Analysis of ‘Popular Website Traffic Over Time ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/popular-website-traffice on 13 February 2022.

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

About this dataset

Background

Have you every been in a conversation and the question comes up, who uses Bing? This question comes up occasionally because people wonder if these sites have any views. For this research study, we are going to be exploring popular website traffic for many popular websites.

Methodology

The data collected originates from SimilarWeb.com.

Source

For the analysis and study, go to The Concept Center

This dataset was created by Chase Willden and contains around 0 samples along with 1/1/2017, Social Media, technical information and other features such as: - 12/1/2016 - 3/1/2017 - and more.

How to use this dataset

• Analyze 11/1/2016 in relation to 2/1/2017
• Study the influence of 4/1/2017 on 1/1/2017
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Chase Willden

Start A New Notebook!

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

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 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

According to the analysis conducted in February 2021, the biggest traffic on emag.hu was generated by online searches. In over 23 percent of the cases the website was accessed directly.

Analysis of ‘Website Analytics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ecee4df3-8149-4b74-8927-428ea920b758 on 13 February 2022.

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

Web traffic statistics for the several City-Parish websites, brla.gov, city.brla.gov, Red Stick Ready, GIS, Open Data etc. Information provided by Google Analytics.

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

Analysis of ‘NYC.gov Web Analytics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/f2b7ec11-c2ad-412c-8a63-914f40515c4d on 28 January 2022.

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

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

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

Comprehensive analysis of Amazon's daily website traffic including visitor counts, traffic sources, mobile vs desktop usage, and seasonal patterns based on May 2025 data.

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

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.

Analysis of ‘Traffic Camera’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/9b4e892b-98c2-411f-b55a-0742094a37ad on 27 January 2022.

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

A web service showing traffic camera locations in the City of New Orleans.

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

The competitive landscape of the website analytics market, encompassing players like Google, BuiltWith, SEMrush, and others, is dynamic and characterized by significant growth. The market's size in 2025 is estimated at $15 billion, reflecting a Compound Annual Growth Rate (CAGR) of 15% from 2019. This robust growth is driven by increasing reliance on data-driven decision-making across businesses, expanding digital marketing strategies, and the rise of e-commerce. Key trends include the integration of AI and machine learning for more sophisticated analysis, the increasing demand for real-time data, and a growing focus on personalized user experiences. While the market faces constraints such as data privacy concerns and the complexity of integrating diverse data sources, the overall outlook remains highly positive. The market is segmented by solution type (website analytics, social media analytics, app analytics), deployment mode (cloud, on-premise), and enterprise size (small, medium, large). Companies are focusing on developing advanced analytical capabilities, strengthening partnerships, and expanding their global reach to maintain their competitive edge. The competitive analysis reveals a clear dominance by established players such as Google Analytics, leveraging its massive user base and comprehensive feature set. However, specialized tools like SEMrush and Ahrefs cater to niche needs like SEO analysis and backlink profiling. Smaller players often differentiate themselves through specialized features, superior customer support, or cost-effectiveness, carving out space within the market. Future market share will largely depend on the ability of companies to innovate, adapt to changing privacy regulations, and successfully integrate cutting-edge technologies like AI and machine learning into their offerings. The competition is expected to intensify further with the emergence of new players and the constant evolution of analytical techniques. Strategic mergers and acquisitions are also likely to reshape the market structure in the coming years.

Introduction

Google Analytics Statistics: Google Analytics is one of the most popular tools to monitor your website’s performance, as it gathers data regarding customer behaviour, engagement, and preferences. They are segmented into two different versions: Google Analytics 4 (GA4) and Google Analytics 360 (GA360). Google Analytics was developed by Google and was released on November 14, 2005.

This article includes several detailed analyses from different insights, including overall market analysis, user bases, visitors' interaction with a website, such as page views, session duration, traffic sources, and conversion rates.

The Alternative Data Market size was valued at USD 7.20 billion in 2023 and is projected to reach USD 126.50 billion by 2032, exhibiting a CAGR of 50.6 % during the forecasts period. The use and processing of information that is not in financial databases is known as the alternative data market. Such data involves posts in social networks, satellite images, credit card transactions, web traffic and many others. It is mostly used in financial field to make the investment decisions, managing risks and analyzing competitors, giving a more general view on market trends as well as consumers’ attitude. It has been found that there is increasing requirement for the obtaining of data from unconventional sources as firms strive to nose ahead in highly competitive markets. Some current trend are the finding of AI and machine learning to drive large sets of data and the broadening utilization of the so called “Alternative Data” across industries that are not only the finance industry. Recent developments include: In April 2023, Thinknum Alternative Data launched new data fields to its employee sentiment datasets for people analytics teams and investors to use this as an 'employee NPS' proxy, and support highly-rated employers set up interviews through employee referrals. , In September 2022, Thinknum Alternative Data announced its plan to combine data Similarweb, SensorTower, Thinknum, Caplight, and Pathmatics with Lagoon, a sophisticated infrastructure platform to deliver an alternative data source for investment research, due diligence, deal sourcing and origination, and post-acquisition strategies in private markets. , In May 2022, M Science LLC launched a consumer spending trends platform, providing daily, weekly, monthly, and semi-annual visibility into consumer behaviors and competitive benchmarking. The consumer spending platform provided real-time insights into consumer spending patterns for Australian brands and an unparalleled business performance analysis. .

The FDOT Annual Average Daily Traffic feature class provides spatial information on Annual Average Daily Traffic section breaks for the state of Florida. In addition, it provides affiliated traffic information like KFCTR, DFCTR and TFCTR among others. This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 06/14/2025.Download Data: Enter Guest as Username to download the source shapefile from here: https://ftp.fdot.gov/file/d/FTP/FDOT/co/planning/transtat/gis/shapefiles/aadt.zip

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.

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]

Snapshot img

Digital Marketing Software (DMS) Market Size 2025-2029

The digital marketing software (DMS) market size is forecast to increase by USD 133.59 billion, at a CAGR of 18.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of new data sources and regulatory innovations. Businesses are leveraging these advancements to gain valuable customer insights and enhance their marketing strategies. Another key factor fueling market expansion is the widespread use of social media and e-commerce platforms for marketing purposes. These channels offer businesses an opportunity to reach a larger and more diverse audience, fostering increased competition and innovation. However, the market is not without challenges. Data privacy and security concerns continue to pose a significant obstacle, as companies strive to protect sensitive customer information while still delivering personalized marketing experiences.
Balancing these competing priorities will require continued investment in advanced security technologies and robust data management practices. By addressing these challenges and capitalizing on emerging opportunities, companies can effectively navigate the dynamic digital marketing landscape and drive growth in the DMS Market.

What will be the Size of the Digital Marketing Software (DMS) 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 shaping its landscape. Seamlessly integrated solutions are transforming marketing efforts across various sectors. Paid advertising (PPC) campaigns and website analytics provide valuable insights into customer behavior, enabling data-driven decision-making. Keyword ranking and technical SEO tools optimize websites for search engines, enhancing visibility. Multivariate testing and affiliate marketing foster conversion and customer segmentation. Website authority and content promotion bolster brand awareness. Form analytics and influencer marketing offer invaluable data on user experience (UX) and engagement. Scroll maps and video optimization cater to evolving consumer preferences. Content marketing and Google Analytics facilitate content strategy and performance measurement.

Local SEO, international SEO, and e-commerce SEO cater to diverse business needs. Website security, email marketing, and link building ensure trust and credibility. On-page optimization and website design optimize user experience. Search console and content syndication expand reach. Social media marketing and structured data enhance online presence. A/B testing and website traffic analysis facilitate continuous improvement. Mobile optimization and image optimization cater to the growing mobile user base. Marketing automation and competitor analysis streamline campaigns and inform strategy. Bing ads and lead generation tools expand advertising reach. Landing pages and XML sitemaps optimize conversion funnels. Bounce rate analysis and backlink checker ensure website health.

Schema markup and marketing automation tools improve search engine understanding of content.

How is this Digitaling Software (DMS) Industry segmented?

The digitaling software (dms) 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.

End-user

  Large enterprises
  Small and medium enterprises (SMEs)


Service

  Professional services
  Managed services


Revenue Stream

  Subscription-based
  License-based
  Pay-per-use
  Freemium


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Russia
    UK


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By End-user Insights

The large enterprises segment is estimated to witness significant growth during the forecast period.

The market is witnessing significant growth due to the increasing adoption of advanced marketing tools by businesses of all sizes. Customer segmentation and social media engagement are key areas where DMS plays a pivotal role, enabling businesses to target their audience effectively and engage with them in real-time. With the rise of pay-per-click (PPC) advertising and search engine optimization (SEO) tools, marketing campaigns are becoming more data-driven and targeted. Local SEO and international SEO are essential for businesses looking to expand their reach, while domain authority and average session duration are crucial metrics for measuring the success of marketing efforts.

User experience (UX) is another critical factor, with content calendars, website audits

As of December 2024, over half of the traffic on the web portal Duckduckgo.com was generated in the United States, its country of origin. Germany and the United Kingdom accounted for 7.06 percent and 4.76 percent of its accesses, followed by Canada and France, representing 3.91 percent and 2.98 percent each.

The Alternative Data (Alt-Data) market is experiencing robust growth, driven by the increasing demand for enhanced investment strategies and improved decision-making across various sectors. The market's value in 2025 is estimated at $8 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 20% from 2025 to 2033. This significant expansion is fueled by several key drivers, including the proliferation of readily available digital data sources, advancements in data analytics and artificial intelligence (AI), and the growing need for more granular and timely insights beyond traditional data sources. The BFSI (Banking, Financial Services, and Insurance) sector is currently the largest application segment, leveraging alt-data for credit scoring, fraud detection, and risk management. However, substantial growth is also expected from the IT and Telecommunications, Retail and Logistics, and Industrial sectors, as these industries increasingly recognize the value of alternative data for optimizing operations and gaining a competitive edge. The widespread adoption of cloud computing and the increasing affordability of sophisticated analytical tools further accelerate market growth. Several trends are shaping the Alt-Data landscape. The increasing sophistication of AI and machine learning algorithms allows for more complex data analysis, leading to more accurate predictions and improved decision-making. Furthermore, the emergence of new data sources, including social media sentiment analysis, web traffic data, and satellite imagery, expands the scope and potential of alt-data applications. However, challenges remain, including data quality concerns, regulatory uncertainties regarding data privacy and security, and the need for skilled professionals to manage and interpret complex datasets effectively. These restraints, while present, are not expected to significantly impede the overall positive growth trajectory of the Alt-Data market in the forecast period. The market's segmentation by data type (credit card transactions, web data, sentiment analysis etc.) reflects the diverse applications and evolving nature of this dynamic market.

Phishing Websites Dataset

Overview

This dataset is designed to aid in the analysis and detection of phishing websites. It contains various features that help distinguish between legitimate and phishing websites based on their structural, security, and behavioral attributes.

Dataset Information

• Total Columns: 31 (30 Features + 1 Target)
• Target Variable: Result (Indicates whether a website is phishing or legitimate)

Features Description

URL-Based Features

• Prefix_Suffix – Checks if the URL contains a hyphen (-), which is commonly used in phishing domains.
• double_slash_redirecting – Detects if the URL redirects using //, which may indicate a phishing attempt.
• having_At_Symbol – Identifies the presence of @ in the URL, which can be used to deceive users.
• Shortining_Service – Indicates whether the URL uses a shortening service (e.g., bit.ly, tinyurl).
• URL_Length – Measures the length of the URL; phishing URLs tend to be longer.
• having_IP_Address – Checks if an IP address is used in place of a domain name, which is suspicious.

Domain-Based Features

• having_Sub_Domain – Evaluates the number of subdomains; phishing sites often have excessive subdomains.
• SSLfinal_State – Indicates whether the website has a valid SSL certificate (secure connection).
• Domain_registeration_length – Measures the duration of domain registration; phishing sites often have short lifespans.
• age_of_domain – The age of the domain in days; older domains are usually more trustworthy.
• DNSRecord – Checks if the domain has valid DNS records; phishing domains may lack these.

Webpage-Based Features

• Favicon – Determines if the website uses an external favicon (which can be a sign of phishing).
• port – Identifies if the site is using suspicious or non-standard ports.
• HTTPS_token – Checks if "HTTPS" is included in the URL but is used deceptively.
• Request_URL – Measures the percentage of external resources loaded from different domains.
• URL_of_Anchor – Analyzes anchor tags (<a> links) and their trustworthiness.
• Links_in_tags – Examines <meta>, <script>, and <link> tags for external links.
• SFH (Server Form Handler) – Determines if form actions are handled suspiciously.
• Submitting_to_email – Checks if forms submit data directly to an email instead of a web server.
• Abnormal_URL – Identifies if the website’s URL structure is inconsistent with common patterns.
• Redirect – Counts the number of redirects; phishing websites may have excessive redirects.

Behavior-Based Features

• on_mouseover – Checks if the website changes content when hovered over (used in deceptive techniques).
• RightClick – Detects if right-click functionality is disabled (phishing sites may disable it).
• popUpWindow – Identifies the presence of pop-ups, which can be used to trick users.
• Iframe – Checks if the website uses <iframe> tags, often used in phishing attacks.

Traffic & Search Engine Features

• web_traffic – Measures the website’s Alexa ranking; phishing sites tend to have low traffic.
• Page_Rank – Google PageRank score; phishing sites usually have a low PageRank.
• Google_Index – Checks if the website is indexed by Google (phishing sites may not be indexed).
• Links_pointing_to_page – Counts the number of backlinks pointing to the website.
• Statistical_report – Uses external sources to verify if the website has been reported for phishing.

Target Variable

• Result – The classification label (1: Legitimate, -1: Phishing)

Usage

This dataset is valuable for:
✅ Machine Learning Models – Developing classifiers for phishing detection.
✅ Cybersecurity Research – Understanding patterns in phishing attacks.
✅ Browser Security Extensions – Enhancing anti-phishing tools.

Analysis of ‘1.08 High Severity Traffic Crashes (summary)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/cd29e892-b0a8-4b4e-8b5e-3f81a9df49a2 on 11 February 2022.

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

Fatal and serious injury crashes are not “accidents” and are preventable. The City of Tempe is committed to reducing the number of fatal and serious injury crashes to zero. This data page provides details about the performance measure related to High Severity Traffic Crashes as well as access to the data sets and any supplemental data. Click on the Showcases tab for visual representations of this data. The Engineering and Transportation Department uses this data to improve safety in Tempe.

This page provides data for the High Severity Traffic Crashes performance measure.

City of Tempe crash data summarized to show fatal and serious injury crashes by year.

The performance measure dashboard is available at 1.08 High Severity Traffic Crashes

Additional Information

Source: Arizona Department of Transportation (ADOT)

Contact:  Julian Dresang

Contact E-Mail:  Julian_Dresang@tempe.gov

Data Source Type:  CSV files and Excel spreadsheets can be downloaded from ADOT website

Preparation Method:  Data is sorted to remove license plate numbers and other sensitive information

Publish Frequency:  Monthly

Publish Method:  Manual

Data Dictionary

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