Be ready for a cookieless internet while capturing anonymous website traffic data!

By installing the resolve pixel onto your website, business owners can start to put a name to the activity seen in analytics sources (i.e. GA4). With capture/resolve, you can identify up to 40% or more of your website traffic. Reach customers BEFORE they are ready to reveal themselves to you and customize messaging toward the right product or service.

This product will include Anonymous IP Data and Web Traffic Data for B2B2C.

Get a 360 view of the web traffic consumer with their business data such as business email, title, company, revenue, and location.

Super easy to implement and extraordinarily fast at processing, business owners are thrilled with the enhanced identity resolution capabilities powered by VisitIQ's First Party Opt-In Identity Platform. Capture/resolve and identify your Ideal Customer Profiles to customize marketing. Identify WHO is looking, WHAT they are looking at, WHERE they are located and HOW the web traffic came to your site.

Create segments based on specific demographic or behavioral attributes and export the data as a .csv or through S3 integration.

Check our product that has the most accurate Web Traffic Data for the B2B2C market.

Explore our detailed website traffic dataset featuring key metrics like page views, session duration, bounce rate, traffic source, and conversion rates.

Daily utilization metrics for data.lacity.org and geohub.lacity.org. Updated monthly

The global market for visitor tracking software tools 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 maintain a healthy Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated market value of $12 billion by 2033. This growth is fueled by several key factors: the rising adoption of e-commerce, the increasing complexity of online marketing campaigns, the demand for personalized user experiences, and the growing availability of sophisticated analytics tools capable of providing actionable insights from website traffic data. Major trends shaping the market include the integration of AI and machine learning for predictive analytics, the increasing use of heatmaps and session recordings for detailed user behavior analysis, and a growing focus on privacy-compliant data collection methods. However, market growth faces certain restraints. Concerns around data privacy and compliance with regulations like GDPR are impacting adoption rates. Furthermore, the competitive landscape is crowded, with both established players like Google and specialized providers like Crazy Egg vying for market share. The market is segmented by solution type (e.g., website analytics, heatmap tools, session recording), deployment model (cloud-based, on-premise), enterprise size (small, medium, large), and industry vertical. Leading companies such as Crazy Egg, Mixpanel, and FullStory are constantly innovating to improve the accuracy and depth of their offerings, while smaller companies are focusing on niche functionalities to differentiate themselves. The future success of these tools depends heavily on continuing innovation in the areas of data security, user experience, and integration with other marketing platforms.

Click Web Traffic Combined with Transaction Data: A New Dimension of Shopper Insights

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. Click enhances the unparalleled accuracy of CE Transact by allowing investors to delve deeper and browse further into global online web traffic for CE Transact companies and more. Leverage the unique fusion of web traffic and transaction datasets to understand the addressable market and understand spending behavior on consumer and B2B websites. See the impact of changes in marketing spend, search engine algorithms, and social media awareness on visits to a merchant’s website, and discover the extent to which product mix and pricing drive or hinder visits and dwell time. Plus, Click uncovers a more global view of traffic trends in geographies not covered by Transact. Doubleclick into better forecasting, with Click.

Consumer Edge’s Click is available in machine-readable file delivery and enables: • Comprehensive Global Coverage: Insights across 620+ brands and 59 countries, including key markets in the US, Europe, Asia, and Latin America. • Integrated Data Ecosystem: Click seamlessly maps web traffic data to CE entities and stock tickers, enabling a unified view across various business intelligence tools. • Near Real-Time Insights: Daily data delivery with a 5-day lag ensures timely, actionable insights for agile decision-making. • Enhanced Forecasting Capabilities: Combining web traffic indicators with transaction data helps identify patterns and predict revenue performance.

Use Case: Analyze Year Over Year Growth Rate by Region

Problem A public investor wants to understand how a company’s year-over-year growth differs by region.

Solution The firm leveraged Consumer Edge Click data to: • Gain visibility into key metrics like views, bounce rate, visits, and addressable spend • Analyze year-over-year growth rates for a time period • Breakout data by geographic region to see growth trends

Metrics Include: • Spend • Items • Volume • Transactions • Price Per Volume

Inquire about a Click subscription to perform more complex, near real-time analyses on public tickers and private brands as well as for industries beyond CPG like: • Monitor web traffic as a leading indicator of stock performance and consumer demand • Analyze customer interest and sentiment at the brand and sub-brand levels

Consumer Edge offers a variety of datasets covering the US, Europe (UK, Austria, France, Germany, Italy, Spain), and across the globe, with subscription options serving a wide range of business needs.

Consumer Edge is the Leader in Data-Driven Insights Focused on the Global Consumer

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.

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.

As of the last quarter of 2023, ***** percent of web traffic in the United States originated from mobile devices, down from ***** percent in the fourth quarter of 2022. In comparison, over half of web traffic worldwide was generated via mobile in the last examined period.

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.

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

The Website Traffic Generator market is projected to reach USD 2,844.3 million by 2033, expanding at a CAGR of 12.1% from 2023 to 2033. Rising demand for online traffic, increasing digital marketing investments, and the advent of new technologies are major factors driving market growth. The increasing adoption of social media platforms and the growing popularity of mobile internet usage further contribute to the demand for website traffic generators. The market is segmented by type into Referral Traffic Generators, Social Media Traffic Generators, Direct Traffic Generators, and Others. Referral Traffic Generators currently dominate the market, accounting for a significant share of the total revenue. However, Social Media Traffic Generators are expected to witness the fastest growth over the forecast period due to the growing popularity of social media platforms as a source of website traffic. The market is also segmented by application into Individual and Enterprise, with the Enterprise segment holding a larger market share. Key players in the market include Babylon Traffic, SparkTraffic, Getthit, TrafficApe, Somiibo, Serp Empire, EasyHits4U, Growtraffic, 10KHits, Traffup, Torpedo Traffic, YOOtraffic, SigmaTraffic, TheTraffic, WebTrafficly, Traffic Creator, and Apex Traffic.

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Global network traffic analytics Industry Overview

Technavio’s analysts have identified the increasing use of network traffic analytics solutions to be one of major factors driving market growth. With the rapidly changing IT infrastructure, security hackers can steal valuable information through various modes. With the increasing dependence on web applications and websites for day-to-day activities and financial transactions, the instances of theft have increased globally. Also, the emergence of social networking websites has aided the malicious attackers to extract valuable information from vulnerable users. The increasing consumer dependence on web applications and websites for day-to-day activities and financial transactions are further increasing the risks of theft. This encourages the organizations to adopt network traffic analytics solutions.

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See the complete table of contents and list of exhibits, as well as selected illustrations and example pages from this report.

Companies covered

The network traffic analytics market is fairly concentrated due to the presence of few established companies offering innovative and differentiated software and services. By offering a complete analysis of the competitiveness of the players in the network monitoring tools market offering varied software and services, this network traffic analytics industry analysis report will aid clients identify new growth opportunities and design new growth strategies.

The report offers a complete analysis of a number of companies including:

• Allot
• Cisco Systems
• IBM
• Juniper Networks
• Microsoft
• Symantec

Network traffic analytics market growth based on geographic regions

• Americas
• APAC
• EMEA

With a complete study of the growth opportunities for the companies across regions such as the Americas, APAC, and EMEA, our industry research analysts have estimated that countries in the Americas will contribute significantly to the growth of the network monitoring tools market throughout the predicted period.

Network traffic analytics market growth based on end-user

• Telecom
• BFSI
• Healthcare
• Media and entertainment

According to our market research experts, the telecom end-user industry will be the major end-user of the network monitoring tools market throughout the forecast period. Factors suc

This is a dataset of Tor cell file extracted from browsing simulation using Tor Browser. The simulations cover both desktop and mobile webpages. The data collection process was using WFP-Collector tool (https://github.com/irsyadpage/WFP-Collector). All the neccessary configuration to perform the simulation as detailed in the tool repository.The webpage URL is selected by using the first 100 website based on: https://dataforseo.com/free-seo-stats/top-1000-websites.Each webpage URL is visited 90 times for each deskop and mobile browsing mode.

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.

Explore the historical Whois records related to free-web-traffic-report.com (Domain). Get insights into ownership history and changes over time.

This is a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from HERE (www.HERE.com). HERE collects billions of GPS and cell phone probe records per month and, where available, uses sensor and toll-tag data to augment the probe data collected. An advanced algorithm compiles the data and computes accurate speeds. Historical traffic is based on the average of observed speeds over the past three years. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map image can be requested for the current time and any time in the future. A map image for a future request might be used for planning purposes. The map layer also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

Between July and September 2022, BYJU's emerged as the top Ed Tech platform for K12 and test preparation In India. It recorded approximately *** million website visits. Following closely behind was Toppr.com, with around *** million visits during the same period.

A dataset comparing features, pricing, and ratings of the top 4 traffic bots in 2025: SparkTraffic (4.5/5), TrafficBot.co (2.5/5), Traffic-Bot.com (3.0/5), and EpicTrafficBot (3.0/5).

Unlock the Power of Behavioural Data with GDPR-Compliant Clickstream Insights.

Swash clickstream data offers a comprehensive and GDPR-compliant dataset sourced from users worldwide, encompassing both desktop and mobile browsing behaviour. Here's an in-depth look at what sets us apart and how our data can benefit your organisation.

User-Centric Approach: Unlike traditional data collection methods, we take a user-centric approach by rewarding users for the data they willingly provide. This unique methodology ensures transparent data collection practices, encourages user participation, and establishes trust between data providers and consumers.

Wide Coverage and Varied Categories: Our clickstream data covers diverse categories, including search, shopping, and URL visits. Whether you are interested in understanding user preferences in e-commerce, analysing search behaviour across different industries, or tracking website visits, our data provides a rich and multi-dimensional view of user activities.

GDPR Compliance and Privacy: We prioritise data privacy and strictly adhere to GDPR guidelines. Our data collection methods are fully compliant, ensuring the protection of user identities and personal information. You can confidently leverage our clickstream data without compromising privacy or facing regulatory challenges.

Market Intelligence and Consumer Behaviuor: Gain deep insights into market intelligence and consumer behaviour using our clickstream data. Understand trends, preferences, and user behaviour patterns by analysing the comprehensive user-level, time-stamped raw or processed data feed. Uncover valuable information about user journeys, search funnels, and paths to purchase to enhance your marketing strategies and drive business growth.

High-Frequency Updates and Consistency: We provide high-frequency updates and consistent user participation, offering both historical data and ongoing daily delivery. This ensures you have access to up-to-date insights and a continuous data feed for comprehensive analysis. Our reliable and consistent data empowers you to make accurate and timely decisions.

Custom Reporting and Analysis: We understand that every organisation has unique requirements. That's why we offer customisable reporting options, allowing you to tailor the analysis and reporting of clickstream data to your specific needs. Whether you need detailed metrics, visualisations, or in-depth analytics, we provide the flexibility to meet your reporting requirements.

Data Quality and Credibility: We take data quality seriously. Our data sourcing practices are designed to ensure responsible and reliable data collection. We implement rigorous data cleaning, validation, and verification processes, guaranteeing the accuracy and reliability of our clickstream data. You can confidently rely on our data to drive your decision-making processes.

In November 2021, mobile devices accounted for nearly ** percent of the web traffic to Google.com in Kenya. The website had the highest number of total visits in the country. Among the leading websites, most of them had a higher share of traffic from mobile. Youtube.com was an exception, with only ********* of its traffic originating from mobile devices.