Unlock the Potential of Your Web Traffic with Advanced Data Resolution

In the digital age, understanding and leveraging web traffic data is crucial for businesses aiming to thrive online. Our pioneering solution transforms anonymous website visits into valuable B2B and B2C contact data, offering unprecedented insights into your digital audience. By integrating our unique tag into your website, you unlock the capability to convert 25-50% of your anonymous traffic into actionable contact rows, directly deposited into an S3 bucket for your convenience. This process, known as "Web Traffic Data Resolution," is at the forefront of digital marketing and sales strategies, providing a competitive edge in understanding and engaging with your online visitors.

Comprehensive Web Traffic Data Resolution Our product stands out by offering a robust solution for "Web Traffic Data Resolution," a process that demystifies the identities behind your website traffic. By deploying a simple tag on your site, our technology goes to work, analyzing visitor behavior and leveraging proprietary data matching techniques to reveal the individuals and businesses behind the clicks. This innovative approach not only enhances your data collection but does so with respect for privacy and compliance standards, ensuring that your business gains insights ethically and responsibly.

Deep Dive into Web Traffic Data At the core of our solution is the sophisticated analysis of "Web Traffic Data." Our system meticulously collects and processes every interaction on your site, from page views to time spent on each section. This data, once anonymous and perhaps seen as abstract numbers, is transformed into a detailed ledger of potential leads and customer insights. By understanding who visits your site, their interests, and their contact information, your business is equipped to tailor marketing efforts, personalize customer experiences, and streamline sales processes like never before.

Benefits of Our Web Traffic Data Resolution Service Enhanced Lead Generation: By converting anonymous visitors into identifiable contact data, our service significantly expands your pool of potential leads. This direct enhancement of your lead generation efforts can dramatically increase conversion rates and ROI on marketing campaigns.

Targeted Marketing Campaigns: Armed with detailed B2B and B2C contact data, your marketing team can create highly targeted and personalized campaigns. This precision in marketing not only improves engagement rates but also ensures that your messaging resonates with the intended audience.

Improved Customer Insights: Gaining a deeper understanding of your web traffic enables your business to refine customer personas and tailor offerings to meet market demands. These insights are invaluable for product development, customer service improvement, and strategic planning.

Competitive Advantage: In a digital landscape where understanding your audience can make or break your business, our Web Traffic Data Resolution service provides a significant competitive edge. By accessing detailed contact data that others in your industry may overlook, you position your business as a leader in customer engagement and data-driven strategies.

Seamless Integration and Accessibility: Our solution is designed for ease of use, requiring only the placement of a tag on your website to start gathering data. The contact rows generated are easily accessible in an S3 bucket, ensuring that you can integrate this data with your existing CRM systems and marketing tools without hassle.

How It Works: A Closer Look at the Process Our Web Traffic Data Resolution process is streamlined and user-friendly, designed to integrate seamlessly with your existing website infrastructure:

Tag Deployment: Implement our unique tag on your website with simple instructions. This tag is lightweight and does not impact your site's loading speed or user experience.

Data Collection and Analysis: As visitors navigate your site, our system collects web traffic data in real-time, analyzing behavior patterns, engagement metrics, and more.

Resolution and Transformation: Using advanced data matching algorithms, we resolve the collected web traffic data into identifiable B2B and B2C contact information.

Data Delivery: The resolved contact data is then securely transferred to an S3 bucket, where it is organized and ready for your access. This process occurs daily, ensuring you have the most up-to-date information at your fingertips.

Integration and Action: With the resolved data now in your possession, your business can take immediate action. From refining marketing strategies to enhancing customer experiences, the possibilities are endless.

Security and Privacy: Our Commitment Understanding the sensitivity of web traffic data and contact information, our solution is built with security and privacy at its core. We adhere to strict data protection regulat...

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

similarweb.com is ranked #1119 in IN with 18.51M Traffic. Categories: Information Technology, Online Services. Learn more about website traffic, market share, and more!

Web Analytics Market Outlook

According to our latest research, the global web analytics market size was valued at USD 8.4 billion in 2024, reflecting robust growth driven by the increasing adoption of digital platforms across industries. The market is projected to expand at a compound annual growth rate (CAGR) of 17.2% from 2025 to 2033, reaching an estimated USD 36.8 billion by 2033. This significant upsurge is primarily attributed to the escalating demand for actionable insights, data-driven decision-making, and the proliferation of online consumer activity. As per the latest research, enterprises worldwide are leveraging advanced web analytics tools to enhance customer engagement, improve marketing strategies, and drive business outcomes.

One of the principal growth factors fueling the web analytics market is the exponential increase in digitalization and internet penetration. Organizations across various sectors are rapidly transitioning their operations online, resulting in a surge of data generation through multiple digital touchpoints. This digital transformation has heightened the need for sophisticated web analytics solutions that can process vast volumes of data, extract meaningful patterns, and provide actionable insights. Moreover, the rise in e-commerce activities, coupled with the growing popularity of social media platforms, has created a fertile environment for the adoption of web analytics, enabling businesses to track consumer behavior, measure campaign effectiveness, and optimize user experiences.

Another critical driver for the web analytics market is the integration of artificial intelligence (AI) and machine learning (ML) technologies. These advanced technologies are revolutionizing the way organizations analyze web data by enabling predictive analytics, real-time reporting, and personalized recommendations. AI-powered web analytics tools can automatically identify trends, anomalies, and customer preferences, empowering businesses to make data-driven decisions faster and more accurately. Furthermore, the increasing focus on omnichannel marketing strategies and the need to unify customer data across different platforms have further accelerated the demand for comprehensive web analytics solutions.

The regulatory landscape and growing emphasis on data privacy and compliance are also shaping the web analytics market. With the implementation of stringent data protection regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, organizations are compelled to adopt web analytics tools that ensure data security and privacy. This has led to the development of privacy-centric analytics platforms that offer enhanced data governance features, enabling businesses to comply with global regulatory requirements while still deriving valuable insights from web data. The ability to balance data-driven innovation with privacy considerations is becoming a key differentiator for vendors in this dynamic market.

From a regional perspective, North America continues to dominate the web analytics market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The region’s leadership is attributed to the presence of major technology providers, a mature digital ecosystem, and high levels of investment in analytics infrastructure. However, Asia Pacific is expected to witness the fastest growth during the forecast period, driven by the rapid adoption of digital technologies, expanding internet user base, and increasing investments in e-commerce and digital marketing. The growing awareness among businesses in emerging economies about the benefits of web analytics is further propelling market growth in this region.

Component Analysis

The web analytics market by component is bifurcated into software and services, with each segment playing a pivotal role in market expansion. The software segment holds the lion’s share of the market, driven by the continuous evolution of analytics plat

A study released in March 2025 that looked at about 35,000 websites found that online search channels were responsible for almost ** percent of the traffic generated to these domains. By the time of this study, direct traffic corresponded to around **** percent of visits to the analyzed websites. Meanwhile, large language models (LLMs) like ChatGPT and Gemini corresponded to around *** percent of the verified traffic, representing a share just below e-mail platforms.

This data set contains internet traffic data captured by an Internet Service Provider (ISP) using Mikrotik SDN Controller and packet sniffer tools. The data set includes traffic from over 2000 customers who use Fibre to the Home (FTTH) and Gpon internet connections. The data was collected over a period of several months and contains all traffic in its original format with headers and packets.

The data set contains information on inbound and outbound traffic, including web browsing, email, file transfers, and more. The data set can be used for research in areas such as network security, traffic analysis, and machine learning.

**Data Collection Method: ** The data was captured using Mikrotik SDN Controller and packet sniffer tools. These tools capture traffic data by monitoring network traffic in real-time. The data set contains all traffic data in its original format, including headers and packets.

**Data Set Content: ** The data set is provided in a CSV format and includes the following fields:

1. Timestamp: The date and time the traffic was captured
2. Source IP Address: The IP address of the device that sent the traffic Destination IP Address: The IP address of the device that received the traffic Protocol: The network protocol used for the traffic (e.g. TCP, UDP) Source Port: The port used by the source device for the traffic Destination Port: The port used by the destination device for the traffic Packet Size: The size of the packet in bytes Payload: The payload data of the packet The data set contains a large volume of traffic data from over 2000 customers. The data is organized by timestamp and includes all traffic data in its original format, including headers and packets. The data set contains both inbound and outbound traffic, and covers various types of internet traffic, including web browsing, email, file transfers, and more. one of listed protocols: ipsec-ah - IPsec AH protocol *ipsec-esp - IPsec ESP protocol ddp - datagram delivery protocol egp - exterior gateway protocol ggp - gateway-gateway protocol gre - general routing encapsulation hmp - host monitoring protocol idpr-cmtp - idpr control message transport icmp - internet control message protocol icmpv6 - internet control message protocol v6 igmp - internet group management protocol ipencap - ip encapsulated in ip ipip - ip encapsulation encap - ip encapsulation iso-tp4 - iso transport protocol class 4 ospf - open shortest path first pup - parc universal packet protocol pim - protocol independent multicast rspf - radio shortest path first rdp - reliable datagram protocol st - st datagram mode tcp - transmission control protocol udp - user datagram protocol vmtp - versatile message transport vrrp - virtual router redundancy protocol xns-idp - xerox xns idp xtp - xpress transfer protocol

MAC Protocol Examples 802.2 - 802.2 Frames (0x0004) arp - Address Resolution Protocol (0x0806) homeplug-av - HomePlug AV MME (0x88E1) ip - Internet Protocol version 4 (0x0800) ipv6 - Internet Protocol Version 6 (0x86DD) ipx - Internetwork Packet Exchange (0x8137) lldp - Link Layer Discovery Protocol (0x88CC) loop-protect - Loop Protect Protocol (0x9003) mpls-multicast - MPLS multicast (0x8848) mpls-unicast - MPLS unicast (0x8847) packing-compr - Encapsulated packets with compressed IP packing (0x9001) packing-simple - Encapsulated packets with simple IP packing (0x9000) pppoe - PPPoE Session Stage (0x8864) pppoe-discovery - PPPoE Discovery Stage (0x8863) rarp - Reverse Address Resolution Protocol (0x8035) service-vlan - Provider Bridging (IEEE 802.1ad) & Shortest Path Bridging IEEE 802.1aq (0x88A8) vlan - VLAN-tagged frame (IEEE 802.1Q) and Shortest Path Bridging IEEE 802.1aq with NNI compatibility (0x8100)

**Data Usage: ** The data set can be used for research in areas such as network security, traffic analysis, and machine learning. Researchers can use the data to develop new algorithms for detecting and preventing cyber attacks, analyzing internet traffic patterns, and more.

**Data Availability: ** If you are interested in using this data set for research purposes, please contact us at asfandyar250@gmail.com for more information and references. The data set is available for download on Kaggle and can be accessed by researchers who have obtained permission from the ISP.

We hope this data set will be useful for researchers in the field of network security and traffic analysis. If you have any questions or need further information, please do not hesitate to contact us. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5985737%2F61c81ce9eb393f8fc7c15540c9819b95%2FData.PNG?generation=1683750473536727&alt=media" alt=""> You can use Wireshark or other software's to view files

Context

The data presented here was obtained in a Kali Machine from University of Cincinnati,Cincinnati,OHIO by carrying out packet captures for 1 hour during the evening on Oct 9th,2023 using Wireshark.This dataset consists of 394137 instances were obtained and stored in a CSV (Comma Separated Values) file.This large dataset could be used utilised for different machine learning applications for instance classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

The dataset can be used for a variety of machine learning tasks, such as network intrusion detection, traffic classification, and anomaly detection.

Content :

This network traffic dataset consists of 7 features.Each instance contains the information of source and destination IP addresses, The majority of the properties are numeric in nature, however there are also nominal and date kinds due to the Timestamp.

The network traffic flow statistics (No. Time Source Destination Protocol Length Info) were obtained using Wireshark (https://www.wireshark.org/).

Dataset Columns:

No : Number of Instance. Timestamp : Timestamp of instance of network traffic Source IP: IP address of Source Destination IP: IP address of Destination Portocol: Protocol used by the instance Length: Length of Instance Info: Information of Traffic Instance

Acknowledgements :

I would like thank University of Cincinnati for giving the infrastructure for generation of network traffic data set.

Ravikumar Gattu , Susmitha Choppadandi

Inspiration : This dataset goes beyond the majority of network traffic classification datasets, which only identify the type of application (WWW, DNS, ICMP,ARP,RARP) that an IP flow contains. Instead, it generates machine learning models that can identify specific applications (like Tiktok,Wikipedia,Instagram,Youtube,Websites,Blogs etc.) from IP flow statistics (there are currently 25 applications in total).

**Dataset License: ** CC0: Public Domain

Dataset Usages : This dataset can be used for different machine learning applications in the field of cybersecurity such as classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

ML techniques benefits from this Dataset :

This dataset is highly useful because it consists of 394137 instances of network traffic data obtained by using the 25 applications on a public,private and Enterprise networks.Also,the dataset consists of very important features that can be used for most of the applications of Machine learning in cybersecurity.Here are few of the potential machine learning applications that could be benefited from this dataset are :

1. Network Performance Monitoring : This large network traffic data set can be utilised for analysing the network traffic to identifying the network patterns in the network .This help in designing the network security algorithms for minimise the network probelms.

2. Anamoly Detection : Large network traffic dataset can be utilised training the machine learning models for finding the irregularitues in the traffic which could help identify the cyber attacks.

3.Network Intrusion Detection : This large dataset could be utilised for machine algorithms training and designing the models for detection of the traffic issues,Malicious traffic network attacks and DOS attacks as well.

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

stackoverflow.com is ranked #572 in US with 83.54M Traffic. Categories: Computer Software and Development, Distance Learning, Information Technology, Online Services. Learn more about website traffic, market share, and more!

Cookie and Website Tracker Scanning Software Market Outlook

The global cookie and website tracker scanning software market is poised for significant growth, with its market size valued at approximately $1.5 billion in 2023 and projected to reach around $4.2 billion by 2032, reflecting a compound annual growth rate (CAGR) of approximately 12.5%. This market's expansion is largely driven by the increasing emphasis on data privacy regulations and compliance, which necessitates businesses to implement robust solutions for monitoring and managing cookies and website trackers. The growing digitalization across various sectors and the rising consumer awareness regarding data privacy are also contributing significantly to the market's upward trajectory.

One of the primary growth factors propelling the cookie and website tracker scanning software market is the proliferation of stringent data privacy regulations worldwide. Laws such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the United States, and other similar legislation globally mandate businesses to enhance their data protection measures. These regulations require organizations to provide transparency regarding data collection practices and ensure that users have control over their personal information. As a result, companies are increasingly adopting cookie and tracker scanning solutions to comply with these legal requirements and avoid potential penalties and reputational damage, thus driving market growth.

Another significant factor contributing to the market's expansion is the escalating awareness and concern among consumers regarding their online privacy. In an era where digital interactions are part and parcel of daily life, consumers are becoming more vigilant about how their data is collected, stored, and utilized by websites. This heightened awareness compels businesses to adopt ethical data practices and implement technologies that offer consumers clear insights into cookie usage and tracking activities. Consequently, organizations are integrating cookie and website tracker scanning software into their operations to enhance user trust and ensure transparency, thereby fostering market growth.

The rapid advancement of technology, leading to increased digitalization, is also a key driver for this market. As businesses across various industries embrace digital transformation, the online ecosystem becomes more complex with an influx of data tracking methods. This complexity necessitates the use of sophisticated tools to monitor, analyze, and manage website trackers effectively. The integration of advanced analytics and AI capabilities into scanning software enables organizations to gain deeper insights into user behavior while ensuring compliance with privacy regulations. This technological evolution is anticipated to further fuel the market's growth over the forecast period.

As the digital landscape continues to evolve, the role of a Consent Management Platform (CMP) becomes increasingly crucial in the realm of data privacy. A CMP serves as a centralized solution for managing user consent across various digital platforms, ensuring that businesses comply with data protection regulations such as GDPR and CCPA. By providing users with clear options to manage their consent preferences, these platforms enhance transparency and trust. Organizations are increasingly integrating CMPs into their operations to streamline consent management processes and reduce the risk of non-compliance. This integration not only helps in maintaining regulatory compliance but also strengthens the relationship between businesses and their users by respecting their privacy choices.

Regionally, North America holds a substantial share in the global cookie and website tracker scanning software market, owing to the early adoption of technology and stringent data privacy regulations in the region. The presence of major technology companies further fuels innovation and development in this market. Europe is also a significant market player, driven by the stringent GDPR regulations that necessitate robust compliance solutions. Meanwhile, the Asia Pacific region is expected to witness the fastest growth rate due to increasing internet penetration, digitalization initiatives, and growing awareness regarding data privacy. As economies in the region continue to develop, the demand for effective data protection solutions is likely to surge, contributing to the market's overall growth.

C

The website visitor tracking tool market is experiencing robust growth, driven by the increasing need for businesses to understand online customer behavior and optimize their digital strategies. The market, estimated at $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. This expansion is fueled by several key factors. Firstly, the rise of e-commerce and the increasing reliance on digital marketing necessitate sophisticated tools for tracking website traffic and analyzing user engagement. Secondly, advancements in analytics capabilities, including AI-powered insights and real-time data visualization, are enhancing the effectiveness of these tools. Finally, the growing adoption of personalized marketing strategies requires detailed visitor data to tailor content and offers to individual customer segments. Key players like Crazy Egg, Mixpanel, and FullStory are capitalizing on these trends through innovative feature development and strategic partnerships. However, challenges remain, including data privacy concerns and the increasing complexity of integrating various tracking tools within existing marketing technology stacks. The market segmentation reveals a strong demand across various business sizes and industries. Small and medium-sized enterprises (SMEs) are increasingly adopting these tools to gain a competitive advantage, while large corporations leverage them for sophisticated customer journey mapping and campaign performance analysis. Geographic distribution shows a high concentration in North America and Europe, driven by early adoption and robust digital infrastructure. However, emerging markets in Asia-Pacific and Latin America represent significant growth opportunities, propelled by rising internet penetration and increasing digitalization. The competitive landscape is dynamic, with established players facing competition from emerging startups offering niche solutions and innovative approaches. The future of this market hinges on ongoing technological advancements, increasing data security measures, and the continued evolution of digital marketing strategies.

Unlock the Power of Behavioural Data with GDPR-Compliant Clickstream Insights.

Swash clickstream data offers a comprehensive and GDPR-compliant dataset sourced from users worldwide, encompassing both desktop and mobile browsing behaviour. Here's an in-depth look at what sets us apart and how our data can benefit your organisation.

User-Centric Approach: Unlike traditional data collection methods, we take a user-centric approach by rewarding users for the data they willingly provide. This unique methodology ensures transparent data collection practices, encourages user participation, and establishes trust between data providers and consumers.

Wide Coverage and Varied Categories: Our clickstream data covers diverse categories, including search, shopping, and URL visits. Whether you are interested in understanding user preferences in e-commerce, analysing search behaviour across different industries, or tracking website visits, our data provides a rich and multi-dimensional view of user activities.

GDPR Compliance and Privacy: We prioritise data privacy and strictly adhere to GDPR guidelines. Our data collection methods are fully compliant, ensuring the protection of user identities and personal information. You can confidently leverage our clickstream data without compromising privacy or facing regulatory challenges.

Market Intelligence and Consumer Behaviuor: Gain deep insights into market intelligence and consumer behaviour using our clickstream data. Understand trends, preferences, and user behaviour patterns by analysing the comprehensive user-level, time-stamped raw or processed data feed. Uncover valuable information about user journeys, search funnels, and paths to purchase to enhance your marketing strategies and drive business growth.

High-Frequency Updates and Consistency: We provide high-frequency updates and consistent user participation, offering both historical data and ongoing daily delivery. This ensures you have access to up-to-date insights and a continuous data feed for comprehensive analysis. Our reliable and consistent data empowers you to make accurate and timely decisions.

Custom Reporting and Analysis: We understand that every organisation has unique requirements. That's why we offer customisable reporting options, allowing you to tailor the analysis and reporting of clickstream data to your specific needs. Whether you need detailed metrics, visualisations, or in-depth analytics, we provide the flexibility to meet your reporting requirements.

Data Quality and Credibility: We take data quality seriously. Our data sourcing practices are designed to ensure responsible and reliable data collection. We implement rigorous data cleaning, validation, and verification processes, guaranteeing the accuracy and reliability of our clickstream data. You can confidently rely on our data to drive your decision-making processes.

This graph shows the tools used by French companies to analyze Big Data in 2016. The results show that almost ** percent of the companies surveyed used Online Analytical Processing engines.

Mobile accounts for approximately half of web traffic worldwide. In the last quarter of 2024, mobile devices (excluding tablets) generated 62.54 percent of global website traffic. Mobiles and smartphones consistently hoovered around the 50 percent mark since the beginning of 2017, before surpassing it in 2020. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.

nasa.gov is ranked #1023 in US with 23.26M Traffic. Categories: . Learn more about website traffic, market share, and more!

Here are a few use cases for this project:

1. Brand Analysis: Marketing teams can use LA-LDN to analyze the presence and visibility of specific brands in public spaces, events, or social media posts. This information can help businesses understand the success of advertising campaigns, consumer trends, and brand recognition.

2. Counterfeit Detection: Retailers, designers, and manufacturers can use LA-LDN to detect counterfeit products by identifying inconsistencies or discrepancies in logos on clothing, accessories, and other items. Reducing counterfeits can help protect brand integrity and customer experience.

3. Sponsorship Measurement: Companies and event organizers can use LA-LDN to measure the impact of sponsorship deals by analyzing the visibility and frequency of sponsored logos in event photos, videos, or online media coverage. This can help them evaluate the return on investment for sponsorships and make data-driven decisions for future partnerships.

4. Customer Behavior Insights: By analyzing customer-generated content (such as social media posts), businesses can gain insights into customer behavior and preferences, such as favorite brands, brand associations, and purchase motivations. This information can guide marketing strategies and product development.

5. Logo Redesign Evaluation: Companies planning to update or redesign their logo can use LA-LDN to compare the performance of the updated logo against the old one in terms of visibility and recognition in real-world scenarios, like in-store displays, billboards, or website traffic. This can help them determine the effectiveness of the redesign and gather feedback for further refinements.

In March 2025, ChatGPT.com received approximately *** billion visits from users worldwide. The most recent year under analysis has seen an increase in traffic to OpenAI's artificial intelligence chatbot. This is the highest traffic volume achieved by the site to date, with values for the most recent analyzed month exceeding twice the average monthly visits for the entire examined period between April 2023 and April 2024.

The Reddit Subreddit Dataset by Dataplex offers a comprehensive and detailed view of Reddit’s vast ecosystem, now enhanced with appended AI-generated columns that provide additional insights and categorization. This dataset includes data from over 2.1 million subreddits, making it an invaluable resource for a wide range of analytical applications, from social media analysis to market research.

Dataset Overview:

This dataset includes detailed information on subreddit activities, user interactions, post frequency, comment data, and more. The inclusion of AI-generated columns adds an extra layer of analysis, offering sentiment analysis, topic categorization, and predictive insights that help users better understand the dynamics of each subreddit.

2.1 Million Subreddits with Enhanced AI Insights: The dataset covers over 2.1 million subreddits and now includes AI-enhanced columns that provide: - Sentiment Analysis: AI-driven sentiment scores for posts and comments, allowing users to gauge community mood and reactions. - Topic Categorization: Automated categorization of subreddit content into relevant topics, making it easier to filter and analyze specific types of discussions. - Predictive Insights: AI models that predict trends, content virality, and user engagement, helping users anticipate future developments within subreddits.

Sourced Directly from Reddit:

All social media data in this dataset is sourced directly from Reddit, ensuring accuracy and authenticity. The dataset is updated regularly, reflecting the latest trends and user interactions on the platform. This ensures that users have access to the most current and relevant data for their analyses.

Key Features:

• Subreddit Metrics: Detailed data on subreddit activity, including the number of posts, comments, votes, and user participation.
• User Engagement: Insights into how users interact with content, including comment threads, upvotes/downvotes, and participation rates.
• Trending Topics: Track emerging trends and viral content across the platform, helping you stay ahead of the curve in understanding social media dynamics.
• AI-Enhanced Analysis: Utilize AI-generated columns for sentiment analysis, topic categorization, and predictive insights, providing a deeper understanding of the data.

Use Cases:

• Social Media Analysis: Researchers and analysts can use this dataset to study online behavior, track the spread of information, and understand how content resonates with different audiences.
• Market Research: Marketers can leverage the dataset to identify target audiences, understand consumer preferences, and tailor campaigns to specific communities.
• Content Strategy: Content creators and strategists can use insights from the dataset to craft content that aligns with trending topics and user interests, maximizing engagement.
• Academic Research: Academics can explore the dynamics of online communities, studying everything from the spread of misinformation to the formation of online subcultures.

Data Quality and Reliability:

The Reddit Subreddit Dataset emphasizes data quality and reliability. Each record is carefully compiled from Reddit’s vast database, ensuring that the information is both accurate and up-to-date. The AI-generated columns further enhance the dataset's value, providing automated insights that help users quickly identify key trends and sentiments.

Integration and Usability:

The dataset is provided in a format that is compatible with most data analysis tools and platforms, making it easy to integrate into existing workflows. Users can quickly import, analyze, and utilize the data for various applications, from market research to academic studies.

User-Friendly Structure and Metadata:

The data is organized for easy navigation and analysis, with metadata files included to help users identify relevant subreddits and data points. The AI-enhanced columns are clearly labeled and structured, allowing users to efficiently incorporate these insights into their analyses.

Ideal For:

• Data Analysts: Conduct in-depth analyses of subreddit trends, user engagement, and content virality. The dataset’s extensive coverage and AI-enhanced insights make it an invaluable tool for data-driven research.
• Marketers: Use the dataset to better understand your target audience, tailor campaigns to specific interests, and track the effectiveness of marketing efforts across Reddit.
• Researchers: Explore the social dynamics of online communities, analyze the spread of ideas and information, and study the impact of digital media on public discourse, all while leveraging AI-generated insights.

This dataset is an essential resource for anyone looking to understand the intricacies of Reddit's vast ecosystem, offering the data and AI-enhanced insights needed to drive informed decisions and strategies across various fields. Whether you’re tracking emerging trends, analyzing user behavior, or conduc...

PredictLeads Key Customers Data provides essential business intelligence by analyzing company relationships, uncovering vendor partnerships, client connections, and strategic affiliations through advanced web scraping and logo recognition. This dataset captures business interactions directly from company websites, offering valuable insights into market positioning, competitive landscapes, and growth opportunities.

Use Cases:

✅ Account Profiling – Gain a 360-degree customer view by mapping company relationships and partnerships. ✅ Competitive Intelligence – Track vendor-client connections and business affiliations to identify key industry players. ✅ B2B Lead Targeting – Prioritize leads based on their business relationships, improving sales and marketing efficiency. ✅ CRM Data Enrichment – Enhance company records with detailed key customer data, ensuring data accuracy. ✅ Market Research – Identify emerging trends and industry networks to optimize strategic planning.

Key API Attributes:

• id (string, UUID) – Unique identifier for the company connection.
• category (string) – Type of relationship (e.g., vendor, client, partner).
• source_category (string) – Where the connection was detected (e.g., partner page, case study).
• source_url (string, URL) – Website where the relationship was found.
• individual_source_url (string, URL) – Specific page confirming the connection.
• context (string) – Extracted description of the business relationship (e.g., "Company X - partners with Company Y to enhance payment processing").
• first_seen_at (ISO 8601 date-time) – Date the connection was first detected.
• last_seen_at (ISO 8601 date-time) – Most recent confirmation of the relationship.
• company1 & company2 (objects) – Details of the two connected companies, including:
• - domain (string) – Company website domain.
• - company_name (string) – Official company name.
• - ticker (string, nullable) – Stock ticker, if available.

📌 PredictLeads Key Customers Data is an indispensable tool for B2B sales, marketing, and market intelligence teams, providing actionable relationship insights to drive targeted outreach, competitor tracking, and strategic decision-making.

PredictLeads Docs: https://docs.predictleads.com/v3/guide/connections_dataset

The "Phishing Data" dataset is a comprehensive collection of information specifically curated for analyzing and understanding phishing attacks. Phishing attacks involve malicious attempts to deceive individuals or organizations into disclosing sensitive information such as passwords or credit card details. This dataset comprises 18 distinct features that offer valuable insights into the characteristics of phishing attempts. These features include the URL of the website being analyzed, the length of the URL, the use of URL shortening services, the presence of the "@" symbol, the presence of redirection using "//", the presence of prefixes or suffixes in the URL, the number of subdomains, the usage of secure connection protocols (HTTPS), the length of time since domain registration, the presence of a favicon, the presence of HTTP or HTTPS tokens in the domain name, the URL of requested external resources, the presence of anchors in the URL, the number of hyperlinks in HTML tags, the server form handler used, the submission of data to email addresses, abnormal URL patterns, and estimated website traffic or popularity. Together, these features enable the analysis and detection of phishing attempts in the "Phishing Data" dataset, aiding in the development of models and algorithms to combat phishing attacks.