46 datasets found
  1. Daily website visitors (time series regression)

    • kaggle.com
    Updated Aug 20, 2020
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    Bob Nau (2020). Daily website visitors (time series regression) [Dataset]. https://www.kaggle.com/bobnau/daily-website-visitors/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 20, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Bob Nau
    Description

    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.

  2. f

    Summary of results comparing Google Analytics and SimilarWeb for total...

    • plos.figshare.com
    xls
    Updated Jun 13, 2023
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    Bernard J. Jansen; Soon-gyo Jung; Joni Salminen (2023). Summary of results comparing Google Analytics and SimilarWeb for total visits, unique visitors, bounce rate, and average session duration. [Dataset]. http://doi.org/10.1371/journal.pone.0268212.t006
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 13, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Bernard J. Jansen; Soon-gyo Jung; Joni Salminen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Difference uses Google Analytics as the Baseline. Results based on Paired t-Test for Hypotheses Supported.

  3. Website Visitor Tracking Tool Market Report | Global Forecast From 2025 To...

    • dataintelo.com
    csv, pdf, pptx
    Updated Jan 7, 2025
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    Dataintelo (2025). Website Visitor Tracking Tool Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-website-visitor-tracking-tool-market
    Explore at:
    csv, pptx, pdfAvailable download formats
    Dataset updated
    Jan 7, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Website Visitor Tracking Tool Market Outlook



    The global website visitor tracking tool market size was valued at $1.1 billion in 2023 and is projected to reach $4.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.2%. The growth factor driving this market includes the rising need for businesses to understand and enhance customer engagement, optimize marketing strategies, and increase conversion rates.



    One of the primary growth drivers for the website visitor tracking tool market is the increasing reliance on digital marketing and online sales channels. As businesses across various industries pivot towards online platforms to attract, engage, and convert customers, the need for robust tools to track and analyze website visitors has become paramount. These tools provide invaluable insights into user behavior, preferences, and engagement patterns, enabling companies to tailor their marketing efforts more effectively and drive higher conversion rates. Additionally, advancements in data analytics and artificial intelligence are further enhancing the capabilities of these tools, making them more precise and insightful.



    Another significant factor contributing to market growth is the rising adoption of customer-centric business strategies. In todayÂ’s competitive business environment, understanding customer needs and preferences is crucial for gaining a competitive edge. Website visitor tracking tools enable businesses to gather comprehensive data about their visitors, such as their browsing history, time spent on different pages, and interaction with various elements on the site. This data can be used to create personalized experiences, improve customer retention, and drive customer loyalty. Furthermore, the integration of these tools with customer relationship management (CRM) systems and other business applications is making it easier for companies to leverage visitor data for better decision-making.



    The growing emphasis on data-driven marketing is also playing a significant role in the expansion of the website visitor tracking tool market. Businesses are increasingly recognizing the importance of data in understanding customer behavior and optimizing marketing strategies. Website visitor tracking tools provide detailed analytics and reporting features that help marketers assess the performance of their campaigns, identify areas for improvement, and measure return on investment (ROI). Additionally, these tools facilitate A/B testing and other optimization techniques, enabling marketers to fine-tune their strategies and achieve better outcomes.



    Affiliate Tracking Software plays a crucial role in the digital marketing ecosystem, particularly for businesses looking to expand their reach through affiliate partnerships. This software enables companies to track the performance of their affiliate marketing campaigns by monitoring clicks, conversions, and sales generated by affiliate links. By providing detailed insights into which affiliates are driving the most traffic and revenue, businesses can optimize their affiliate programs for better results. Additionally, Affiliate Tracking Software helps in managing payouts and commissions, ensuring transparency and efficiency in affiliate relationships. As the demand for performance-based marketing continues to rise, the adoption of robust affiliate tracking solutions is becoming increasingly important for businesses aiming to maximize their marketing ROI.



    Regionally, North America holds a significant share of the website visitor tracking tool market, driven by the high adoption of digital marketing technologies and the presence of numerous leading market players. The region's advanced technological infrastructure and the growing emphasis on data-driven decision-making are further propelling market growth. Europe and Asia Pacific are also witnessing substantial growth, supported by the increasing digital transformation initiatives and the rising number of online businesses in these regions. The Middle East & Africa and Latin America markets are expected to grow at a steady pace, driven by the gradual adoption of digital marketing tools and the growing awareness about the benefits of website visitor tracking.



    Component Analysis



    The website visitor tracking tool market can be segmented by component into software and services. The software segment holds the largest share of the market, driven by the high demand for advanced tracking and analytics solutions. These software solut

  4. Total global visitor traffic to Instagram.com 2024

    • statista.com
    • ai-chatbox.pro
    Updated Nov 11, 2024
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    Statista (2024). Total global visitor traffic to Instagram.com 2024 [Dataset]. https://www.statista.com/statistics/1258821/instagram-monthly-visitors/
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    Dataset updated
    Nov 11, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Oct 2023 - Mar 2024
    Area covered
    Worldwide
    Description

    Instagram is a web traffic powerhouse: in March 2024 seven billion visits were measured to the online photo and video sharing platform, making it one of the most-visited websites online. Instagram is one of the most popular social networks worldwide.

  5. Data from: Analysis of the Quantitative Impact of Social Networks General...

    • figshare.com
    • produccioncientifica.ucm.es
    doc
    Updated Oct 14, 2022
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    David Parra; Santiago Martínez Arias; Sergio Mena Muñoz (2022). Analysis of the Quantitative Impact of Social Networks General Data.doc [Dataset]. http://doi.org/10.6084/m9.figshare.21329421.v1
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    docAvailable download formats
    Dataset updated
    Oct 14, 2022
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    David Parra; Santiago Martínez Arias; Sergio Mena Muñoz
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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

  6. f

    Comparison of definitions of total visits, unique visitors, bounce rate, and...

    • plos.figshare.com
    xls
    Updated Jun 13, 2023
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    Bernard J. Jansen; Soon-gyo Jung; Joni Salminen (2023). Comparison of definitions of total visits, unique visitors, bounce rate, and session duration conceptually and for the two analytics platforms: Google Analytics and SimilarWeb. [Dataset]. http://doi.org/10.1371/journal.pone.0268212.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 13, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Bernard J. Jansen; Soon-gyo Jung; Joni Salminen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Comparison of definitions of total visits, unique visitors, bounce rate, and session duration conceptually and for the two analytics platforms: Google Analytics and SimilarWeb.

  7. Web Analytics Market Analysis, Size, and Forecast 2025-2029: North America...

    • technavio.com
    Updated Apr 15, 2025
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    Technavio (2025). Web Analytics Market Analysis, Size, and Forecast 2025-2029: North America (US and Canada), Europe (France, Germany, Italy, and UK), APAC (China, India, Japan, and South Korea), and Rest of World (ROW) [Dataset]. https://www.technavio.com/report/web-analytics-market-industry-analysis
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    Dataset updated
    Apr 15, 2025
    Dataset provided by
    TechNavio
    Authors
    Technavio
    Time period covered
    2021 - 2025
    Area covered
    Global, United States
    Description

    Snapshot img

    Web Analytics Market Size 2025-2029

    The web analytics market size is forecast to increase by USD 3.63 billion, at a CAGR of 15.4% between 2024 and 2029.

    The market is experiencing significant growth, driven by the rising preference for online shopping and the increasing adoption of cloud-based solutions. The shift towards e-commerce is fueling the demand for advanced web analytics tools that enable businesses to gain insights into customer behavior and optimize their digital strategies. Furthermore, cloud deployment models offer flexibility, scalability, and cost savings, making them an attractive option for businesses of all sizes. However, the market also faces challenges associated with compliance to data privacy and regulations. With the increasing amount of data being generated and collected, ensuring data security and privacy is becoming a major concern for businesses.
    Regulatory compliance, such as GDPR and CCPA, adds complexity to the implementation and management of web analytics solutions. Companies must navigate these challenges effectively to maintain customer trust and avoid potential legal issues. To capitalize on market opportunities and address these challenges, businesses should invest in robust web analytics solutions that prioritize data security and privacy while providing actionable insights to inform strategic decision-making and enhance customer experiences.
    

    What will be the Size of the Web 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.
    Request Free Sample

    The market continues to evolve, with dynamic market activities unfolding across various sectors. Entities such as reporting dashboards, schema markup, conversion optimization, session duration, organic traffic, attribution modeling, conversion rate optimization, call to action, content calendar, SEO audits, website performance optimization, link building, page load speed, user behavior tracking, and more, play integral roles in this ever-changing landscape. Data visualization tools like Google Analytics and Adobe Analytics provide valuable insights into user engagement metrics, helping businesses optimize their content strategy, website design, and technical SEO. Goal tracking and keyword research enable marketers to measure the return on investment of their efforts and refine their content marketing and social media marketing strategies.

    Mobile optimization, form optimization, and landing page optimization are crucial aspects of website performance optimization, ensuring a seamless user experience across devices and improving customer acquisition cost. Search console and page speed insights offer valuable insights into website traffic analysis and help businesses address technical issues that may impact user behavior. Continuous optimization efforts, such as multivariate testing, data segmentation, and data filtering, allow businesses to fine-tune their customer journey mapping and cohort analysis. Search engine optimization, both on-page and off-page, remains a critical component of digital marketing, with backlink analysis and page authority playing key roles in improving domain authority and organic traffic.

    The ongoing integration of user behavior tracking, click-through rate, and bounce rate into marketing strategies enables businesses to gain a deeper understanding of their audience and optimize their customer experience accordingly. As market dynamics continue to evolve, the integration of these tools and techniques into comprehensive digital marketing strategies will remain essential for businesses looking to stay competitive in the digital landscape.

    How is this Web Analytics Industry segmented?

    The web 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.

    Deployment
    
      Cloud-based
      On-premises
    
    
    Application
    
      Social media management
      Targeting and behavioral analysis
      Display advertising optimization
      Multichannel campaign analysis
      Online marketing
    
    
    Component
    
      Solutions
      Services
    
    
    Geography
    
      North America
    
        US
        Canada
    
    
      Europe
    
        France
        Germany
        Italy
        UK
    
    
      APAC
    
        China
        India
        Japan
        South Korea
    
    
      Rest of World (ROW)
    

    .

    By Deployment Insights

    The cloud-based segment is estimated to witness significant growth during the forecast period.

    In today's digital landscape, web analytics plays a pivotal role in driving business growth and optimizing online performance. Cloud-based deployment of web analytics is a game-changer, enabling on-demand access to computing resources for data analysis. This model streamlines business intelligence processes by collecting,

  8. f

    Host country of organization for 86 websites in study.

    • plos.figshare.com
    xls
    Updated Jun 15, 2023
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    Bernard J. Jansen; Soon-gyo Jung; Joni Salminen (2023). Host country of organization for 86 websites in study. [Dataset]. http://doi.org/10.1371/journal.pone.0268212.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 15, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Bernard J. Jansen; Soon-gyo Jung; Joni Salminen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Host country of organization for 86 websites in study.

  9. d

    NYC.gov Web Analytics

    • catalog.data.gov
    • data.cityofnewyork.us
    • +4more
    Updated Sep 30, 2022
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    data.cityofnewyork.us (2022). NYC.gov Web Analytics [Dataset]. https://catalog.data.gov/dataset/nyc-gov-web-analytics
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    Dataset updated
    Sep 30, 2022
    Dataset provided by
    data.cityofnewyork.us
    Area covered
    New York
    Description

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

  10. Web clicks from Indiana University

    • zenodo.org
    • data.niaid.nih.gov
    application/gzip
    Updated Jan 24, 2020
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    Dimitar Nikolov; Dimitar Nikolov; Filippo Menczer; Filippo Menczer (2020). Web clicks from Indiana University [Dataset]. http://doi.org/10.5281/zenodo.2650234
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    application/gzipAvailable download formats
    Dataset updated
    Jan 24, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Dimitar Nikolov; Dimitar Nikolov; Filippo Menczer; Filippo Menczer
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    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.

  11. P

    Traffic Dataset

    • paperswithcode.com
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    Traffic Dataset [Dataset]. https://paperswithcode.com/dataset/traffic
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    Description

    Abstract: The task for this dataset is to forecast the spatio-temporal traffic volume based on the historical traffic volume and other features in neighboring locations.

    Data Set CharacteristicsNumber of InstancesAreaAttribute CharacteristicsNumber of AttributesDate DonatedAssociated TasksMissing Values
    Multivariate2101ComputerReal472020-11-17RegressionN/A

    Source: Liang Zhao, liang.zhao '@' emory.edu, Emory University.

    Data Set Information: The task for this dataset is to forecast the spatio-temporal traffic volume based on the historical traffic volume and other features in neighboring locations. Specifically, the traffic volume is measured every 15 minutes at 36 sensor locations along two major highways in Northern Virginia/Washington D.C. capital region. The 47 features include: 1) the historical sequence of traffic volume sensed during the 10 most recent sample points (10 features), 2) week day (7 features), 3) hour of day (24 features), 4) road direction (4 features), 5) number of lanes (1 feature), and 6) name of the road (1 feature). The goal is to predict the traffic volume 15 minutes into the future for all sensor locations. With a given road network, we know the spatial connectivity between sensor locations. For the detailed data information, please refer to the file README.docx.

    Attribute Information: The 47 features include: (1) the historical sequence of traffic volume sensed during the 10 most recent sample points (10 features), (2) week day (7 features), (3) hour of day (24 features), (4) road direction (4 features), (5) number of lanes (1 feature), and (6) name of the road (1 feature).

    Relevant Papers: Liang Zhao, Olga Gkountouna, and Dieter Pfoser. 2019. Spatial Auto-regressive Dependency Interpretable Learning Based on Spatial Topological Constraints. ACM Trans. Spatial Algorithms Syst. 5, 3, Article 19 (August 2019), 28 pages. DOI:[Web Link]

    Citation Request: To use these datasets, please cite the papers:

    Liang Zhao, Olga Gkountouna, and Dieter Pfoser. 2019. Spatial Auto-regressive Dependency Interpretable Learning Based on Spatial Topological Constraints. ACM Trans. Spatial Algorithms Syst. 5, 3, Article 19 (August 2019), 28 pages. DOI:[Web Link]

  12. f

    Website type for the 86 websites in study.

    • figshare.com
    xls
    Updated Jun 13, 2023
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    Bernard J. Jansen; Soon-gyo Jung; Joni Salminen (2023). Website type for the 86 websites in study. [Dataset]. http://doi.org/10.1371/journal.pone.0268212.t005
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 13, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Bernard J. Jansen; Soon-gyo Jung; Joni Salminen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Website type for the 86 websites in study.

  13. Enterprise Website Analytics Software Market Report | Global Forecast From...

    • dataintelo.com
    csv, pdf, pptx
    Updated Jan 7, 2025
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    Dataintelo (2025). Enterprise Website Analytics Software Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-enterprise-website-analytics-software-market
    Explore at:
    csv, pdf, pptxAvailable download formats
    Dataset updated
    Jan 7, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Enterprise Website Analytics Software Market Outlook



    The global Enterprise Website Analytics Software market size was valued at approximately USD 3.45 billion in 2023 and is projected to reach about USD 10.48 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.1% during the forecast period. This robust growth can be attributed to the increasing importance of online presence for businesses, the rising adoption of data-driven decision-making processes, and the ever-growing demand for customer insights and behavioral analysis.



    One of the major growth factors for the Enterprise Website Analytics Software market is the increasing emphasis on digital marketing strategies across various industries. As businesses strive to enhance their online presence and digital customer engagement, the need for sophisticated analytics tools to measure website performance becomes imperative. The ability to track and analyze user interactions, conversion rates, and other metrics helps businesses to optimize their websites, enhance user experience, and ultimately drive sales growth.



    Another key driver is the rapid technological advancements in the field of big data and artificial intelligence (AI). These technologies enable the processing of vast amounts of data generated from website visits, providing deeper and more accurate insights into customer behavior. With AI and machine learning algorithms, analytics software can now predict customer preferences and trends, allowing businesses to tailor their strategies more effectively. Additionally, the integration of AI in analytics processes helps in automating complex data analysis, making it accessible even for non-technical users.



    The shift towards e-commerce and online retail has also significantly boosted the demand for enterprise website analytics software. With more consumers shopping online, businesses need advanced tools to understand their customers' online behavior better. Analytics software helps e-commerce companies to monitor site traffic, identify potential issues, and improve user experience, thereby increasing customer satisfaction and loyalty. Furthermore, the COVID-19 pandemic has accelerated digital transformation across various sectors, leading to a surge in demand for analytics solutions.



    The emergence of Advanced Analytics Service Software is revolutionizing the way businesses approach data analysis. By leveraging sophisticated algorithms and machine learning models, these software solutions offer unparalleled insights into complex data sets. This enables organizations to make more informed decisions, optimize their operations, and enhance customer experiences. As businesses increasingly recognize the value of data-driven strategies, the demand for advanced analytics services is expected to grow significantly. These tools not only facilitate real-time data processing but also provide predictive analytics capabilities, allowing companies to anticipate market trends and customer needs with greater accuracy.



    Regionally, North America holds a significant share of the market, driven by the presence of a large number of tech-savvy businesses and high adoption rates of advanced technologies. Europe follows closely, with an increasing number of businesses recognizing the importance of data analytics in staying competitive. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to rapid digitalization and growing internet penetration in countries like China and India.



    Component Analysis



    In the Enterprise Website Analytics Software market, the component segment is bifurcated into Software and Services. The software segment dominates the market due to the frequent demand for advanced analytics tools that offer features such as real-time data tracking, customer journey mapping, and conversion rate optimization. These software solutions are continually evolving to include more sophisticated functionalities, making them indispensable for businesses aiming to leverage data-driven insights.



    The services segment, while smaller in market share compared to software, plays a crucial role in the overall ecosystem. Services include consulting, implementation, training, and support that complement the software solutions. Many enterprises, particularly those lacking in-house technical expertise, rely on these services to effectively deploy and utilize analytics software. The demand for managed ser

  14. f

    Comparison of user, site, and network-centric approaches to web analytics...

    • plos.figshare.com
    xls
    Updated May 31, 2023
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    Bernard J. Jansen; Soon-gyo Jung; Joni Salminen (2023). 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. [Dataset]. http://doi.org/10.1371/journal.pone.0268212.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Bernard J. Jansen; Soon-gyo Jung; Joni Salminen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  15. Data from: Google Analytics & Twitter dataset from a movies, TV series and...

    • figshare.com
    • portalcientificovalencia.univeuropea.com
    txt
    Updated Feb 7, 2024
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    Víctor Yeste (2024). Google Analytics & Twitter dataset from a movies, TV series and videogames website [Dataset]. http://doi.org/10.6084/m9.figshare.16553061.v4
    Explore at:
    txtAvailable download formats
    Dataset updated
    Feb 7, 2024
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Víctor Yeste
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Author: Víctor Yeste. Universitat Politècnica de Valencia.The object of this study is the design of a cybermetric methodology whose objectives are to measure the success of the content published in online media and the possible prediction of the selected success variables.In this case, due to the need to integrate data from two separate areas, such as web publishing and the analysis of their shares and related topics on Twitter, has opted for programming as you access both the Google Analytics v4 reporting API and Twitter Standard API, always respecting the limits of these.The website analyzed is hellofriki.com. It is an online media whose primary intention is to solve the need for information on some topics that provide daily a vast number of news in the form of news, as well as the possibility of analysis, reports, interviews, and many other information formats. All these contents are under the scope of the sections of cinema, series, video games, literature, and comics.This dataset has contributed to the elaboration of the PhD Thesis:Yeste Moreno, VM. (2021). Diseño de una metodología cibermétrica de cálculo del éxito para la optimización de contenidos web [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/176009Data have been obtained from each last-minute news article published online according to the indicators described in the doctoral thesis. All related data are stored in a database, divided into the following tables:tesis_followers: User ID list of media account followers.tesis_hometimeline: data from tweets posted by the media account sharing breaking news from the web.status_id: Tweet IDcreated_at: date of publicationtext: content of the tweetpath: URL extracted after processing the shortened URL in textpost_shared: Article ID in WordPress that is being sharedretweet_count: number of retweetsfavorite_count: number of favoritestesis_hometimeline_other: data from tweets posted by the media account that do not share breaking news from the web. Other typologies, automatic Facebook shares, custom tweets without link to an article, etc. With the same fields as tesis_hometimeline.tesis_posts: data of articles published by the web and processed for some analysis.stats_id: Analysis IDpost_id: Article ID in WordPresspost_date: article publication date in WordPresspost_title: title of the articlepath: URL of the article in the middle webtags: Tags ID or WordPress tags related to the articleuniquepageviews: unique page viewsentrancerate: input ratioavgtimeonpage: average visit timeexitrate: output ratiopageviewspersession: page views per sessionadsense_adunitsviewed: number of ads viewed by usersadsense_viewableimpressionpercent: ad display ratioadsense_ctr: ad click ratioadsense_ecpm: estimated ad revenue per 1000 page viewstesis_stats: data from a particular analysis, performed at each published breaking news item. Fields with statistical values can be computed from the data in the other tables, but total and average calculations are saved for faster and easier further processing.id: ID of the analysisphase: phase of the thesis in which analysis has been carried out (right now all are 1)time: "0" if at the time of publication, "1" if 14 days laterstart_date: date and time of measurement on the day of publicationend_date: date and time when the measurement is made 14 days latermain_post_id: ID of the published article to be analysedmain_post_theme: Main section of the published article to analyzesuperheroes_theme: "1" if about superheroes, "0" if nottrailer_theme: "1" if trailer, "0" if notname: empty field, possibility to add a custom name manuallynotes: empty field, possibility to add personalized notes manually, as if some tag has been removed manually for being considered too generic, despite the fact that the editor put itnum_articles: number of articles analysednum_articles_with_traffic: number of articles analysed with traffic (which will be taken into account for traffic analysis)num_articles_with_tw_data: number of articles with data from when they were shared on the media’s Twitter accountnum_terms: number of terms analyzeduniquepageviews_total: total page viewsuniquepageviews_mean: average page viewsentrancerate_mean: average input ratioavgtimeonpage_mean: average duration of visitsexitrate_mean: average output ratiopageviewspersession_mean: average page views per sessiontotal: total of ads viewedadsense_adunitsviewed_mean: average of ads viewedadsense_viewableimpressionpercent_mean: average ad display ratioadsense_ctr_mean: average ad click ratioadsense_ecpm_mean: estimated ad revenue per 1000 page viewsTotal: total incomeretweet_count_mean: average incomefavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesterms_ini_num_tweets: total tweets on the terms on the day of publicationterms_ini_retweet_count_total: total retweets on the terms on the day of publicationterms_ini_retweet_count_mean: average retweets on the terms on the day of publicationterms_ini_favorite_count_total: total of favorites on the terms on the day of publicationterms_ini_favorite_count_mean: average of favorites on the terms on the day of publicationterms_ini_followers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the terms on the day of publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms on the day of publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who spoke about the terms on the day of publicationterms_ini_user_age_mean: average age in days of users who have spoken of the terms on the day of publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms on the day of publicationterms_end_num_tweets: total tweets on terms 14 days after publicationterms_ini_retweet_count_total: total retweets on terms 14 days after publicationterms_ini_retweet_count_mean: average retweets on terms 14 days after publicationterms_ini_favorite_count_total: total bookmarks on terms 14 days after publicationterms_ini_favorite_count_mean: average of favorites on terms 14 days after publicationterms_ini_followers_talking_rate: ratio of media Twitter account followers who have recently posted a tweet talking about the terms 14 days after publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms 14 days after publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who have spoken about the terms 14 days after publicationterms_ini_user_age_mean: the average age in days of users who have spoken of the terms 14 days after publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms 14 days after publication.tesis_terms: data of the terms (tags) related to the processed articles.stats_id: Analysis IDtime: "0" if at the time of publication, "1" if 14 days laterterm_id: Term ID (tag) in WordPressname: Name of the termslug: URL of the termnum_tweets: number of tweetsretweet_count_total: total retweetsretweet_count_mean: average retweetsfavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesfollowers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the termuser_num_followers_mean: average followers of users who were talking about the termuser_num_tweets_mean: average number of tweets published by users who were talking about the termuser_age_mean: average age in days of users who were talking about the termurl_inclusion_rate: URL inclusion ratio

  16. A

    ‘Strategic Measure _Open Data Asset Access Frequency’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Apr 9, 2020
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2020). ‘Strategic Measure _Open Data Asset Access Frequency’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-strategic-measure-open-data-asset-access-frequency-ee5d/6ac5900a/?iid=002-101&v=presentation
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    Dataset updated
    Apr 9, 2020
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Strategic Measure _Open Data Asset Access Frequency’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/5a60a305-880e-4cbd-b387-ee9f191ea659 on 26 January 2022.

    --- Dataset description provided by original source is as follows ---

    This dataset represents the total number of Open Data Portal assets and the frequency of how often the asset is accessed. This data is collected by using Socrata Analytics. This dataset supports measure GTW.G.4 of SD23.

    Data Source: Socrata.

    Calculations: (GTW.G.4) Percentage of datasets published in the Open Data portal that are being accessed frequently (such as through a website views, API interactions, embeds or mobile views).

    Measure Time Period: Fiscal Year Annually Automated: No Date of Last description update: 4/1/2020

    For questions please contact CTMCollaborationServices@austintexas.gov

    --- Original source retains full ownership of the source dataset ---

  17. Total global visitor traffic to Pinterest.com 2024

    • statista.com
    Updated Nov 11, 2024
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    Statista (2024). Total global visitor traffic to Pinterest.com 2024 [Dataset]. https://www.statista.com/statistics/277694/number-of-unique-us-visitors-to-pinterestcom/
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    Dataset updated
    Nov 11, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Oct 2023 - Mar 2024
    Area covered
    Worldwide
    Description

    Pinterest is a web traffic powerhouse: in March 2024 approximately 1.3 billion visits were measured to the Pinterest.com, making it one of the most-visited websites online. In the third quarter of 2023, Pinterest had 482 million monthly active users worldwide.

  18. Industry vertical of organization for 86 websites in study.

    • plos.figshare.com
    xls
    Updated Jun 15, 2023
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    Bernard J. Jansen; Soon-gyo Jung; Joni Salminen (2023). Industry vertical of organization for 86 websites in study. [Dataset]. http://doi.org/10.1371/journal.pone.0268212.t004
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    xlsAvailable download formats
    Dataset updated
    Jun 15, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Bernard J. Jansen; Soon-gyo Jung; Joni Salminen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Industry vertical of organization for 86 websites in study.

  19. w

    Global Website Visitor Tracking Software Market Research Report: By...

    • wiseguyreports.com
    Updated Aug 10, 2024
    + more versions
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    wWiseguy Research Consultants Pvt Ltd (2024). Global Website Visitor Tracking Software Market Research Report: By Deployment Type (On-Premise, Cloud), By Usage (Traffic Analysis, Lead Generation, Ad Campaign Optimization, User Experience Measurement, Customer Segmentation), By Industry Vertical (Retail and Ecommerce, Automotive, Healthcare, Media and Entertainment, Education), By Organization Size (Large Enterprises, Small and Medium-sized Businesses) and By Regional (North America, Europe, South America, Asia Pacific, Middle East and Africa) - Forecast to 2032. [Dataset]. https://www.wiseguyreports.com/cn/reports/website-visitor-tracking-software-market
    Explore at:
    Dataset updated
    Aug 10, 2024
    Dataset authored and provided by
    wWiseguy Research Consultants Pvt Ltd
    License

    https://www.wiseguyreports.com/pages/privacy-policyhttps://www.wiseguyreports.com/pages/privacy-policy

    Time period covered
    Jan 8, 2024
    Area covered
    Global
    Description
    BASE YEAR2024
    HISTORICAL DATA2019 - 2024
    REPORT COVERAGERevenue Forecast, Competitive Landscape, Growth Factors, and Trends
    MARKET SIZE 20233.07(USD Billion)
    MARKET SIZE 20243.51(USD Billion)
    MARKET SIZE 203210.2(USD Billion)
    SEGMENTS COVEREDDeployment Type ,Usage ,Industry Vertical ,Organization Size ,Regional
    COUNTRIES COVEREDNorth America, Europe, APAC, South America, MEA
    KEY MARKET DYNAMICSRising demand for personalized customer experiences Growing adoption of digital marketing channels Increasing focus on data privacy and compliance Advancements in artificial intelligence and machine learning Emergence of new technologies such as headless CMS and serverside tagging
    MARKET FORECAST UNITSUSD Billion
    KEY COMPANIES PROFILEDClicky ,Smartlook ,Google Analytics ,Crazy Egg ,Quantum Metric ,PIWIK PRO ,Woopra ,AT Internet ,Inspectlet ,Kissmetrics ,Mouseflow ,Matomo ,Hotjar ,Mixpanel ,SessionStack
    MARKET FORECAST PERIOD2025 - 2032
    KEY MARKET OPPORTUNITIESAIpowered analytics Mobile optimization Integration with CRM systems Predictive analytics Realtime insights
    COMPOUND ANNUAL GROWTH RATE (CAGR) 14.27% (2025 - 2032)
  20. GTT23: A 2023 Dataset of Genuine Tor Traces

    • zenodo.org
    • data.niaid.nih.gov
    Updated Apr 11, 2024
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    Rob Jansen; Rob Jansen; Ryan Wails; Ryan Wails; Aaron Johnson; Aaron Johnson (2024). GTT23: A 2023 Dataset of Genuine Tor Traces [Dataset]. http://doi.org/10.5281/zenodo.10620520
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    Dataset updated
    Apr 11, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Rob Jansen; Rob Jansen; Ryan Wails; Ryan Wails; Aaron Johnson; Aaron Johnson
    Time period covered
    2023
    Description
    The GTT23 dataset contains network metadata of encrypted traffic measured from exit relays in the Tor network over a 13-week measurement period in 2023. The metadata is suitable for analyzing and evaluating website fingerprinting attacks and defenses.
    Our dataset measurement process was designed to prioritize safety and privacy and was developed through consultation with the Tor Research Safety Board (TRSB, submission #37). Our TRSB interaction resulted in a “No Objections” score.
    The measurement process, additional safety and ethical considerations, and a statistical analysis of the dataset will be presented in further detail in a forthcoming publication.
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Bob Nau (2020). Daily website visitors (time series regression) [Dataset]. https://www.kaggle.com/bobnau/daily-website-visitors/code
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Daily website visitors (time series regression)

Predict tomorrow's number of website visitors from 5 years of daily data

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5 scholarly articles cite this dataset (View in Google Scholar)
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Aug 20, 2020
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Bob Nau
Description

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.

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