Web Analytics Market size was valued at USD 6.16 Billion in 2024 and is projected to reach USD 24.07 Billion by 2032, growing at a CAGR of 18.58% during the forecast period 2026-2032.Global Web Analytics Market DriversThe digital landscape is in constant flux, and at its core, understanding user behavior is paramount for any business aiming to thrive. This imperative fuels the robust expansion of the Web Analytics Market, driven by a confluence of technological advancements, evolving business needs, and shifting consumer behaviors. Let's delve into the major forces propelling this vital industry forward.Digitalization and the Explosive Growth of Online Presence: The most fundamental driver is the relentless march of digitalization. Businesses across every sector are establishing, expanding, and optimizing their online presence, whether through sophisticated e-commerce platforms, informative corporate websites, or engaging mobile applications. As more operations, customer interactions, and commerce migrate to the digital realm, the sheer volume of online activity creates an insatiable demand for tools that can decipher user journeys, measure website performance, and identify areas for improvement. This foundational shift necessitates web analytics to transform raw digital interactions into actionable insights, making it indispensable for strategic decision-making in the modern business environment.The Imperative for Data-Driven Decision Making: In today's competitive landscape, gut feelings and anecdotal evidence are no longer sufficient. Businesses are increasingly recognizing the critical importance of basing their strategies on empirical data. Web analytics provides this crucial foundation, offering deep insights into customer behavior, site usage patterns, conversion funnels, and potential drop-off points. From optimizing marketing spend to refining product offerings and enhancing user experience, data-driven decision-making, powered by comprehensive web analytics, allows companies to minimize risks, maximize opportunities, and achieve measurable growth, thereby solidifying its position as a core business intelligence tool.Proliferation of Mobile Devices and Mobile Web Traffic: The smartphone revolution has profoundly reshaped how users interact with the internet. With billions of people globally accessing the web predominantly via mobile devices and tablets, understanding mobile-specific behaviors has become a paramount concern. Web analytics tools are evolving rapidly to effectively capture and analyze interactions across a myriad of devices, operating systems, and browser types. This includes tracking mobile app usage, responsive website performance, and ensuring a seamless cross-device user experience. The pervasive nature of mobile traffic means that robust mobile analytics capabilities are no longer a luxury but a necessity for any comprehensive web analytics solution.

The dataset provides 12 months (August 2016 to August 2017) of obfuscated Google Analytics 360 data from the Google Merchandise Store , a real ecommerce store that sells Google-branded merchandise, in BigQuery. It’s a great way analyze business data and learn the benefits of using BigQuery to analyze Analytics 360 data Learn more about the data The data includes The data is typical of what an ecommerce website would see and includes the following information:Traffic source data: information about where website visitors originate, including data about organic traffic, paid search traffic, and display trafficContent data: information about the behavior of users on the site, such as URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions on the Google Merchandise Store website.Limitations: All users have view access to the dataset. This means you can query the dataset and generate reports but you cannot complete administrative tasks. Data for some fields is obfuscated such as fullVisitorId, or removed such as clientId, adWordsClickInfo and geoNetwork. “Not available in demo dataset” will be returned for STRING values and “null” will be returned for INTEGER values when querying the fields containing no data.This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery

DataForSEO Labs API offers three powerful keyword research algorithms and historical keyword data:

• Related Keywords from the “searches related to” element of Google SERP. • Keyword Suggestions that match the specified seed keyword with additional words before, after, or within the seed key phrase. • Keyword Ideas that fall into the same category as specified seed keywords. • Historical Search Volume with current cost-per-click, and competition values.

Based on in-market categories of Google Ads, you can get keyword ideas from the relevant Categories For Domain and discover relevant Keywords For Categories. You can also obtain Top Google Searches with AdWords and Bing Ads metrics, product categories, and Google SERP data.

You will find well-rounded ways to scout the competitors:

• Domain Whois Overview with ranking and traffic info from organic and paid search. • Ranked Keywords that any domain or URL has positions for in SERP. • SERP Competitors and the rankings they hold for the keywords you specify. • Competitors Domain with a full overview of its rankings and traffic from organic and paid search. • Domain Intersection keywords for which both specified domains rank within the same SERPs. • Subdomains for the target domain you specify along with the ranking distribution across organic and paid search. • Relevant Pages of the specified domain with rankings and traffic data. • Domain Rank Overview with ranking and traffic data from organic and paid search. • Historical Rank Overview with historical data on rankings and traffic of the specified domain from organic and paid search. • Page Intersection keywords for which the specified pages rank within the same SERP.

All DataForSEO Labs API endpoints function in the Live mode. This means you will be provided with the results in response right after sending the necessary parameters with a POST request.

The limit is 2000 API calls per minute, however, you can contact our support team if your project requires higher rates.

We offer well-rounded API documentation, GUI for API usage control, comprehensive client libraries for different programming languages, free sandbox API testing, ad hoc integration, and deployment support.

We have a pay-as-you-go pricing model. You simply add funds to your account and use them to get data. The account balance doesn't expire.

Scientists are increasingly engaging the web to provide formal and informal science education opportunities. Despite the prolific growth of web-based resources, systematic evaluation and assessment of their efficacy remains limited. We used clickstream analytics, a widely available method for tracking website visitors and their behavior, to evaluate >60,000 visits over three years to an educational website focused on ecology. Visits originating from search engine queries were a small proportion of the traffic, suggesting the need to actively promote websites to drive visitation. However, the number of visits referred to the website per social media post varied depending on the social media platform and the quality of those visits (e.g., time on site and number of pages viewed) was significantly lower than visits originating from other referring websites. In particular, visitors referred to the website through targeted promotion (e.g., inclusion in a website listing classroom teaching resources) had higher quality visits. Once engaged in the site's core content, visitor retention was high; however, visitors rarely used the tutorial resources that serve to explain the site's use. Our results demonstrate that simple changes in website design, content and promotion are likely to increase the number of visitors and their engagement. While there is a growing emphasis on using the web to broaden the impacts of biological research, time and resources remain limited. Clickstream analytics provides an easily accessible, relatively fast and quantitative means by which those engaging in educational outreach can improve upon their efforts.

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.

gms-vis is a web-based implementation of our visual-analytics approach for assessing remote-sensing data. It is implemented based on the GWT framework. Once deployed through a webserver it acts as the user interface for the GeoMultiSens (GMS) platform. Within the interface users can intuitively define spatial, temporal as well as quality constraints, for remote sensing scenes. A heatmap enables the user to assess the spatial distribution of selected scenes, while a time histogram allows the user to assess their temporal distribution. Finally, users can specify a workflow which will be executed by the GeoMultiSens platform. Though gms-vis is part of the GeoMultiSens platform, it is relatively self-contained and can be attached to different analysis frameworks and platforms with reasonable effort.

This dataset contains the web analytics of the data catalogue of the Canton of Zurich (www.zh.ch/daten). The data shows which records (dataset) and resources (distribution) (see definition in DCAP-AP CH, linked below) were accessed and visited how often. The calls and visits from external websites that have integrated the data catalog are counted. For technical reasons, not all accesses are registered (e.g. 'Ad-Blocker' when using ad blockers). We obtain the data from several Matomo APIs. Records and resources with 0 calls/visitors are not shown. Calls via metadata catalog such as opendata.swiss are not shown.

Website Screenshot Software Market Outlook

The global website screenshot software market size was valued at USD 250 million in 2023 and is projected to reach USD 550 million by 2032, growing at a compound annual growth rate (CAGR) of 9.1% from 2024 to 2032. The primary growth driver for this market is the increasing need for detailed website analytics and competitive analysis, facilitated by the enhanced functionality that website screenshot software provides.

One of the significant growth factors contributing to the expansion of the website screenshot software market is the booming e-commerce sector. As businesses increasingly move online, the demand for tools that can capture, analyze, and archive website content has surged. E-commerce companies, in particular, rely heavily on website screenshot software to track their competitors' pricing strategies, promotional activities, and website design changes. Moreover, the emphasis on digital marketing strategies necessitates frequent monitoring and analysis of various web pages, propelling the demand for such software.

The rise in remote work is another critical factor driving the market growth. With teams working from various locations, the need for collaborative tools that facilitate real-time sharing of web content has become imperative. Website screenshot software allows team members to capture and share web pages seamlessly, aiding in better communication and faster decision-making. Such tools are particularly beneficial for web development and digital marketing teams, enabling them to provide visual feedback and suggestions efficiently.

Technological advancements and the integration of advanced features like automated screenshot capture, scheduling, and cloud storage capabilities are also contributing to market growth. These advancements make it easier for users to capture, store, and manage large volumes of web content. Additionally, the increasing adoption of cloud-based solutions offers flexibility and scalability, further boosting the adoption of website screenshot software. The continuous innovation in software capabilities is expected to sustain market growth over the forecast period.

In the realm of digital tools, Web Scraper Software plays a pivotal role in complementing website screenshot software. While screenshot software captures static images of web pages, web scraper software goes a step further by extracting data from websites for analysis. This capability is crucial for businesses that require detailed insights into competitor activities, market trends, and consumer behavior. By automating the data extraction process, web scraper software saves time and resources, allowing companies to focus on strategic decision-making. The synergy between website screenshot and web scraper software can significantly enhance a company's ability to conduct comprehensive web analytics and competitive analysis.

Regionally, North America holds a significant share of the website screenshot software market, driven by the presence of major technology companies and a high adoption rate of advanced digital tools. However, Asia Pacific is projected to witness the highest growth rate during the forecast period, thanks to the rapid digital transformation in emerging economies, increasing internet penetration, and the burgeoning e-commerce sector. Europe is also a key market, with growing investments in digital marketing and web development driving the demand for website screenshot software.

Deployment Type Analysis

The website screenshot software market is segmented into cloud-based and on-premises deployment. Cloud-based deployment is expected to dominate the market owing to its benefits such as ease of access, scalability, and lower upfront costs. Cloud-based solutions allow users to access the software from anywhere, making it highly suitable for remote teams and enterprises with multiple locations. This flexibility is a significant advantage, especially in the current scenario where remote working has become the norm for many organizations. Furthermore, cloud-based deployment facilitates automatic updates and maintenance, reducing the burden on in-house IT teams.

On-premises deployment, however, holds its significance in the market, particularly among large enterprises with stringent data security and compliance requirements. These organizations prefer to have complete control over their data and infrastructure, which is achievable through on-p

Locations of signalised intersections and signalised pedestrian crossings in Hamilton City

Column_InfoSite_Number, int : SCATS ID - Unique identifierRoad_1, varchar : First road descriptorRoad_2, varchar : Second road descriptor - 'Ped xing' means it is a mid-block pedestrian signalRoad_3, varchar : Third road descriptor if relevantSite_Type, varchar : Pedestrian crossing or intersection, and whether on a state highway or council roadIs_CBD, int : Site is within the CBD boundaryEasting, decimal : Eastward-measured distance in NZTM projectionNorthing, decimal : Northward-measured distance in NZTM projectionLatitude, decimal : North-south geographic coordinatesLongitude, decimal : East-west geographic coordinates

Relationship

This table is referenced by Traffic_Signal_Detector

Analytics

For convenience Hamilton City Council has also built a Quick Analytics Dashboard over this dataset that you can access here.

Disclaimer

Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works.

Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data.

While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data:

‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

Discreet, accessible interventions are urgently needed to mitigate the risk of intimate-partner violence (IPV) and other relationship barriers that women encounter to using HIV prevention methods such as pre-exposure prophylaxis (PrEP). We adapted a counsellor-administered intervention, CHARISMA, into a mobile-optimized website to enhance accessibility and reduce human resources required for HIV prevention and relationship counseling. Using human-centered design and participatory methods, CHARISMA was adapted through workshops with former CHARISMA in-person intervention participants (n = 14; ages 18–45) and web development ‘sprints’ combined with cognitive interviews (n = 24). ‘CHARISMA mobile’ was then beta-tested with 81 women naïve to the in-person intervention. In beta-testing, participants used a ‘think aloud’ process to provide feedback on ease of use and rated design, functionality, comprehension, confidentiality, safety, and usefulness on a scale of 1 to 5 via a survey. Data were conducted in four rounds, interspersed with rapid assessment according to go/no-go criteria, and website adaptations. The updated website was pilot tested for ‘real-world’ feasibility and acceptability among 159 women using their own smartphones at a location of their choice. Feedback was measured via surveys and website analytics. Workshops and cognitive interviews generated insights on technology use, contextual adaptations, and confidentiality, which were integrated into the beta version. The beta version met all ‘go’ criteria and was further adapted for pilot testing. In pilot testing, users found the website was useful (mean rating 4.54 out of 5), safe (4.5 out of 5), and had few concerns about confidentiality (1.75, representing low concern). On average, users rated the website more than 4 stars out of 5. Beta and pilot-testing suggested the smartphone-optimized website was well-accepted, relevant, engaging, feasible to administer, discreet and safe. Results contributed to a refined website, suitable for adaptations to other contexts and further evaluation where outcomes related to PrEP use and relationships should be assessed.

See uploaded ReadMe file.

This Kaggle dataset comes from an output dataset that powers my March Madness Data Analysis dashboard in Domo. - Click here to view this dashboard: Dashboard Link - Click here to view this dashboard features in a Domo blog post: Hoops, Data, and Madness: Unveiling the Ultimate NCAA Dashboard

This dataset offers one the most robust resource you will find to discover key insights through data science and data analytics using historical NCAA Division 1 men's basketball data. This data, sourced from KenPom, goes as far back as 2002 and is updated with the latest 2025 data. This dataset is meticulously structured to provide every piece of information that I could pull from this site as an open-source tool for analysis for March Madness.

Key features of the dataset include: - Historical Data: Provides all historical KenPom data from 2002 to 2025 from the Efficiency, Four Factors (Offense & Defense), Point Distribution, Height/Experience, and Misc. Team Stats endpoints from KenPom's website. Please note that the Height/Experience data only goes as far back as 2007, but every other source contains data from 2002 onward. - Data Granularity: This dataset features an individual line item for every NCAA Division 1 men's basketball team in every season that contains every KenPom metric that you can possibly think of. This dataset has the ability to serve as a single source of truth for your March Madness analysis and provide you with the granularity necessary to perform any type of analysis you can think of. - 2025 Tournament Insights: Contains all seed and region information for the 2025 NCAA March Madness tournament. Please note that I will continually update this dataset with the seed and region information for previous tournaments as I continue to work on this dataset.

These datasets were created by downloading the raw CSV files for each season for the various sections on KenPom's website (Efficiency, Offense, Defense, Point Distribution, Summary, Miscellaneous Team Stats, and Height). All of these raw files were uploaded to Domo and imported into a dataflow using Domo's Magic ETL. In these dataflows, all of the column headers for each of the previous seasons are standardized to the current 2025 naming structure so all of the historical data can be viewed under the exact same field names. All of these cleaned datasets are then appended together, and some additional clean up takes place before ultimately creating the intermediate (INT) datasets that are uploaded to this Kaggle dataset. Once all of the INT datasets were created, I joined all of the tables together on the team name and season so all of these different metrics can be viewed under one single view. From there, I joined an NCAAM Conference & ESPN Team Name Mapping table to add a conference field in its full length and respective acronyms they are known by as well as the team name that ESPN currently uses. Please note that this reference table is an aggregated view of all of the different conferences a team has been a part of since 2002 and the different team names that KenPom has used historically, so this mapping table is necessary to map all of the teams properly and differentiate the historical conferences from their current conferences. From there, I join a reference table that includes all of the current NCAAM coaches and their active coaching lengths because the active current coaching length typically correlates to a team's success in the March Madness tournament. I also join another reference table to include the historical post-season tournament teams in the March Madness, NIT, CBI, and CIT tournaments, and I join another reference table to differentiate the teams who were ranked in the top 12 in the AP Top 25 during week 6 of the respective NCAA season. After some additional data clean-up, all of this cleaned data exports into the "DEV _ March Madness" file that contains the consolidated view of all of this data.

This dataset provides users with the flexibility to export data for further analysis in platforms such as Domo, Power BI, Tableau, Excel, and more. This dataset is designed for users who wish to conduct their own analysis, develop predictive models, or simply gain a deeper understanding of the intricacies that result in the excitement that Division 1 men's college basketball provides every year in March. Whether you are using this dataset for academic research, personal interest, or professional interest, I hope this dataset serves as a foundational tool for exploring the vast landscape of college basketball's most riveting and anticipated event of its season.

Comprehensive comparison of Output Speed (Output Tokens per Second) vs. Price (USD per 1M Tokens) by Provider

Comparison of Price: USD per 1M Tokens; Lower is better by Provider