This anonymized data set consists of one month's (October 2018) web tracking data of 2,148 German users. For each user, the data contains the anonymized URL of the webpage the user visited, the domain of the webpage, category of the domain, which provides 41 distinct categories. In total, these 2,148 users made 9,151,243 URL visits, spanning 49,918 unique domains. For each user in our data set, we have self-reported information (collected via a survey) about their gender and age.

We acknowledge the support of Respondi AG, which provided the web tracking and survey data free of charge for research purposes, with special thanks to François Erner and Luc Kalaora at Respondi for their insights and help with data extraction.

The data set is analyzed in the following paper:

• Kulshrestha, J., Oliveira, M., Karacalik, O., Bonnay, D., Wagner, C. "Web Routineness and Limits of Predictability: Investigating Demographic and Behavioral Differences Using Web Tracking Data." Proceedings of the International AAAI Conference on Web and Social Media. 2021. https://arxiv.org/abs/2012.15112.

The code used to analyze the data is also available at https://github.com/gesiscss/web_tracking.

If you use data or code from this repository, please cite the paper above and the Zenodo link.

Users are advised that some domains in this data set may link to potentially questionable or inappropriate content. The domains have not been individually reviewed, as content verification was not the primary objective of this data set. Therefore, user discretion is strongly recommended when accessing or scraping any content from these domains.

Cookie and Website Tracker Scanning Software Market size was valued at USD 1.55 Billion in 2023 and is projected to reach USD 5.75 Billion by 2030, growing at a CAGR of 15.9% during the forecast period 2024-2030.Global Cookie and Website Tracker Scanning Software Market DriversThe growth and development of the Cookie and Website Tracker Scanning Software Market is attributed to certain main market drivers. These factors have a big impact on how integrated gas systems are demanded and adopted in different sectors. Several of the major market forces are as follows:Regulations and Privacy Concerns: Some international rules, including the California Consumer Privacy Act (CCPA) in the United States and the General Data Protection Regulation (GDPR) in the European Union, have been introduced as a result of growing concerns about online privacy and data protection. The need for scanning software that assists in identifying and managing cookies and website trackers may be driven by the necessity for organizations to comply with these requirements.Knowledge of the Consumer: Growing consumer awareness of data privacy and online tracking issues can encourage companies to invest in solutions that meet customer expectations and improve transparency. The demand for software that offers visibility and control over cookies and trackers may rise as a result of this awareness.Corporate Observance: Corporate governance and compliance are becoming more and more important to businesses. Companies that want to comply with data privacy laws and regulations may install scanning software to keep an eye on and control the use of cookies and trackers on their websites.Increasing Amount of Websites and Online Communities The sheer number of websites and online services has expanded along with the ongoing expansion of digital platforms and online enterprises. Tools that can effectively scan and handle the tracking features found on these websites are therefore more important.Technological Progress: Further developments in artificial intelligence and machine learning, among other areas of technology, may lead to the creation of increasingly complex scanning software. Adoption in the market may be fueled by enhanced features like automated cookie and tracker identification and analysis.

Discover the booming website visitor tracking software market! Our analysis reveals a $5 billion market in 2025, projected to reach $15 billion by 2033, driven by digital marketing, data-driven decisions, and AI-powered analytics. Learn about key players, market trends, and regional insights.

The size of the Website Visitor Tracking Software market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

PredictLeads Job Openings Data provides high-quality hiring insights sourced directly from company websites - not job boards. Using advanced web scraping technology, our dataset offers real-time access to job trends, salaries, and skills demand, making it a valuable resource for B2B sales, recruiting, investment analysis, and competitive intelligence.

Key Features:

✅232M+ Job Postings Tracked – Data sourced from 92 Million company websites worldwide. ✅7,1M+ Active Job Openings – Updated in real-time to reflect hiring demand. ✅Salary & Compensation Insights – Extract salary ranges, contract types, and job seniority levels. ✅Technology & Skill Tracking – Identify emerging tech trends and industry demands. ✅Company Data Enrichment – Link job postings to employer domains, firmographics, and growth signals. ✅Web Scraping Precision – Directly sourced from employer websites for unmatched accuracy.

Primary Attributes:

• id (string, UUID) – Unique identifier for the job posting.
• type (string, constant: "job_opening") – Object type.
• title (string) – Job title.
• description (string) – Full job description, extracted from the job listing.
• url (string, URL) – Direct link to the job posting.
• first_seen_at – Timestamp when the job was first detected.
• last_seen_at – Timestamp when the job was last detected.
• last_processed_at – Timestamp when the job data was last processed.

Job Metadata:

• contract_types (array of strings) – Type of employment (e.g., "full time", "part time", "contract").
• categories (array of strings) – Job categories (e.g., "engineering", "marketing").
• seniority (string) – Seniority level of the job (e.g., "manager", "non_manager").
• status (string) – Job status (e.g., "open", "closed").
• language (string) – Language of the job posting.
• location (string) – Full location details as listed in the job description.
• Location Data (location_data) (array of objects)
• city (string, nullable) – City where the job is located.
• state (string, nullable) – State or region of the job location.
• zip_code (string, nullable) – Postal/ZIP code.
• country (string, nullable) – Country where the job is located.
• region (string, nullable) – Broader geographical region.
• continent (string, nullable) – Continent name.
• fuzzy_match (boolean) – Indicates whether the location was inferred.

Salary Data (salary_data)

• salary (string) – Salary range extracted from the job listing.
• salary_low (float, nullable) – Minimum salary in original currency.
• salary_high (float, nullable) – Maximum salary in original currency.
• salary_currency (string, nullable) – Currency of the salary (e.g., "USD", "EUR").
• salary_low_usd (float, nullable) – Converted minimum salary in USD.
• salary_high_usd (float, nullable) – Converted maximum salary in USD.
• salary_time_unit (string, nullable) – Time unit for the salary (e.g., "year", "month", "hour").

Occupational Data (onet_data) (object, nullable)

• code (string, nullable) – ONET occupation code.
• family (string, nullable) – Broad occupational family (e.g., "Computer and Mathematical").
• occupation_name (string, nullable) – Official ONET occupation title.

Additional Attributes:

• tags (array of strings, nullable) – Extracted skills and keywords (e.g., "Python", "JavaScript").

📌 Trusted by enterprises, recruiters, and investors for high-precision job market insights.

PredictLeads Dataset: https://docs.predictleads.com/v3/guide/job_openings_dataset

Stores submission control numbers and status inserts to support E-Services Control and Tracking.

600 000 000 web traffic records normalized into MySQL tables using TokuDB storage, complete with original web server response codes. Suitable for browser data and trend analysis as well as AI training of exploit and bot detection algorithms. The data had been collected from multiple Apache 2.x web servers across 8000+ domain names with special care for GDPR compliance.

Discover the booming web tracking technologies market! This comprehensive analysis reveals a $25 billion market in 2025, projected to reach over $75 billion by 2033, with a 15% CAGR. Explore key drivers, trends, and restraints shaping this dynamic sector. Learn about leading players like Google and Adobe, and understand regional market shares.

Have you ever seen yourself surfing Amazon for headphones, and now every page you visit offers advertisements for headphones Nowadays, trackers are incorporated on websites. These trackers collect data under the pretense of improving the user experience. They are, however, used to profile a user's attributes and behaviors by analyzing location history, browsing history, and other data to deduce your gender, ethnicity, interests, and habits. This online profile is then used for targeted advertising.

This dataset is comprised of the 585 popular websites from 105 different countries.

The dataset has the following columns: 1. Index 2. W_NAME 3. T_URL 4. T_CAT 5. Avg_Daily_Visitors 6. Avg_Daily_Pageviews 7. Location 8. Hosted_by 9. country 10. tracker_count 11. tracker_org

For more details on the dataset, viewing the study done on the data please visit https://github.com/k-lohani/state-of-online-tracking

This data about nola.gov provides a window into how people are interacting with the the City of New Orleans online. The data comes from a unified Google Analytics account for New Orleans. We do not track individuals and we anonymize the IP addresses of all visitors.

The Top Websites in the World

How They Change Over Time

About this dataset

This dataset consists of the top 50 most visited websites in the world, as well as the category and principal country/territory for each site. The data provides insights into which sites are most popular globally, and what type of content is most popular in different parts of the world

How to use the dataset

This dataset can be used to track the most popular websites in the world over time. It can also be used to compare website popularity between different countries and categories

Research Ideas

• To track the most popular websites in the world over time
• To see how website popularity changes by region
• To find out which website categories are most popular

Acknowledgements

Dataset by Alexa Internet, Inc. (2019), released on Kaggle under the Open Data Commons Public Domain Dedication and License (ODC-PDDL)

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: df_1.csv | Column name | Description | |:--------------------------------|:---------------------------------------------------------------------| | Site | The name of the website. (String) | | Domain Name | The domain name of the website. (String) | | Category | The category of the website. (String) | | Principal country/territory | The principal country/territory where the website is based. (String) |

This collection contains analysis scripts and raw data to reproduce our analysis for the web cookie measurements to investigate the prevalence of intractable cookies in the wild. More information and updates can be found on the project's website: https://bannerclick.github.io/

A web service to process requests from applications for submission of control numbers and status inserts.

In the face of ubiquitous surveillance and people’s loss of control over their personal data, informational privacy is widely perceived as irretrievably “dead”. Illustrating the complexity of the problem and contributing to the search for realistic solutions, this cumulative dissertation deals with privacy threats posed by mobile apps, web tracking, and embedded sensors. The dissertation starts with an introductory chapter that establishes the motivation and introduces the theoretical background, followed by four parts: Part I focuses on privacy threats posed by sensors embedded into consumer devices. First, three studies provide an overview of the rich variety of personal information that can be inferred from eye-tracking data, accelerometer data, and voice recordings. Second, a study on users’ perceptions about the privacy impacts of voice and speech analysis is presented. Third, the feasibility and detectability of smartphone-based eavesdropping is investigated, addressing accelerometers as a possible eavesdropping channel. Fourth, the privacy-invading potential of video games and their associated sensor-equipped hardware is explored. Part II focuses on data practices of mobile apps. An undercover investigation is presented, probing whether app vendors comply with transparency obligations prescribed by EU’s General Data Protection Regulation. While the law grants consumers the right to access the personal data that companies hold about them, the study reveals severe obstacles to exercising this right in practice. Part III presents two novel approaches for the detection and exposure of hidden web tracking. First, a browser extension is proposed that records internet browsing sessions to obtain training data for automated web-tracking detection. Artificial data, which is commonly used for this purpose, has severe drawbacks that can be overcome by using real-world browsing data. Second, methods are explored to “sonify” web-tracking activity, i.e., make it audible to internet users through indicative sounds and melodies. Furthermore, in reference to topics covered in Part I, suggestions are provided as to how the range of personal information that can be inferred from different types of sensor data could be recorded in a digital database in order to be presented in an interactive and updatable form. Part IV sheds a critical light on the legal principle that people individually manage their privacy via notice and choice (“privacy self-management”), drawing on findings from the previous parts and related literature. Based on a holistic examination of its limitations, it is argued that privacy self-management does not function in practice, amounting to a major loophole in privacy law. This dissertation emphasizes the need for new ways of dealing with the excessive and obscure forms of surveillance prevalent in modern life. It adds to the academic debate and scientific literature about possible privacy threats emerging from consumer electronics as well as to exposing the failure of current laws to protect our privacy. The dissertation concludes with a discussion of overarching themes. While emphasizing the seriousness and complexity of the privacy threats under investigation, a general privacy-is-dead attitude is firmly rejected. In light of the findings, policy recommendations and possible avenues for future research are presented.

Discover the booming website visitor tracking tool market! Explore key trends, leading companies (Crazy Egg, Mixpanel, FullStory), and a projected $15B market size by 2033. Learn how this technology is transforming digital marketing and enhancing customer understanding.

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.

Do people learn about the political world through online media? We address this question by developing different exogenous measures of media exposure, drawing on three months of web-tracking data from five democracies. Our analysis distinguishes between visits to general news domains and visits to politically or content-specific articles, identified using machine learning techniques. We evaluate these measures through multiple approaches, including their ability to significantly predict political knowledge. To deepen our understanding, we analyze knowledge gains during a major, unexpected news event—the 2022 Russian invasion of Ukraine—using observed media exposure measures varying in granularity. Our findings underscore the importance of granularity: visits and time spent on Ukraine-related articles emerge as the only significant predictor of surveillance knowledge, while broader measures, such as domain-level visits, show no significant impact when controlling for self-reported exposure and other key predictors. We conclude by discussing the substantive and methodological implications of these results.

Discover the booming visitor tracking software market! This comprehensive analysis reveals key trends, growth projections (reaching $12B by 2033), leading companies (Crazy Egg, Mixpanel, etc.), and regional insights. Learn how AI, heatmaps, and session recordings are shaping the future of website analytics.

Global AI price tracking tools market size was $2.79 billion in 2024 & is projected to reach $7.30 million by 2034, CAGR of 12.80% from 2025 to 2034.

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.