Dataset Overview

This dataset provides detailed information on website traffic, including page views, session duration, bounce rate, traffic source, time spent on page, previous visits, and conversion rate.

Dataset Description

• Page Views: The number of pages viewed during a session.
• Session Duration: The total duration of the session in minutes.
• Bounce Rate: The percentage of visitors who navigate away from the site after viewing only one page.
• Traffic Source: The origin of the traffic (e.g., Organic, Social, Paid).
• Time on Page: The amount of time spent on the specific page.
• Previous Visits: The number of previous visits by the same visitor.
• Conversion Rate: The percentage of visitors who completed a desired action (e.g., making a purchase).

Data Summary

• Total Records: 2000
• Total Features: 7

Key Features

1. Page Views: This feature indicates the engagement level of the visitors by showing how many pages they visit during their session.
2. Session Duration: This feature measures the length of time a visitor stays on the website, which can indicate the quality of the content.
3. Bounce Rate: A critical metric for understanding user behavior. A high bounce rate may indicate that visitors are not finding what they are looking for.
4. Traffic Source: Understanding where your traffic comes from can help in optimizing marketing strategies.
5. Time on Page: This helps in analyzing which pages are retaining visitors' attention the most.
6. Previous Visits: This can be used to analyze the loyalty of visitors and the effectiveness of retention strategies.
7. Conversion Rate: The ultimate metric for measuring the effectiveness of the website in achieving its goals.

Usage

This dataset can be used for various analyses such as:

• Identifying key drivers of engagement and conversion.
• Analyzing the effectiveness of different traffic sources.
• Understanding user behavior patterns and optimizing the website accordingly.
• Improving marketing strategies based on traffic source performance.
• Enhancing user experience by analyzing time spent on different pages.

Acknowledgments

This dataset was generated for educational purposes and is not from a real website. It serves as a tool for learning data analysis and machine learning techniques.

Learn how to measure dark social traffic in Google Analytics, which drives 40% of blog visitors. Essential tracking insights for startup founders and content marketers.

In the measured time period, September 2025 saw the highest figures for online traffic to the C2C fashion marketplace depop.com. According to the data, desktop and mobile visits to depop.com reached **** million visits that month.

In the measured time period, November 2024 saw the highest figures for online traffic to the C2C fashion marketplace vestiairecollective.com. According to the data, desktop and mobile visits to vestiairecollective.com reached **** million visits that month.

Web traffic statistics for the top 2000 most visited pages on nyc.gov by month.

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

Comprehensive dataset analyzing Amazon's daily website visits, traffic patterns, seasonal trends, and comparative analysis with other ecommerce platforms based on May 2025 data.

Internet traffic volume measures global IP traffic, or the amount of data being sent and received over the internet globally each month. Data and forecasts are sourced from Cisco Systems Inc.

Management Measures for Internet Traffic Records..

A collection of Web (HTTP) requests for the month of November 2009 originating from Indiana Univesity.

This dataset was used in the Data Visualization Challenge at WebSci 2014 in Bloomington, Indiana. It is a sample of the larger Indiana University Click dataset.

In the measured time period, July 2025 saw the highest figures for online traffic to the fast fashion marketplace shein.com. According to the data, desktop and mobile visits to shein.com reached almost *** million visits that month. Shein's valuation Shein is among the private startups that rapidly reached a valuation of over *** billion dollars, otherwise known as unicorns. Among the top unicorn companies ranked in 2024, Shein was fifth with a total valuation of over ** billion U.S. dollars. In 2023, Shein was also the most visited fashion and apparel website worldwide, outpacing big names such as Nike and Zara. Global unicorn landscape: U.S. leads in numbers As of February 2024, the United States was the country with the most unicorn companies, ***, followed by China at ***. Although China does not have the most unicorns, ByteDance, the Chinese tech company that owns TikTok, had the highest valuation worldwide. Software and finance are the most likely industries for unicorn companies to form.

Representative applications that can directly collect 5G da-tasets from mobile terminals without using specialized equipment include G-NetTrack Pro and PCAPdroid. The for-mer allows for the monitoring and logging of the header and payload information of the medium access control (MAC) frame passing through the 5G air interface. The latter is an open-source network capture and monitoring tool that works without root privileges, analyzing connections made by ap-plications installed on the user's mobile device. The latter can also dump mobile traffic to PCAP (also known as libpcap) and send it to the well-known Wireshark for further analysis. We created 5G datasets by measuring 5G traffic directly from a major mobile operator in South Korea. The model name of the mobile terminal used for traffic measurement is the Samsung Galaxy A90 5G, and it was equipped with a Qualcomm Snapdragon X50 5G modem. The packet sniffer software used for traffic measurement, PCAPdroid, was in-stalled in the terminal through Google play. Traffic was measured sequentially per application on two stationary ter-minals (only one terminal was used for non-interactive ser-vices) with no background traffic. The collected dataset is representative resource-intensive video traffic that has the greatest impact on 5G network planning and provisioning, and background traffic was not mixed to measure the unique characteristics of each type of traffic. The video streaming dataset includes data directly meas-ured while watching Netflix and Amazon Prime, which are representative over-the-top (OTT) services, on mobile devic-es. The live streaming dataset was measured while watching YouTube Live and South Korea's representative live broad-casts (Naver NOW and Afreeca TV). Video conferencing data were measured by holding an actual meeting on the widely used Zoom, MS Teams, and Google Meet platform. Two types of metaverse traffic were acquired: Zepeto and Roblox. Zepeto traffic was collected while staying in the 'camping world' for 15 hours. Roblox traffic was collected over 25 hours of playing the 'Collect All Pets' game using an auto clicker. We collected two types of mobile network gaming traffic. The first was cloud gaming, an online game setup that runs video games on remote servers and streams them direct-ly to the user's device. The second was a traditional mobile game connected to the Internet. The dataset was collected from May to October 2022, is a massive 328 hours in total, and is provided in the csv file format. The dataset we collected is a timestamp-mapped time series dataset with packet header information, and traffic analysis by application is possible because it includes source and destination addresses. To make it more usable as a traffic source model, Section III describes how to use it as a training dataset for the traffic simulator platform's source generator.

A 5G traffic dataset measured by PCAPdroid has been re-leased and can be used as a training dataset for various ML models. However, since the size of this dataset is very large, it is inconvenient to handle, and additional data preprocessing is required to use it for its intended purpose.

This data set can be used to learn GANs, time-series forcasting deep learning models.

Our implementation is given on GitHub. https://github.com/0913ktg/5G-Traffic-Generator

Context

This file contains 5 years of daily time series data for several measures of traffic on a statistical forecasting teaching notes website whose alias is statforecasting.com. The variables have complex seasonality that is keyed to the day of the week and to the academic calendar. The patterns you you see here are similar in principle to what you would see in other daily data with day-of-week and time-of-year effects. Some good exercises are to develop a 1-day-ahead forecasting model, a 7-day ahead forecasting model, and an entire-next-week forecasting model (i.e., next 7 days) for unique visitors.

Content

The variables are daily counts of page loads, unique visitors, first-time visitors, and returning visitors to an academic teaching notes website. There are 2167 rows of data spanning the date range from September 14, 2014, to August 19, 2020. A visit is defined as a stream of hits on one or more pages on the site on a given day by the same user, as identified by IP address. Multiple individuals with a shared IP address (e.g., in a computer lab) are considered as a single user, so real users may be undercounted to some extent. A visit is classified as "unique" if a hit from the same IP address has not come within the last 6 hours. Returning visitors are identified by cookies if those are accepted. All others are classified as first-time visitors, so the count of unique visitors is the sum of the counts of returning and first-time visitors by definition. The data was collected through a traffic monitoring service known as StatCounter.

Inspiration

This file and a number of other sample datasets can also be found on the website of RegressIt, a free Excel add-in for linear and logistic regression which I originally developed for use in the course whose website generated the traffic data given here. If you use Excel to some extent as well as Python or R, you might want to try it out on this dataset.

The city is using Travel Time Index as a measure to quantify traffic delay in the city. The Travel Time Index is the ratio of the travel time during the peak period to the time required to make the same trip at free-flow speeds. It should be noted that this data is subject to seasonal variations. The 2020 Q2 and Q3 data includes the summer months when traffic volumes are lower, thus the Travel Time Index is improved in these quarters. The performance measure page is available at 3.27 Traffic Delay Reduction. Additional Information Source: Bluetooth ARID sensors Contact (author): Cathy Hollow Contact E-Mail (author): catherine_hollow@tempe.gov Contact (maintainer): Contact E-Mail (maintainer): Data Source Type: Table, CSV Preparation Method: Peak period data is manually extracted. The travel time index calculation is the peak period data divided by the free flow data (constant per segment). Publish Frequency: Quarterly Publish Method: Manual Data Dictionary

This statistic presents the web traffic to Glassdoor.com from the United States as of April 2018. During the measured ** days, the online employer review platform had **** million views from mobile connections.

In 2024, most of the global website traffic was still generated by humans, but bot traffic is constantly growing. Fraudulent traffic through bad bot actors accounted for 37 percent of global web traffic in the most recently measured period, representing an increase of 12 percent from the previous year. Sophistication of Bad Bots on the rise The complexity of malicious bot activity has dramatically increased in recent years. Advanced bad bots have doubled in prevalence over the past 2 years, indicating a surge in the sophistication of cyber threats. Simultaneously, the share of simple bad bots drastically increased over the last years, suggesting a shift in the landscape of automated threats. Meanwhile, areas like food and groceries, sports, gambling, and entertainment faced the highest amount of advanced bad bots, with more than 70 percent of their bot traffic affected by evasive applications. Good and bad bots across industries The impact of bot traffic varies across different sectors. Bad bots accounted for over 50 percent of the telecom and ISPs, community and society, and computing and IT segments web traffic. However, not all bot traffic is considered bad. Some of these applications help index websites for search engines or monitor website performance, assisting users throughout their online search. Therefore, areas like entertainment, food and groceries, and even areas targeted by bad bots themselves experienced notable levels of good bot traffic, demonstrating the diverse applications of benign automated systems across different sectors.

Web Analytics Market size was valued at USD 6.16 Billion in 2024 and is projected to reach USD 24.07 Billion by 2032, growing at a CAGR of 18.58% during the forecast period 2026-2032.Global Web Analytics Market DriversThe digital landscape is in constant flux, and at its core, understanding user behavior is paramount for any business aiming to thrive. This imperative fuels the robust expansion of the Web Analytics Market, driven by a confluence of technological advancements, evolving business needs, and shifting consumer behaviors. Let's delve into the major forces propelling this vital industry forward.Digitalization and the Explosive Growth of Online Presence: The most fundamental driver is the relentless march of digitalization. Businesses across every sector are establishing, expanding, and optimizing their online presence, whether through sophisticated e-commerce platforms, informative corporate websites, or engaging mobile applications. As more operations, customer interactions, and commerce migrate to the digital realm, the sheer volume of online activity creates an insatiable demand for tools that can decipher user journeys, measure website performance, and identify areas for improvement. This foundational shift necessitates web analytics to transform raw digital interactions into actionable insights, making it indispensable for strategic decision-making in the modern business environment.The Imperative for Data-Driven Decision Making: In today's competitive landscape, gut feelings and anecdotal evidence are no longer sufficient. Businesses are increasingly recognizing the critical importance of basing their strategies on empirical data. Web analytics provides this crucial foundation, offering deep insights into customer behavior, site usage patterns, conversion funnels, and potential drop-off points. From optimizing marketing spend to refining product offerings and enhancing user experience, data-driven decision-making, powered by comprehensive web analytics, allows companies to minimize risks, maximize opportunities, and achieve measurable growth, thereby solidifying its position as a core business intelligence tool.Proliferation of Mobile Devices and Mobile Web Traffic: The smartphone revolution has profoundly reshaped how users interact with the internet. With billions of people globally accessing the web predominantly via mobile devices and tablets, understanding mobile-specific behaviors has become a paramount concern. Web analytics tools are evolving rapidly to effectively capture and analyze interactions across a myriad of devices, operating systems, and browser types. This includes tracking mobile app usage, responsive website performance, and ensuring a seamless cross-device user experience. The pervasive nature of mobile traffic means that robust mobile analytics capabilities are no longer a luxury but a necessity for any comprehensive web analytics solution.

Each point holds the measured average daily traffic volume for that site during the most recent survey. The site type attribute defines whether a classifier was used for the traffic count.Historical count data is available on the ADT website located at https://apps.co.marion.or.us/adt/

This dataset encompasses mobile web clickstream behavior on any browser, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). Use it for measurement, attribution or path to purchase and consumer journey understanding. Full URL deliverable available including searches.