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 Behaviour: 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.

TagX Web Browsing Clickstream Data: Unveiling Digital Behavior Across North America and EU Unique Insights into Online User Behavior TagX Web Browsing clickstream Data offers an unparalleled window into the digital lives of 1 million users across North America and the European Union. This comprehensive dataset stands out in the market due to its breadth, depth, and stringent compliance with data protection regulations. What Makes Our Data Unique?

Extensive Geographic Coverage: Spanning two major markets, our data provides a holistic view of web browsing patterns in developed economies. Large User Base: With 300K active users, our dataset offers statistically significant insights across various demographics and user segments. GDPR and CCPA Compliance: We prioritize user privacy and data protection, ensuring that our data collection and processing methods adhere to the strictest regulatory standards. Real-time Updates: Our clickstream data is continuously refreshed, providing up-to-the-minute insights into evolving online trends and user behaviors. Granular Data Points: We capture a wide array of metrics, including time spent on websites, click patterns, search queries, and user journey flows.

Data Sourcing: Ethical and Transparent Our web browsing clickstream data is sourced through a network of partnered websites and applications. Users explicitly opt-in to data collection, ensuring transparency and consent. We employ advanced anonymization techniques to protect individual privacy while maintaining the integrity and value of the aggregated data. Key aspects of our data sourcing process include:

Voluntary user participation through clear opt-in mechanisms Regular audits of data collection methods to ensure ongoing compliance Collaboration with privacy experts to implement best practices in data anonymization Continuous monitoring of regulatory landscapes to adapt our processes as needed

Primary Use Cases and Verticals TagX Web Browsing clickstream Data serves a multitude of industries and use cases, including but not limited to:

Digital Marketing and Advertising:

Audience segmentation and targeting Campaign performance optimization Competitor analysis and benchmarking

E-commerce and Retail:

Customer journey mapping Product recommendation enhancements Cart abandonment analysis

Media and Entertainment:

Content consumption trends Audience engagement metrics Cross-platform user behavior analysis

Financial Services:

Risk assessment based on online behavior Fraud detection through anomaly identification Investment trend analysis

Technology and Software:

User experience optimization Feature adoption tracking Competitive intelligence

Market Research and Consulting:

Consumer behavior studies Industry trend analysis Digital transformation strategies

Integration with Broader Data Offering TagX Web Browsing clickstream Data is a cornerstone of our comprehensive digital intelligence suite. It seamlessly integrates with our other data products to provide a 360-degree view of online user behavior:

Social Media Engagement Data: Combine clickstream insights with social media interactions for a holistic understanding of digital footprints. Mobile App Usage Data: Cross-reference web browsing patterns with mobile app usage to map the complete digital journey. Purchase Intent Signals: Enrich clickstream data with purchase intent indicators to power predictive analytics and targeted marketing efforts. Demographic Overlays: Enhance web browsing data with demographic information for more precise audience segmentation and targeting.

By leveraging these complementary datasets, businesses can unlock deeper insights and drive more impactful strategies across their digital initiatives. Data Quality and Scale We pride ourselves on delivering high-quality, reliable data at scale:

Rigorous Data Cleaning: Advanced algorithms filter out bot traffic, VPNs, and other non-human interactions. Regular Quality Checks: Our data science team conducts ongoing audits to ensure data accuracy and consistency. Scalable Infrastructure: Our robust data processing pipeline can handle billions of daily events, ensuring comprehensive coverage. Historical Data Availability: Access up to 24 months of historical data for trend analysis and longitudinal studies. Customizable Data Feeds: Tailor the data delivery to your specific needs, from raw clickstream events to aggregated insights.

Empowering Data-Driven Decision Making In today's digital-first world, understanding online user behavior is crucial for businesses across all sectors. TagX Web Browsing clickstream Data empowers organizations to make informed decisions, optimize their digital strategies, and stay ahead of the competition. Whether you're a marketer looking to refine your targeting, a product manager seeking to enhance user experience, or a researcher exploring digital trends, our cli...

This project contains data sets containing counts of (referer, resource) pairs extracted from the request logs of Wikipedia. A referer is an HTTP header field that identifies the address of the webpage that linked to the resource being requested. The data shows how people get to a Wikipedia article and what links they click on. In other words, it gives a weighted network of articles, where each edge weight corresponds to how often people navigate from one page to another. To give an example, consider the figure below, which shows incoming and outgoing traffic to the "London" article on English Wikipedia during January 2015.

https://upload.wikimedia.org/wikipedia/commons/0/02/London_clickstream.png%20=300x100" alt="Alt text">

Official Documentation

Can be found here

We view web forums as virtual living organisms feeding on user's clicks and investigate how they grow at the expense of clickstreams. We find that (the number of page views in a given time period) and (the number of unique visitors in the time period) of the studied forums satisfy the law of the allometric growth, i.e., . We construct clickstream networks and explain the observed temporal dynamics of networks by the interactions between nodes. We describe the transportation of clickstreams using the function , in which is the total amount of clickstreams passing through node and is the amount of the clickstreams dissipated from to the environment. It turns out that , an indicator for the efficiency of network dissipation, not only negatively correlates with , but also sets the bounds for . In particular, when and when . Our findings have practical consequences. For example, can be used as a measure of the “stickiness” of forums, which quantifies the stable ability of forums to remain users “lock-in” on the forum. Meanwhile, the correlation between and provides a method to predict the long-term “stickiness” of forums from the clickstream data in a short time period. Finally, we discuss a random walk model that replicates both of the allometric growth and the dissipation function .

This data release includes two Wikipedia datasets related to the readership of the project as it relates to the early COVID-19 pandemic period. The first dataset is COVID-19 article page views by country, the second dataset is one hop navigation where one of the two pages are COVID-19 related. The data covers roughly the first six months of the pandemic, more specifically from January 1st 2020 to June 30th 2020. For more background on the pandemic in those months, see English Wikipedia's Timeline of the COVID-19 pandemic.Wikipedia articles are considered COVID-19 related according the methodology described here, the list of COVID-19 articles used for the released datasets is available in covid_articles.tsv. For simplicity and transparency, the same list of articles from 20 April 2020 was used for the entire dataset though in practice new COVID-19-relevant articles were constantly being created as the pandemic evolved.Privacy considerationsWhile this data is considered valuable for the insight that it can provide about information-seeking behaviors around the pandemic in its early months across diverse geographies, care must be taken to not inadvertently reveal information about the behavior of individual Wikipedia readers. We put in place a number of filters to release as much data as we can while minimizing the risk to readers.The Wikimedia foundation started to release most viewed articles by country from Jan 2021. At the beginning of the COVID-19 an exemption was made to store reader data about the pandemic with additional privacy protections:- exclude the page views from users engaged in an edit session- exclude reader data from specific countries (with a few exceptions)- the aggregated statistics are based on 50% of reader sessions that involve a pageview to a COVID-19-related article (see covid_pages.tsv). As a control, a 1% random sample of reader sessions that have no pageviews to COVID-19-related articles was kept. In aggregate, we make sure this 1% non-COVID-19 sample and 50% COVID-19 sample represents less than 10% of pageviews for a country for that day. The randomization and filters occurs on a daily cadence with all timestamps in UTC.- exclude power users - i.e. userhashes with greater than 500 pageviews in a day. This doubles as another form of likely bot removal, protects very heavy users of the project, and also in theory would help reduce the chance of a single user heavily skewing the data.- exclude readership from users of the iOS and Android Wikipedia apps. In effect, the view counts in this dataset represent comparable trends rather than the total amount of traffic from a given country. For more background on readership data per country data, and the COVID-19 privacy protections in particular, see this phabricator.To further minimize privacy risks, a k-anonymity threshold of 100 was applied to the aggregated counts. For example, a page needs to be viewed at least 100 times in a given country and week in order to be included in the dataset. In addition, the view counts are floored to a multiple of 100.DatasetsThe datasets published in this release are derived from a reader session dataset generated by the code in this notebook with the filtering described above. The raw reader session data itself will not be publicly available due to privacy considerations. The datasets described below are similar to the pageviews and clickstream data that the Wikimedia foundation publishes already, with the addition of the country specific counts.COVID-19 pageviewsThe file covid_pageviews.tsv contains:- pageview counts for COVID-19 related pages, aggregated by week and country- k-anonymity threshold of 100- example: In the 13th week of 2020 (23 March - 29 March 2020), the page 'Pandémie_de_Covid-19_en_Italie' on French Wikipedia was visited 11700 times from readers in Belgium- as a control bucket, we include pageview counts to all pages aggregated by week and country. Due to privacy considerations during the collection of the data, the control bucket was sampled at ~1% of all view traffic. The view counts for the control title are thus proportional to the total number of pageviews to all pages.The file is ~8 MB and contains ~134000 data points across the 27 weeks, 108 countries, and 168 projects.Covid reader session bigramsThe file covid_session_bigrams.tsv contains:- number of occurrences of visits to pages A -> B, where either A or B is a COVID-19 related article. Note that the bigrams are tuples (from, to) of articles viewed in succession, the underlying mechanism can be clicking on a link in an article, but it may also have been a new search or reading both articles based on links from third source articles. In contrast, the clickstream data is based on referral information only- aggregated by month and country- k-anonymity threshold of 100- example: In March of 2020, there were a 1000 occurences of readers accessing the page es.wikipedia/SARS-CoV-2 followed by es.wikipedia/Orthocoronavirinae from ChileThe file is ~10 MB and contains ~90000 bigrams across the 6 months, 96 countries, and 56 projects.ContactPlease reach out to research-feedback@wikimedia.org for any questions.

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Retail Analytics Market Size 2025-2029

The retail analytics market size is forecast to increase by USD 28.47 billion, at a CAGR of 29.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing volume and complexity of data generated by retail businesses. This data deluge offers valuable insights for retailers, enabling them to optimize operations, enhance customer experience, and make data-driven decisions. However, this trend also presents challenges. One of the most pressing issues is the increasing adoption of Artificial Intelligence (AI) in the retail sector. While AI brings numerous benefits, such as personalized marketing and improved supply chain management, it also raises privacy and security concerns among customers.
Retailers must address these concerns through transparent data handling practices and robust security measures to maintain customer trust and loyalty. Navigating these challenges requires a strategic approach, with a focus on data security, customer privacy, and effective implementation of AI technologies. Companies that successfully harness the power of retail analytics while addressing these challenges will gain a competitive edge in the market.

What will be the Size of the Retail Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by the constant need for businesses to gain insights from their data and adapt to shifting consumer behaviors. Entities such as text analytics, data quality, price optimization, customer journey mapping, mobile analytics, time series analysis, regression analysis, social media analytics, data mining, historical data analysis, and data cleansing are integral components of this dynamic landscape. Text analytics uncovers hidden patterns and trends in unstructured data, while data quality ensures the accuracy and consistency of information. Price optimization leverages historical data to determine optimal pricing strategies, and customer journey mapping provides insights into the customer experience.

Mobile analytics caters to the growing number of mobile shoppers, and time series analysis identifies trends and patterns over time. Regression analysis uncovers relationships between variables, social media analytics monitors brand sentiment, and data mining uncovers hidden patterns and correlations. Historical data analysis informs strategic decision-making, and data cleansing prepares data for analysis. Customer feedback analysis provides valuable insights into customer satisfaction, and association rule mining uncovers relationships between customer behaviors and purchases. Predictive analytics anticipates future trends, real-time analytics delivers insights in real-time, and market basket analysis uncovers relationships between products. Data security safeguards sensitive information, machine learning (ML) and artificial intelligence (AI) enhance data analysis capabilities, and cloud-based analytics offers flexibility and scalability.

Business intelligence (BI) and open-source analytics provide comprehensive data analysis solutions, while inventory management and supply chain optimization streamline operations. Data governance ensures data is used ethically and effectively, and loyalty programs and A/B testing optimize customer engagement and retention. Seasonality analysis accounts for seasonal trends, and trend analysis identifies emerging trends. Data integration connects disparate data sources, and clickstream analysis tracks user behavior on websites. In the ever-changing retail landscape, these entities are seamlessly integrated into retail analytics solutions, enabling businesses to stay competitive and adapt to evolving market dynamics.

How is this Retail Analytics Industry segmented?

The retail analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  In-store operation
  Customer management
  Supply chain management
  Marketing and merchandizing
  Others


Component

  Software
  Services


Deployment

  Cloud-based
  On-premises


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The in-store operation segment is estimated to witness significant growth during the forecast period. In the realm of retail, the in-store operation segment of the market plays a pivotal role in optimizing brick-and-mortar retail operations. This segment encompasses various data analytics applications with

This article presents a student click-stream database comprising of 120542 train images and 80362 test images where each directory contains two sub directories i.e. "Dropouts" and "NonDropouts" as two different classes.The original dataset was provided by KDD Cup Challenge 2015 in which the dataset was provided by chinese MOOC(Massive open online course) platform XuetangX. These samples have been acquired or captured through the clickstream activity/user activity on the platform. We transformed the KDD-Cup 2015 dataset into an image dataset. This transformation will enable the application of novel deep learning and computer vision techniques to develop more sustainable, accurate, and robust predictive models for identifying students at risk of dropping out and will enable MOOC platforms to design highly robust Early Warning Systems. Furthermore, this dataset will be made publicly available to the research community to advance interdisciplinary research at the intersection of education and computer vision.