Code:

Packet_Features_Generator.py & Features.py

To run this code:

pkt_features.py [-h] -i TXTFILE [-x X] [-y Y] [-z Z] [-ml] [-s S] -j

-h, --help show this help message and exit -i TXTFILE input text file -x X Add first X number of total packets as features. -y Y Add first Y number of negative packets as features. -z Z Add first Z number of positive packets as features. -ml Output to text file all websites in the format of websiteNumber1,feature1,feature2,... -s S Generate samples using size s. -j

Purpose:

Turns a text file containing lists of incomeing and outgoing network packet sizes into separate website objects with associative features.

Uses Features.py to calcualte the features.

startMachineLearning.sh & machineLearning.py

To run this code:

bash startMachineLearning.sh

This code then runs machineLearning.py in a tmux session with the nessisary file paths and flags

Options (to be edited within this file):

--evaluate-only to test 5 fold cross validation accuracy

--test-scaling-normalization to test 6 different combinations of scalers and normalizers

Note: once the best combination is determined, it should be added to the data_preprocessing function in machineLearning.py for future use

--grid-search to test the best grid search hyperparameters - note: the possible hyperparameters must be added to train_model under 'if not evaluateOnly:' - once best hyperparameters are determined, add them to train_model under 'if evaluateOnly:'

Purpose:

Using the .ml file generated by Packet_Features_Generator.py & Features.py, this program trains a RandomForest Classifier on the provided data and provides results using cross validation. These results include the best scaling and normailzation options for each data set as well as the best grid search hyperparameters based on the provided ranges.

Data

Encrypted network traffic was collected on an isolated computer visiting different Wikipedia and New York Times articles, different Google search queres (collected in the form of their autocomplete results and their results page), and different actions taken on a Virtual Reality head set.

Data for this experiment was stored and analyzed in the form of a txt file for each experiment which contains:

First number is a classification number to denote what website, query, or vr action is taking place.

The remaining numbers in each line denote:

The size of a packet,

and the direction it is traveling.

negative numbers denote incoming packets

positive numbers denote outgoing packets

Figure 4 Data

This data uses specific lines from the Virtual Reality.txt file.

The action 'LongText Search' refers to a user searching for "Saint Basils Cathedral" with text in the Wander app.

The action 'ShortText Search' refers to a user searching for "Mexico" with text in the Wander app.

The .xlsx and .csv file are identical

Each file includes (from right to left):

The origional packet data,

each line of data organized from smallest to largest packet size in order to calculate the mean and standard deviation of each packet capture,

and the final Cumulative Distrubution Function (CDF) caluclation that generated the Figure 4 Graph.

The website traffic analysis tool market is experiencing robust growth, driven by the increasing reliance of businesses, both large and small, on digital marketing strategies. The demand for data-driven decision-making and performance optimization across various online channels is fueling the adoption of these tools. The market, estimated at $15 billion in 2025, is projected to grow at a compound annual growth rate (CAGR) of 15% through 2033, reaching approximately $45 billion. This growth is fueled by several key trends: the rise of cloud-based solutions offering greater scalability and accessibility, increasing sophistication of analytics capabilities (including AI-powered insights), and a growing need for comprehensive website performance monitoring. While the market exhibits strong growth potential, businesses face challenges including the increasing complexity of website analytics, the need for skilled personnel to interpret data effectively, and the rising costs associated with premium features and advanced analytics platforms. The segmentation reveals a significant presence of both SMEs and large enterprises leveraging the technology, with a clear preference toward cloud-based solutions due to their flexibility and cost-effectiveness. Key players such as Semrush, Ahrefs, Google Analytics, and others are actively shaping the market through continuous innovation and expansion into new markets. The geographical distribution of the market reflects a strong presence in North America and Europe, driven by higher digital maturity and adoption rates within these regions. However, significant growth opportunities exist in Asia Pacific and other emerging markets, as digital infrastructure expands and businesses increasingly prioritize online presence. The competitive landscape is characterized by a mix of established players and emerging startups, leading to continuous innovation and price competition, benefiting end users. This intense competition drives the development of advanced features such as real-time analytics, predictive modeling, and integration with other marketing tools. The ongoing evolution of digital marketing itself is a major driver, requiring the constant refinement and improvement of these analytics tools to keep pace with changes in SEO, social media, and online advertising practices. This creates a dynamic environment conducive to further market expansion.

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

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

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

Use Case: Analyze Year Over Year Growth Rate by Region

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

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

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

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

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

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

Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

The global no-code website builder tools market is projected to reach $43.7 billion by 2033, exhibiting a CAGR of 12.4% during the forecast period. The increasing adoption of digital platforms by businesses and the growing demand for user-friendly website development tools are driving the market growth. The availability of a wide range of templates and drag-and-drop functionality makes no-code website builders accessible to individuals and businesses with limited technical expertise. Key trends shaping the market include the integration of artificial intelligence (AI) and machine learning (ML) to enhance the user experience, the rise of headless CMS platforms that enable greater flexibility and scalability, and the growing popularity of cloud-based no-code website builders that offer convenience and cost-effectiveness. The market is segmented into various types, applications, and regions, with North America holding a significant share due to the presence of leading technology companies and a large number of small and medium-sized businesses. Major players in the market include Wix, Bubble, Webflow, Squarespace, and WordPress, among others, who are focusing on expanding their offerings, forming strategic partnerships, and investing in research and development to gain a competitive edge. The no-code website builder tools market is experiencing exponential growth, with its value projected to reach over $17.6 billion by 2026. These tools empower non-technical individuals and businesses to create professional-looking websites without the need for programming knowledge.

The growth of the Internet since its inception has fueled strong demand and profitability for web design services, as both businesses and households increasingly conduct activities online. The pandemic accelerated this trend, forcing businesses to upgrade their digital presence amid lockdowns and remote work, which resulted in significant revenue gains for web designers in 2020. This trend continued in 2021 as the strong economic recovery boosted corporate profit and gave businesses greater funds to invest in the industry’s services. More recently, high inflation and rising interest rates have raised costs and curtailed demand, with some businesses opting for cheaper alternatives like templates rather than custom web design, contributing to a drop in revenue in 2022. Despite these challenges, rising stock prices linked to AI advancements pushed business income substantially upward, enabling further investment in web design through 2023 and 2024 and benefiting revenue. However, high inflation and rising interest rates have recently raised costs and curtailed demand, with some businesses opting for cheaper alternatives like templates rather than custom web design. In response to shifting client expectations, web designers now prioritize mobile-first design, rapid performance, personalization and interactive content. These adaptations, along with investments in new technologies, have allowed web designers—especially smaller ones—to differentiate themselves and sustain long-term growth. Overall, revenue for web design services companies has swelled at a CAGR of 2.3% over the past five years, reaching $47.4 billion in 2025. This includes a 1.5% rise in revenue in that year. Market saturation will limit revenue growth for website designers moving forward. With nearly all US adults now using the Internet, opportunities for finding new customers are dwindling as internet usage approaches universality. As a result, major providers may turn to mergers and acquisitions to maintain market share, while smaller companies will likely focus on niche markets or specific geographies to secure stable income. Additionally, tariffs imposed by the Trump administration could further restrain demand by increasing consumer prices, reducing disposable income and pushing the economy toward recession. In response, web designers may expand geographically to find new clients. Amid these headwinds, AI and automation technologies are transforming design workflows, increasing efficiency while fostering a greater need for skilled workers and enabling more tailored services. Companies are also adapting by prioritizing inclusivity and sustainability, attracting broader demographics and eco-conscious clients. Overall, revenue for web design services providers is forecast to inch upward at a CAGR of 1.1% over the next five years, reaching $49.9 billion in 2030.

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

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

The global Web Analytics Tools market is burgeoning, with a staggering market size of XXX million in 2025 and a CAGR of XX% from 2025 to 2033. The market is driven by the increasing demand for data-driven decision-making, the proliferation of digital marketing channels, and the growing adoption of cloud-based solutions. Additionally, the rising awareness of privacy concerns is prompting organizations to seek compliant web analytics tools. The market is segmented based on application (personal, enterprise, other), type (basic, standard, senior), and region (North America, South America, Europe, Middle East & Africa, Asia Pacific). Major market players include Netcore Solution, Leadtosale, ClickCease, AgencyAnalytics, Agile CRM, and Smartlook, among others. The North American region holds a significant market share due to the presence of a large number of established companies and the high adoption of web analytics tools in the region.

Analysis of ‘Phishing website Detector’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/eswarchandt/phishing-website-detector on 12 November 2021.

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

Description

The data set is provided both in text file and csv file which provides the following resources that can be used as inputs for model building :

1. A collection of website URLs for 11000+ websites. Each sample has 30 website parameters and a class label identifying it as a phishing website or not (1 or -1).

2. The code template containing these code blocks: a. Import modules (Part 1) b. Load data function + input/output field descriptions

The data set also serves as an input for project scoping and tries to specify the functional and non-functional requirements for it.

Background of Problem Statement :

You are expected to write the code for a binary classification model (phishing website or not) using Python Scikit-Learn that trains on the data and calculates the accuracy score on the test data. You have to use one or more of the classification algorithms to train a model on the phishing website data set.

Dataset Description:

1. The dataset for a “.txt” file is with no headers and has only the column values.
2. The actual column-wise header is described above and, if needed, you can add the header manually if you are using '.txt' file.If you are using '.csv' file then the column names were added and given.
3. The header list (column names) is as follows : [ 'UsingIP', 'LongURL', 'ShortURL', 'Symbol@', 'Redirecting//', 'PrefixSuffix-', 'SubDomains', 'HTTPS', 'DomainRegLen', 'Favicon', 'NonStdPort', 'HTTPSDomainURL', 'RequestURL', 'AnchorURL', 'LinksInScriptTags', 'ServerFormHandler', 'InfoEmail', 'AbnormalURL', 'WebsiteForwarding', 'StatusBarCust', 'DisableRightClick', 'UsingPopupWindow', 'IframeRedirection', 'AgeofDomain', 'DNSRecording', 'WebsiteTraffic', 'PageRank', 'GoogleIndex', 'LinksPointingToPage', 'StatsReport', 'class' ] ### Brief Description of the features in data set ● UsingIP (categorical - signed numeric) : { -1,1 } ● LongURL (categorical - signed numeric) : { 1,0,-1 } ● ShortURL (categorical - signed numeric) : { 1,-1 } ● Symbol@ (categorical - signed numeric) : { 1,-1 } ● Redirecting// (categorical - signed numeric) : { -1,1 } ● PrefixSuffix- (categorical - signed numeric) : { -1,1 } ● SubDomains (categorical - signed numeric) : { -1,0,1 } ● HTTPS (categorical - signed numeric) : { -1,1,0 } ● DomainRegLen (categorical - signed numeric) : { -1,1 } ● Favicon (categorical - signed numeric) : { 1,-1 } ● NonStdPort (categorical - signed numeric) : { 1,-1 } ● HTTPSDomainURL (categorical - signed numeric) : { -1,1 } ● RequestURL (categorical - signed numeric) : { 1,-1 } ● AnchorURL (categorical - signed numeric) :

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

Here are a few use cases for this project:

1. Web Accessibility Improvement: The "Web Page Object Detection" model can be used to identify and label various elements on a web page, making it easier for people with visual impairments to navigate and interact with websites using screen readers and other assistive technologies.

2. Web Design Analysis: The model can be employed to analyze the structure and layout of popular websites, helping web designers understand best practices and trends in web design. This information can inform the creation of new, user-friendly websites or redesigns of existing pages.

3. Automatic Web Page Summary Generation: By identifying and extracting key elements, such as titles, headings, content blocks, and lists, the model can assist in generating concise summaries of web pages, which can aid users in their search for relevant information.

4. Web Page Conversion and Optimization: The model can be used to detect redundant or unnecessary elements on a web page and suggest their removal or modification, leading to cleaner designs and faster-loading pages. This can improve user experience and, potentially, search engine rankings.

5. Assisting Web Developers in Debugging and Testing: By detecting web page elements, the model can help identify inconsistencies or errors in a site's code or design, such as missing or misaligned elements, allowing developers to quickly diagnose and address these issues.

Raster data providing site suitability results for the production of castor throughout Brazil. The pixel value range from 1 (currently not suitable) to 10 (highly suitable) for a suitability ranking in the given pixel location. The site suitability for castor was conducted using data associated with agronomic and disease characteristics. The various characteristics were subject to a weighted overlay analysis in conjunction with an analytical hierarchy process. The raster was the result of these analytics.

The data represent web-scraping of hyperlinks from a selection of environmental stewardship organizations that were identified in the 2017 NYC Stewardship Mapping and Assessment Project (STEW-MAP) (USDA 2017). There are two data sets: 1) the original scrape containing all hyperlinks within the websites and associated attribute values (see "README" file); 2) a cleaned and reduced dataset formatted for network analysis. For dataset 1: Organizations were selected from from the 2017 NYC Stewardship Mapping and Assessment Project (STEW-MAP) (USDA 2017), a publicly available, spatial data set about environmental stewardship organizations working in New York City, USA (N = 719). To create a smaller and more manageable sample to analyze, all organizations that intersected (i.e., worked entirely within or overlapped) the NYC borough of Staten Island were selected for a geographically bounded sample. Only organizations with working websites and that the web scraper could access were retained for the study (n = 78). The websites were scraped between 09 and 17 June 2020 to a maximum search depth of ten using the snaWeb package (version 1.0.1, Stockton 2020) in the R computational language environment (R Core Team 2020). For dataset 2: The complete scrape results were cleaned, reduced, and formatted as a standard edge-array (node1, node2, edge attribute) for network analysis. See "READ ME" file for further details. References: R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://res1wwwd-o-tR-projectd-o-torg.vcapture.xyz/. Version 4.0.3. Stockton, T. (2020). snaWeb Package: An R package for finding and building social networks for a website, version 1.0.1. USDA Forest Service. (2017). Stewardship Mapping and Assessment Project (STEW-MAP). New York City Data Set. Available online at https://res1wwwd-o-tnrsd-o-tfsd-o-tfedd-o-tus.vcapture.xyz/STEW-MAP/data/. This dataset is associated with the following publication: Sayles, J., R. Furey, and M. Ten Brink. How deep to dig: effects of web-scraping search depth on hyperlink network analysis of environmental stewardship organizations. Applied Network Science. Springer Nature, New York, NY, 7: 36, (2022).

Market Size and Growth: The website visitor tracking software market is projected to reach USD XX million by 2033, expanding at a CAGR of XX% from 2025 to 2033. The market is driven by the increasing adoption of digital marketing and analytics, as businesses seek to understand their website visitors' behavior and optimize their marketing campaigns. The growing demand for data privacy and compliance regulations is also fueling market growth. Industry Trends and Dynamics: The website visitor tracking software market is experiencing several trends, including the rise of cloud-based solutions, the integration of artificial intelligence (AI) and machine learning (ML) for enhanced data analysis, and the increased focus on personalization and customer segmentation. Key players in the market include Visitor Queue, Crazy Egg, VWO Insights, Leadfeeder, and Google Analytics, among others. The competitive landscape is characterized by strategic partnerships, acquisitions, and product innovations. Regional markets are also witnessing significant growth, particularly in North America, Europe, and Asia Pacific, as businesses across these regions embrace digital transformation and customer-centric strategies.

Market Size and Growth: The global website testing tool market was valued at approximately USD XXX million in 2025 and is projected to reach USD XXX million by 2033, exhibiting a CAGR of XX%. The market is driven by increasing demand for digital transformation, rising adoption of software-as-a-service (SaaS) and cloud-based solutions, and the growing need for ensuring website quality and functionality. Key factors contributing to growth include the rise of e-commerce, the proliferation of mobile devices, and the increasing use of artificial intelligence (AI) and machine learning (ML) for website optimization. Segmentation and Competitive Landscape: The website testing tool market is segmented by application (SMEs, Large Enterprises), type (Cloud-based, On-premises), and region. Key players in the market include testRigor, Testpad, TestComplete, QA Wolf, Katalon Platform, LambdaTest, Intruder, Selenium, HP UFT, Tricentis, SOAP UI, BugZilla, JIRA, Ghostlab, Sauce Labs, Browser-Stack, LoadNinja, Apache, Case Complete, ARX, and PMD. The market is highly competitive, with vendors offering a range of solutions to cater to the diverse needs of enterprises. Strategic partnerships, product innovations, and mergers and acquisitions are common competitive strategies.

The global web analytics market, valued at $5529.7 million in 2025, is poised for substantial growth. While the provided CAGR is missing, considering the rapid advancements in digital technologies and the increasing reliance on data-driven decision-making across industries, a conservative estimate would place the Compound Annual Growth Rate (CAGR) between 15% and 20% for the forecast period 2025-2033. This growth is fueled by several key drivers: the rising adoption of cloud-based analytics solutions, the increasing demand for real-time data insights, and the growing need for personalized customer experiences. Furthermore, the expansion of e-commerce and the proliferation of mobile devices are significantly contributing to the market's expansion. Emerging trends such as artificial intelligence (AI) and machine learning (ML) integration within web analytics platforms are further enhancing analytical capabilities and driving market growth. While challenges like data privacy concerns and the complexity of integrating diverse data sources exist, the overall market outlook remains positive, suggesting a significant increase in market value by 2033. The competitive landscape is dynamic, with a mix of established players like Adobe, Google, and IBM alongside agile startups like Heap and Mouseflow. These companies offer a range of solutions catering to different business sizes and needs, from basic website traffic analysis to sophisticated predictive analytics. The market is witnessing a shift towards more user-friendly and visually appealing dashboards, making web analytics accessible to a broader range of users beyond dedicated data scientists. This democratization of data, coupled with ongoing technological advancements, promises to further accelerate market growth and consolidate the position of web analytics as a critical component of successful digital strategies across all sectors.

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...

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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.
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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)

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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,

The global website monitoring tools market is projected to grow from USD XXX million in 2025 to USD XXX million by 2033, at a CAGR of XX%. The market is driven by the increasing adoption of cloud-based applications, the need for real-time monitoring of website performance, and the growing awareness of the importance of website uptime. Additionally, the rising demand for mobile-friendly websites and the increasing complexity of website architectures are further contributing to the growth of the market. Key segments of the website monitoring tools market include application (e-commerce, banking, healthcare, education, government), type (uptime monitoring, performance monitoring, security monitoring), and region (North America, Europe, Asia Pacific, Middle East & Africa, and South America). Major players in the market include AppDynamics, New Relic, SmartBear, Dynatrace, LogicMonitor, SolarWinds, Pingdom, Riverbed, Ghostery, Geckoboard, Google, Jetpack, Uptime Robot, and ManageWP. These companies offer a wide range of website monitoring solutions, including cloud-based, on-premises, and hybrid solutions.

The global website design company services market size is expected to reach $189.5 billion by 2033, growing at a CAGR of 12.5% from 2023 to 2033. The market experienced significant growth between 2019 and 2024, and a further increase is projected over the forecast period of 2023-2033. This growth can be attributed to factors such as the increasing adoption of digital technologies, the rising need for online presence, and the growing awareness of the importance of website design for businesses. Key market segments include application (individual, enterprise, others) and type (online service, offline service). Among these, the enterprise segment is expected to hold the largest market share due to the growing demand from businesses for professional website design services to enhance their online presence and reach potential customers. Geographically, the North America region is projected to dominate the market due to the presence of a large number of established website design companies, while the Asia Pacific region is expected to witness the highest CAGR during the forecast period.