GP Bullhound, ninety-six percent of Slack's webpage traffic during the measured period was direct, compared to Salesforce's more mixed traffic strategy.
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Network traffic datasets created by Single Flow Time Series Analysis
Datasets were created for the paper: Network Traffic Classification based on Single Flow Time Series Analysis -- Josef Koumar, Karel Hynek, Tomáš Čejka -- which was published at The 19th International Conference on Network and Service Management (CNSM) 2023. Please cite usage of our datasets as:
J. Koumar, K. Hynek and T. Čejka, "Network Traffic Classification Based on Single Flow Time Series Analysis," 2023 19th International Conference on Network and Service Management (CNSM), Niagara Falls, ON, Canada, 2023, pp. 1-7, doi: 10.23919/CNSM59352.2023.10327876.
This Zenodo repository contains 23 datasets created from 15 well-known published datasets which are cited in the table below. Each dataset contains 69 features created by Time Series Analysis of Single Flow Time Series. The detailed description of features from datasets is in the file: feature_description.pdf
In the following table is a description of each dataset file:
File name | Detection problem | Citation of original raw dataset |
botnet_binary.csv | Binary detection of botnet | S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. |
botnet_multiclass.csv | Multi-class classification of botnet | S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. |
cryptomining_design.csv | Binary detection of cryptomining; the design part | Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 |
cryptomining_evaluation.csv | Binary detection of cryptomining; the evaluation part | Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 |
dns_malware.csv | Binary detection of malware DNS | Samaneh Mahdavifar et al. Classifying Malicious Domains using DNS Traffic Analysis. In DASC/PiCom/CBDCom/CyberSciTech 2021, pages 60–67. IEEE, 2021. |
doh_cic.csv | Binary detection of DoH |
Mohammadreza MontazeriShatoori et al. Detection of doh tunnels using time-series classification of encrypted traffic. In DASC/PiCom/CBDCom/CyberSciTech 2020, pages 63–70. IEEE, 2020 |
doh_real_world.csv | Binary detection of DoH | Kamil Jeřábek et al. Collection of datasets with DNS over HTTPS traffic. Data in Brief, 42:108310, 2022 |
dos.csv | Binary detection of DoS | Nickolaos Koroniotis et al. Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Gener. Comput. Syst., 100:779–796, 2019. |
edge_iiot_binary.csv | Binary detection of IoT malware | Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022. |
edge_iiot_multiclass.csv | Multi-class classification of IoT malware | Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022. |
https_brute_force.csv | Binary detection of HTTPS Brute Force | Jan Luxemburk et al. HTTPS Brute-force dataset with extended network flows, November 2020 |
ids_cic_binary.csv | Binary detection of intrusion in IDS | Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. |
ids_cic_multiclass.csv | Multi-class classification of intrusion in IDS | Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. |
ids_unsw_nb_15_binary.csv | Binary detection of intrusion in IDS | Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015. |
ids_unsw_nb_15_multiclass.csv | Multi-class classification of intrusion in IDS | Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015. |
iot_23.csv | Binary detection of IoT malware | Sebastian Garcia et al. IoT-23: A labeled dataset with malicious and benign IoT network traffic, January 2020. More details here https://www.stratosphereips.org /datasets-iot23 |
ton_iot_binary.csv | Binary detection of IoT malware | Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021 |
ton_iot_multiclass.csv | Multi-class classification of IoT malware | Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021 |
tor_binary.csv | Binary detection of TOR | Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. |
tor_multiclass.csv | Multi-class classification of TOR | Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. |
vpn_iscx_binary.csv | Binary detection of VPN | Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. |
vpn_iscx_multiclass.csv | Multi-class classification of VPN | Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. |
vpn_vnat_binary.csv | Binary detection of VPN | Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022 |
vpn_vnat_multiclass.csv | Multi-class classification of VPN | Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022 |
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Comparison of user, site, and network-centric approaches to web analytics data collection showing advantages, disadvantages, and examples of each approach at the time of the study.
In January 2025, Wolt's website, wolt.com, had just under 11 million visitors globally, recording a bounce rate of approximately 32 percent. Wolt was acquired by DoorDash in May 2022. For comparison, web traffic figures of DoorDash show nearly 72 monthly visitors.
Important Note: This item is in mature support as of June 2023 and will be retired in December 2025.This map shows traffic counts in the United States, collected through 2022 in a multiscale map. Traffic counts are widely used for site selection by real estate firms and franchises. Traffic counts are also used by departments of transportation for highway funding. This map is best viewed at large scales where you can click on each point to access up to five different traffic counts over time. At medium to small scales, comparisons along major roads are possible. The Business Basemap has been added to provide context at medium and small scales. It shows the location of businesses in the United States and helps to understand where and why traffic counts are collected and used. The pop-up is configured to display the following information:The most recent traffic countThe street name where the count was collectedThey type of count that was taken. See the methodology document for definitions of count types such as AADT - Average Annual Daily Traffic. Traffic Counts seasonally adjusted to represent the average day of the year. AADT counts represent counts taken Sunday—Saturday.A graph displaying up to five traffic counts taken at the same location over time. Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.
As of April 2024, the website pastaevangelists.com was the fastest-growing meal delivery website in the United Kingdom (U.K.) in terms of online traffic. That year, the website had an **-percent increase in its online visibility score in comparison to the previous year. The website cookfood.net was the second fastest-growing website, with a year-on-year increase of ** percent in its online traffic score.
According to a report published by DataReportal, as of December 2024, mobile phones had the highest share of web traffic at ***** percent in Thailand. By comparison, the share of web traffic on laptops and desktop computers was around ** percent.
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ProtonMail vs Gmail Statistics: ProtonMail and Gmail are both popular email services, known for their unique features, as well as several advantages and disadvantages. Proton Mail is a Swiss end-to-end encrypted email service operated by Proton AG, which also operates Proton VPN, Proton Drive, Proton Calendar, Proton Pass and Proton Wallet. In contrast, Gmail is a widely used email service provided by Google, offering a webmail interface accessible through a web browser and also available through the official mobile application.
This article provides a current analysis of both email platforms, drawing on various insights, including a basic comparison, overall market analysis, user base, usability, storage, pricing, and more.
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Average Annual Daily Traffic data for use with GIS mapping software, databases, and web applications are from Caliper Corporation and contain data on the total volume of vehicle traffic on a highway or road for a year divided by 365 days.
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Valuation Office Website Traffic and Stats . Published by Tailte Éireann – Surveying. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).This dataset provides the number of users to the Valuation Office website....
As of the last quarter of 2023, 31.57 percent of web traffic in the United States originated from mobile devices, down from 49.51 percent in the fourth quarter of 2022. In comparison, over half of web traffic worldwide was generated via mobile in the last examined period.