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.

Explore our detailed website traffic dataset featuring key metrics like page views, session duration, bounce rate, traffic source, and conversion rates.

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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.

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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.

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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...

Code:

Packet_Features_Generator.py & Features.py

To run this code:

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

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

Purpose:

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

Uses Features.py to calcualte the features.

startMachineLearning.sh & machineLearning.py

To run this code:

bash startMachineLearning.sh

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

Options (to be edited within this file):

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

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

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

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

Purpose:

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

Data

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

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

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

The remaining numbers in each line denote:

The size of a packet,

and the direction it is traveling.

negative numbers denote incoming packets

positive numbers denote outgoing packets

As of the last quarter of 2023, 31.57 percent of web traffic in the United States originated from mobile devices, down from 49.51 percent in the fourth quarter of 2022. In comparison, over half of web traffic worldwide was generated via mobile in the last examined period.

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.

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.

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.

According to the results of a survey conducted worldwide in 2023, nearly half of responding digital marketers believed artificial intelligence (AI) would have a positive impact on website search traffic in the next five years. Some 20 percent stated AI would have a neutral effect, while 30 percent agreed that the technology would negatively impact search traffic.

The Website Traffic Generator market is projected to reach USD 2,844.3 million by 2033, expanding at a CAGR of 12.1% from 2023 to 2033. Rising demand for online traffic, increasing digital marketing investments, and the advent of new technologies are major factors driving market growth. The increasing adoption of social media platforms and the growing popularity of mobile internet usage further contribute to the demand for website traffic generators. The market is segmented by type into Referral Traffic Generators, Social Media Traffic Generators, Direct Traffic Generators, and Others. Referral Traffic Generators currently dominate the market, accounting for a significant share of the total revenue. However, Social Media Traffic Generators are expected to witness the fastest growth over the forecast period due to the growing popularity of social media platforms as a source of website traffic. The market is also segmented by application into Individual and Enterprise, with the Enterprise segment holding a larger market share. Key players in the market include Babylon Traffic, SparkTraffic, Getthit, TrafficApe, Somiibo, Serp Empire, EasyHits4U, Growtraffic, 10KHits, Traffup, Torpedo Traffic, YOOtraffic, SigmaTraffic, TheTraffic, WebTrafficly, Traffic Creator, and Apex Traffic.

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?

In terms of website traffic, Shein ranked 123rd globally in May 2024, securing the leading spot in the fashion apparel category. Direct traffic accounted for 58.6 percent of its web traffic, indicating the Chinese fast-fashion conglomerate's growing brand awareness among global consumers. Nonetheless, the traffic share of organic searches remained modest at 17.7 percent, reflecting the necessity of enhancing keyword optimization.

Traffic Analysis Zones (TAZ) for the COG/TPB Modeled Region from Metropolitan Washington Council of Governments. The TAZ dataset is used to join several types of zone-based transportation modeling data. For more information, visit https://plandc.dc.gov/page/traffic-analysis-zone.

The global website visitor tracking software 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 expand at 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, including the rising adoption of digital marketing strategies, the growing importance of data-driven decision-making, and the increasing sophistication of website visitor tracking tools. Cloud-based solutions dominate the market due to their scalability, accessibility, and cost-effectiveness, particularly appealing to Small and Medium-sized Enterprises (SMEs). However, large enterprises continue to invest significantly in on-premise solutions for enhanced data security and control. The market is highly competitive, with numerous established players and emerging startups offering a range of features and functionalities. Technological advancements, such as AI-powered analytics and enhanced integration with other marketing tools, are shaping the future of the market. The market's geographical distribution reflects the global digital landscape. North America, with its mature digital economy and high adoption rates, holds a significant market share. However, regions like Asia-Pacific are showing rapid growth, driven by increasing internet penetration and digitalization across various industries. Despite the overall positive outlook, challenges such as data privacy regulations and the increasing complexity of website tracking technology are influencing market dynamics. The ongoing competition among vendors necessitates continuous innovation and the development of more user-friendly and insightful tools. The future growth of the website visitor tracking software market is promising, fueled by the continuing importance of data-driven decision-making within marketing and business strategies. A key factor will be the ongoing adaptation to evolving privacy regulations and user expectations.

Desktop and mobile website traffic data showed that Germany domain of Zalando had by far the highest number of visitors compared to all other European countries. Between July 2023 and December 2023, zalando.de recorded more nearly 203 million visits. The Polish web domain followed in the ranking, as the total visits amounted to 99.2 million.

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

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).

Click is global web traffic data combined with transaction data to enhance signal capabilities. Click adds a layer to your analysis for richer spending behavior on consumer & B2B websites, addressable market overview, trends and shopping behavior shifts, and effects of pricing on growth.

This is a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from HERE (www.HERE.com). HERE collects billions of GPS and cell phone probe records per month and, where available, uses sensor and toll-tag data to augment the probe data collected. An advanced algorithm compiles the data and computes accurate speeds. Historical traffic is based on the average of observed speeds over the past three years. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map image can be requested for the current time and any time in the future. A map image for a future request might be used for planning purposes. The map layer also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

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