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.

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?

This dataset provides the Austin Google Analytic. Google Analytics is a freemium web analytics service offered by Google that tracks and reports website traffic.

Experience the power of Serpstat's Website Data API, offering comprehensive insights into key SEO metrics, organic traffic estimation, and semantical structure analysis. With our API, unlock valuable data to identify top-performing industry players, analyze competitor strategies, and optimize content for maximum impact.

Unveil essential SEO metrics such as website visibility, organic traffic estimation, and keywords that websites rank for organically. Additionally, gain insights into keywords that websites are shown in Google Ads, providing valuable intelligence for ad targeting and optimization.

Utilize our API to uncover top-performing content in any industry, enabling you to identify trends, understand user intent, and refine your content strategy accordingly.

Benefit from the most cost-effective API solution, starting at just $120 per million rows. Tailor your queries to extract the most relevant data for your specific use-cases, ensuring that you're equipped with the insights necessary to thrive in today's competitive digital landscape.

Preliminary research efforts regarding Social Media Platforms and their contribution to website traffic in LAMs. Through the Similar Web API, the leading social networks (Facebook, Twitter, Youtube, Instagram, Reddit, Pinterest, LinkedIn) that drove traffic to each one of the 220 cases in our dataset were identified and analyzed in the first sheet. Aggregated results proved that Facebook platform was responsible for 46.1% of social traffic (second sheet).

The global website analytics market, encompassing solutions for large enterprises and SMEs, is poised for significant growth. While the provided data lacks specific market size and CAGR figures, a reasonable estimation based on industry trends suggests a 2025 market size of approximately $15 billion, experiencing a compound annual growth rate (CAGR) of 12% from 2025 to 2033. This robust growth is fueled by several key drivers: the increasing reliance on data-driven decision-making across businesses, the escalating need for enhanced website performance optimization, and the growing adoption of sophisticated analytics tools offering deeper insights into user behavior and conversion rates. Market segmentation reveals strong demand across diverse analytics types, including product, traffic, and sales analytics. The competitive landscape is intensely dynamic, with established players like Google, SEMrush, and SimilarWeb vying for market share alongside emerging innovative companies like Owletter and TrendSource. These companies are constantly innovating to provide more comprehensive and user-friendly analytics platforms, leading to increased competition. This competitive pressure fosters innovation, but also necessitates strategic differentiation, focusing on specific niche markets or offering unique features to attract and retain customers. The market’s geographic distribution shows significant traction in North America and Europe, but emerging markets in Asia Pacific are also exhibiting substantial growth potential, driven by increasing internet penetration and digital transformation initiatives. While data security concerns and the complexity of implementing analytics tools present some restraints, the overall market outlook remains highly positive, promising considerable opportunities for market participants in the coming years.

The FDOT Telemetered Traffic Monitoring Site (TTMS) feature class provides information on Florida Telemetered Traffic Monitoring Site locations, as well affiliated information like KFCTR and TFCTR from the FDOT Traffic Characteristics Inventory database. This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 02/08/2025.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/DOTShapesFGDB.zip

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

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.

Competitive Analysis of Industry Rivals The market for competitive analysis is expected to grow significantly over the forecast period, driven by increasing need for businesses to understand their competitive landscape. Key players in the market include BuiltWith, WooRank, SEMrush, Google, SpyFu, Owletter, SimilarWeb, Moz, SunTec Data, and TrendSource. These companies offer a range of services to help businesses track their competitors' online performance, including website traffic, social media engagement, and search engine rankings. Some of the key trends driving the growth of the market include the increasing adoption of digital marketing by businesses, the growing importance of social media, and the increasing availability of data and analytics tools. The market is segmented by type, application, and region. In terms of type, the market is divided into product analysis, traffic analytics, sales analytics, and others. In terms of application, the market is divided into SMEs and large enterprises. In terms of region, the market is divided into North America, South America, Europe, Middle East & Africa, and Asia Pacific. The North American region is expected to dominate the market during the forecast period, due to the presence of a large number of established players in the market. The Asia Pacific region is expected to grow at the highest CAGR during the forecast period, due to the increasing adoption of digital marketing by businesses in the region. This report provides a comprehensive analysis of the industry rivals, encompassing their concentration, product insights, regional trends, and key industry developments.

The global Clickstream Analytics Market was valued at $615.37 Million in 2022, and is projected to $1,298.63 Million by 2030, growing at a CAGR of 11.26%.

The size and share of the market is categorized based on Type (Web Traffic Analytics, Conversion Analytics, User Behavior Analytics, SEO Analytics) and Application (Website Optimization, Marketing Performance, User Experience, Sales Tracking) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

The FDOT Portable Traffic Monitoring Site (PTMS) feature class provides information on Florida Portable Traffic Monitoring Site locations, as well affiliated information like KFCTR and TFCTR from the FDOT Traffic Characteristics Inventory database. This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 02/08/2025.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/ptms.zip

Web Analytics Market Valuation – 2024-2031

Web Analytics Market was valued at USD 6.16 Billion in 2024 and is projected to reach USD 13.6 Billion by 2031, growing at a CAGR of 18.58% from 2024 to 2031.

Web Analytics Market Drivers

Data-Driven Decision Making: Businesses increasingly rely on data-driven insights to optimize their online strategies. Web analytics provides valuable data on website traffic, user behavior, and conversion rates, enabling data-driven decision-making.

E-commerce Growth: The rapid growth of e-commerce has fueled the demand for web analytics tools to track online sales, customer behavior, and marketing campaign effectiveness.

Mobile Dominance: The increasing use of mobile devices for internet browsing has made mobile analytics a crucial aspect of web analytics. Businesses need to understand how users interact with their websites and apps on mobile devices.

Web Analytics Market Restraints

Data Privacy and Security Concerns: As data privacy regulations become stricter, businesses must ensure that they collect and process user data ethically and securely.

Complex Web Analytics Tools: Some web analytics tools can be complex to implement and use, requiring technical expertise.

Please refer to the original data article for further data description: Jan Luxemburk et al. CESNET-QUIC22: A large one-month QUIC network traffic dataset from backbone lines, Data in Brief, 2023, 108888, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2023.108888. We recommend using the CESNET DataZoo python library, which facilitates the work with large network traffic datasets. More information about the DataZoo project can be found in the GitHub repository https://github.com/CESNET/cesnet-datazoo. The QUIC (Quick UDP Internet Connection) protocol has the potential to replace TLS over TCP, which is the standard choice for reliable and secure Internet communication. Due to its design that makes the inspection of QUIC handshakes challenging and its usage in HTTP/3, there is an increasing demand for research in QUIC traffic analysis. This dataset contains one month of QUIC traffic collected in an ISP backbone network, which connects 500 large institutions and serves around half a million people. The data are delivered as enriched flows that can be useful for various network monitoring tasks. The provided server names and packet-level information allow research in the encrypted traffic classification area. Moreover, included QUIC versions and user agents (smartphone, web browser, and operating system identifiers) provide information for large-scale QUIC deployment studies. Data capture The data was captured in the flow monitoring infrastructure of the CESNET2 network. The capturing was done for four weeks between 31.10.2022 and 27.11.2022. The following list provides per-week flow count, capture period, and uncompressed size:

W-2022-44

Uncompressed Size: 19 GB Capture Period: 31.10.2022 - 6.11.2022 Number of flows: 32.6M W-2022-45

Uncompressed Size: 25 GB Capture Period: 7.11.2022 - 13.11.2022 Number of flows: 42.6M W-2022-46

Uncompressed Size: 20 GB Capture Period: 14.11.2022 - 20.11.2022 Number of flows: 33.7M W-2022-47

Uncompressed Size: 25 GB Capture Period: 21.11.2022 - 27.11.2022 Number of flows: 44.1M CESNET-QUIC22

Uncompressed Size: 89 GB Capture Period: 31.10.2022 - 27.11.2022 Number of flows: 153M

Data description The dataset consists of network flows describing encrypted QUIC communications. Flows were created using ipfixprobe flow exporter and are extended with packet metadata sequences, packet histograms, and with fields extracted from the QUIC Initial Packet, which is the first packet of the QUIC connection handshake. The extracted handshake fields are the Server Name Indication (SNI) domain, the used version of the QUIC protocol, and the user agent string that is available in a subset of QUIC communications. Packet Sequences Flows in the dataset are extended with sequences of packet sizes, directions, and inter-packet times. For the packet sizes, we consider payload size after transport headers (UDP headers for the QUIC case). Packet directions are encoded as ±1, +1 meaning a packet sent from client to server, and -1 a packet from server to client. Inter-packet times depend on the location of communicating hosts, their distance, and on the network conditions on the path. However, it is still possible to extract relevant information that correlates with user interactions and, for example, with the time required for an API/server/database to process the received data and generate the response to be sent in the next packet. Packet metadata sequences have a length of 30, which is the default setting of the used flow exporter. We also derive three fields from each packet sequence: its length, time duration, and the number of roundtrips. The roundtrips are counted as the number of changes in the communication direction (from packet directions data); in other words, each client request and server response pair counts as one roundtrip. Flow statistics Flows also include standard flow statistics, which represent aggregated information about the entire bidirectional flow. The fields are: the number of transmitted bytes and packets in both directions, the duration of flow, and packet histograms. Packet histograms include binned counts of packet sizes and inter-packet times of the entire flow in both directions (more information in the PHISTS plugin documentation There are eight bins with a logarithmic scale; the intervals are 0-15, 16-31, 32-63, 64-127, 128-255, 256-511, 512-1024, >1024 [ms or B]. The units are milliseconds for inter-packet times and bytes for packet sizes. Moreover, each flow has its end reason - either it was idle, reached the active timeout, or ended due to other reasons. This corresponds with the official IANA IPFIX-specified values. The FLOW_ENDREASON_OTHER field represents the forced end and lack of resources reasons. The end of flow detected reason is not considered because it is not relevant for UDP connections. Dataset structure The dataset flows are delivered in compressed CSV files. CSV files contain one flow per row; data columns are summarized in the provided list below. For each flow data file, there is a JSON file with the number of saved and seen (before sampling) flows per service and total counts of all received (observed on the CESNET2 network), service (belonging to one of the dataset's services), and saved (provided in the dataset) flows. There is also the stats-week.json file aggregating flow counts of a whole week and the stats-dataset.json file aggregating flow counts for the entire dataset. Flow counts before sampling can be used to compute sampling ratios of individual services and to resample the dataset back to the original service distribution. Moreover, various dataset statistics, such as feature distributions and value counts of QUIC versions and user agents, are provided in the dataset-statistics folder. The mapping between services and service providers is provided in the servicemap.csv file, which also includes SNI domains used for ground truth labeling. The following list describes flow data fields in CSV files:

ID: Unique identifier SRC_IP: Source IP address DST_IP: Destination IP address DST_ASN: Destination Autonomous System number SRC_PORT: Source port DST_PORT: Destination port PROTOCOL: Transport protocol QUIC_VERSION QUIC: protocol version QUIC_SNI: Server Name Indication domain QUIC_USER_AGENT: User agent string, if available in the QUIC Initial Packet TIME_FIRST: Timestamp of the first packet in format YYYY-MM-DDTHH-MM-SS.ffffff TIME_LAST: Timestamp of the last packet in format YYYY-MM-DDTHH-MM-SS.ffffff DURATION: Duration of the flow in seconds BYTES: Number of transmitted bytes from client to server BYTES_REV: Number of transmitted bytes from server to client PACKETS: Number of packets transmitted from client to server PACKETS_REV: Number of packets transmitted from server to client PPI: Packet metadata sequence in the format: [[inter-packet times], [packet directions], [packet sizes]] PPI_LEN: Number of packets in the PPI sequence PPI_DURATION: Duration of the PPI sequence in seconds PPI_ROUNDTRIPS: Number of roundtrips in the PPI sequence PHIST_SRC_SIZES: Histogram of packet sizes from client to server PHIST_DST_SIZES: Histogram of packet sizes from server to client PHIST_SRC_IPT: Histogram of inter-packet times from client to server PHIST_DST_IPT: Histogram of inter-packet times from server to client APP: Web service label CATEGORY: Service category FLOW_ENDREASON_IDLE: Flow was terminated because it was idle FLOW_ENDREASON_ACTIVE: Flow was terminated because it reached the active timeout FLOW_ENDREASON_OTHER: Flow was terminated for other reasons

Link to other CESNET datasets

https://www.liberouter.org/technology-v2/tools-services-datasets/datasets/ https://github.com/CESNET/cesnet-datazoo Please cite the original data article:

@article{CESNETQUIC22, author = {Jan Luxemburk and Karel Hynek and Tomáš Čejka and Andrej Lukačovič and Pavel Šiška}, title = {CESNET-QUIC22: a large one-month QUIC network traffic dataset from backbone lines}, journal = {Data in Brief}, pages = {108888}, year = {2023}, issn = {2352-3409}, doi = {https://doi.org/10.1016/j.dib.2023.108888}, url = {https://www.sciencedirect.com/science/article/pii/S2352340923000069} }

The traffic_tmscnt feature class shows the location of traffic monitoring sites maintained by the Florida Department of Transportation, Transportation Data and Analytics office's Traffic data section. The sites have daily hourly traffic count data by direction for the most recent six months. This feature class is updated daily using event mapping against the FDOT TDA linear referencing system (LRS). The feature class also contains information about total volume, managing district, and county location. This dataset is maintained by the Transportation Data & Analytics office (TDA). This hosted feature layer was updated on: 02-18-2025 06:00:05.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/traffic_tmscnt.zip

E-commerce Analytics Software Market size was valued at USD 15.4 Billion in 2024 and is projected to reach USD 17.24 Billion by 2031, growing at a CAGR of 19.7 % during the forecast period 2024-2031.

Global E-commerce Analytics Software Market Drivers

Fast Growth of the E-Commerce Sector: Over the past ten years, the global e-commerce sector has grown at an exponential rate due to reasons like rising internet penetration, smartphone use, and shifting consumer tastes. Robust analytics solutions are becoming more and more necessary as more organisations go online in order to better analyse customer behaviour, streamline processes, and increase sales.

Demand for Actionable Insights: Businesses are using analytics software more and more in the fiercely competitive e-commerce sector to obtain actionable insights into a range of business-related topics, such as customer demographics, purchasing trends, website traffic, and marketing efficacy. By using these insights, organisations may improve the overall customer experience, tailor marketing campaigns, and make well-informed decisions.

Emphasis on Customer Experience: Businesses are placing a higher priority on using analytics software to better understand and accommodate customer requirements and preferences since it is becoming a crucial differentiator in the e-commerce sector. Through the examination of consumer contact, feedback, and satisfaction data, businesses can pinpoint opportunities for enhancement and modify their products to align with changing demands.

Technological Developments: The progress of ecommerce analytics software is being driven by the ongoing technological developments, especially in fields like big data analytics, artificial intelligence (AI), and machine learning (ML). Businesses can now process massive amounts of data in real-time, identify intricate patterns and trends, and produce predictive insights that can guide strategic decision-making thanks to these technologies.

Growing Significance of Omnichannel Retailing: Companies are using omnichannel retailing tactics more and more as a result of the expansion of various sales channels, such as websites, mobile apps, social media platforms, and physical stores. Consolidating data from these various channels, offering a comprehensive picture of customer behaviour across touchpoints, and facilitating smooth integration and optimisation of the complete sales ecosystem are all made possible by ecommerce analytics software.

Emphasis on Cost Efficiency and ROI: Businesses are giving top priority to solutions that provide measurable returns on investment (ROI) and aid in optimising operating costs in a time of constrained budgets and heightened scrutiny of spending. Ecommerce analytics software is seen as a crucial tool for increasing profitability and efficiency because it helps companies find inefficiencies, optimise marketing budgets, and generate more income.

Regulatory Compliance and Data Security Issues: Businesses are facing more and more pressure to maintain compliance and safeguard customer data as a result of the introduction of data privacy laws like the California Consumer Privacy Act (CCPA) and the General Data Protection Regulation (GDPR). In response to these worries, ecommerce analytics software companies are strengthening data security protocols, putting in place strong compliance frameworks, and providing capabilities like anonymization and encryption to protect sensitive data.

At Echo, our dedication to data curation is unmatched; we focus on providing our clients with an in-depth picture of a physical location based on activity in and around the point of interest (POI) over time. Our dataset empowers you to explore the cross-shopping patterns from your visitors by allowing you to dig deeper into consumer profiles, eliminate gaps in your trade area and discover untapped sites of action.

This sample of our Market Analysis solution helps you determine the geographical reach of your store or facility based on the brands or categories most visited by consumers who visit your specific POI. This empowers your location strategy. This particular dataset is for Europe.

Additional Information:

• Understand the actual movement patterns of consumers without using PII data, gaining a 360-degree consumer view. Complement your online behavior knowledge with actual offline actions, and better attribute intent based on real-world behaviors.
• Echo collects, cleans and updates its footfall on a daily basis. Normalization of the data occurs on a monthly basis.
• We provide data aggregation on a weekly, monthly and quarterly basis.

• Information about our country offering and data schema can be found here:

  1) Data Schema: https://docs.echo-analytics.com/activity/data-schema 2) Country Availability: https://docs.echo-analytics.com/activity/country-coverage 3) Methodology: https://docs.echo-analytics.com/activity/methodology

  Echo's commitment to customer service is evident in our exceptional data quality and dedicated team, providing 360° support throughout your location intelligence journey. We handle the complex tasks to deliver analysis-ready datasets to you.

Business Needs: - Site Selection and Lease Renegotiation: Leverage foot traffic data for optimal site selection and advantageous lease renegotiations. This approach enables you to pinpoint ideal store locations and secure lease terms that align with business objectives, optimizing operational efficiency and cost-effectiveness.

-Market Intelligence: Outsmart your competition by understanding competitor foot traffic trends, allowing you to identify growth opportunities and gain a competitive advantage. Analyze regional consumer behaviors and preferences to pinpoint new markets and assess the competitive landscape for strategic expansion.

Traffic statistics for the For government franchise on the Queensland Government website. Source: Google Analytics.

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.