Websites in the energy, utilities, and construction sector averaged the largest amount of visits per online session worldwide. In the fourth quarter of 2022, desktop users in that segment visited around seven pages per online session. Travel and hospitality ranked second, with an average of almost six pages visited. In terms of mobile users, travel and hospitality registered the highest number of page views, followed by retail.

In March 2023, it was found that, among selected fantasy sports websites in the United States, fanduel.com had the highest average number of pages viewed by visitor, with an average of 8.89 pages visited. Meanwhile, nbcsportsedge.com had an average of three pages viewed per visit.

Data dictionary: Page_Title: Title of webpage used for pages of the website www.cityofrochester.gov Pageviews: Total number of pages viewed over the course of the calendar year listed in the year column. Repeated views of a single page are counted. Unique_Pageviews: Unique Pageviews - The number of sessions during which a specified page was viewed at least once. A unique pageview is counted for each URL and page title combination. Avg_Time: Average amount of time users spent looking at a specified page or screen. Entrances: The number of times visitors entered the website through a specified page.Bounce_Rate: " A bounce is a single-page session on your site. In Google Analytics, a bounce is calculated specifically as a session that triggers only a single request to the Google Analytics server, such as when a user opens a single page on your site and then exits without triggering any other requests to the Google Analytics server during that session. Bounce rate is single-page sessions on a page divided by all sessions that started with that page, or the percentage of all sessions on your site in which users viewed only a single page and triggered only a single request to the Google Analytics server. These single-page sessions have a session duration of 0 seconds since there are no subsequent hits after the first one that would let Google Analytics calculate the length of the session. "Exit_Rate: The number of exits from a page divided by the number of pageviews for the page. This is inclusive of sessions that started on different pages, as well as “bounce” sessions that start and end on the same page. For all pageviews to the page, Exit Rate is the percentage that were the last in the session. Year: Calendar year over which the data was collected. Data reflects the counts for each metric from January 1st through December 31st.

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.

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?

Analysis of ‘Top 50 Pages By Pageviews on Austintexas.gov -’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/3c834400-d28d-4a2f-b8ba-fc2b6d9f2490 on 26 January 2022.

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

This data, exported from Google Analytics displays the most popular 50 pages on Austintexas.gov based on the following: Pageviews: The total number of times the page was viewed. Repeated views of a single page are counted. Unique Pageviews: The number of visits during which the specified page was viewed at least once. A unique pageview is counted for each page URL + page Title combination. Average Time on Page: The average amount of time users spent viewing a specified page or screen, or set of pages or screens. Entrances: The number of times visitors entered your site through a specified page or set of pages. Bounce Rate: The percentage of single-page visits (i.e. visits in which the person left your site from the entrance page without interacting with the page). Percent Exit: (number of exits) / (number of pageviews) for the page or set of pages. It indicates how often users exit from that page or set of pages when they view the page(s). This demonstrates the top 50 pages for a three-month period.

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

In November 2024, Google.com was the most popular website worldwide with 136 billion average monthly visits. The online platform has held the top spot as the most popular website since June 2010, when it pulled ahead of Yahoo into first place. Second-ranked YouTube generated more than 72.8 billion monthly visits in the measured period. The internet leaders: search, social, and e-commerce Social networks, search engines, and e-commerce websites shape the online experience as we know it. While Google leads the global online search market by far, YouTube and Facebook have become the world’s most popular websites for user generated content, solidifying Alphabet’s and Meta’s leadership over the online landscape. Meanwhile, websites such as Amazon and eBay generate millions in profits from the sale and distribution of goods, making the e-market sector an integral part of the global retail scene. What is next for online content? Powering social media and websites like Reddit and Wikipedia, user-generated content keeps moving the internet’s engines. However, the rise of generative artificial intelligence will bring significant changes to how online content is produced and handled. ChatGPT is already transforming how online search is performed, and news of Google's 2024 deal for licensing Reddit content to train large language models (LLMs) signal that the internet is likely to go through a new revolution. While AI's impact on the online market might bring both opportunities and challenges, effective content management will remain crucial for profitability on the web.

This statistic shows the number of page views per visit on leading European online travel agency websites in 2015. According to the report Skyscanner.net had an average of 4.53 page views per visit in May 2015, whereas on Tripadvisor.co.uk they amounted to 3.93 views per visit.

This data, exported from Google Analytics displays the most popular 50 pages on Austintexas.gov based on the following: Pageviews: The total number of times the page was viewed. Repeated views of a single page are counted. Unique Pageviews: The number of visits during which the specified page was viewed at least once. A unique pageview is counted for each page URL + page Title combination. Average Time on Page: The average amount of time users spent viewing a specified page or screen, or set of pages or screens. Entrances: The number of times visitors entered your site through a specified page or set of pages. Bounce Rate: The percentage of single-page visits (i.e. visits in which the person left your site from the entrance page without interacting with the page). Percent Exit: (number of exits) / (number of pageviews) for the page or set of pages. It indicates how often users exit from that page or set of pages when they view the page(s). This demonstrates the top 50 pages for a three-month period.

Host country of organization for 86 websites in study.

Author: Víctor Yeste. Universitat Politècnica de Valencia.The object of this study is the design of a cybermetric methodology whose objectives are to measure the success of the content published in online media and the possible prediction of the selected success variables.In this case, due to the need to integrate data from two separate areas, such as web publishing and the analysis of their shares and related topics on Twitter, has opted for programming as you access both the Google Analytics v4 reporting API and Twitter Standard API, always respecting the limits of these.The website analyzed is hellofriki.com. It is an online media whose primary intention is to solve the need for information on some topics that provide daily a vast number of news in the form of news, as well as the possibility of analysis, reports, interviews, and many other information formats. All these contents are under the scope of the sections of cinema, series, video games, literature, and comics.This dataset has contributed to the elaboration of the PhD Thesis:Yeste Moreno, VM. (2021). Diseño de una metodología cibermétrica de cálculo del éxito para la optimización de contenidos web [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/176009Data have been obtained from each last-minute news article published online according to the indicators described in the doctoral thesis. All related data are stored in a database, divided into the following tables:tesis_followers: User ID list of media account followers.tesis_hometimeline: data from tweets posted by the media account sharing breaking news from the web.status_id: Tweet IDcreated_at: date of publicationtext: content of the tweetpath: URL extracted after processing the shortened URL in textpost_shared: Article ID in WordPress that is being sharedretweet_count: number of retweetsfavorite_count: number of favoritestesis_hometimeline_other: data from tweets posted by the media account that do not share breaking news from the web. Other typologies, automatic Facebook shares, custom tweets without link to an article, etc. With the same fields as tesis_hometimeline.tesis_posts: data of articles published by the web and processed for some analysis.stats_id: Analysis IDpost_id: Article ID in WordPresspost_date: article publication date in WordPresspost_title: title of the articlepath: URL of the article in the middle webtags: Tags ID or WordPress tags related to the articleuniquepageviews: unique page viewsentrancerate: input ratioavgtimeonpage: average visit timeexitrate: output ratiopageviewspersession: page views per sessionadsense_adunitsviewed: number of ads viewed by usersadsense_viewableimpressionpercent: ad display ratioadsense_ctr: ad click ratioadsense_ecpm: estimated ad revenue per 1000 page viewstesis_stats: data from a particular analysis, performed at each published breaking news item. Fields with statistical values can be computed from the data in the other tables, but total and average calculations are saved for faster and easier further processing.id: ID of the analysisphase: phase of the thesis in which analysis has been carried out (right now all are 1)time: "0" if at the time of publication, "1" if 14 days laterstart_date: date and time of measurement on the day of publicationend_date: date and time when the measurement is made 14 days latermain_post_id: ID of the published article to be analysedmain_post_theme: Main section of the published article to analyzesuperheroes_theme: "1" if about superheroes, "0" if nottrailer_theme: "1" if trailer, "0" if notname: empty field, possibility to add a custom name manuallynotes: empty field, possibility to add personalized notes manually, as if some tag has been removed manually for being considered too generic, despite the fact that the editor put itnum_articles: number of articles analysednum_articles_with_traffic: number of articles analysed with traffic (which will be taken into account for traffic analysis)num_articles_with_tw_data: number of articles with data from when they were shared on the media’s Twitter accountnum_terms: number of terms analyzeduniquepageviews_total: total page viewsuniquepageviews_mean: average page viewsentrancerate_mean: average input ratioavgtimeonpage_mean: average duration of visitsexitrate_mean: average output ratiopageviewspersession_mean: average page views per sessiontotal: total of ads viewedadsense_adunitsviewed_mean: average of ads viewedadsense_viewableimpressionpercent_mean: average ad display ratioadsense_ctr_mean: average ad click ratioadsense_ecpm_mean: estimated ad revenue per 1000 page viewsTotal: total incomeretweet_count_mean: average incomefavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesterms_ini_num_tweets: total tweets on the terms on the day of publicationterms_ini_retweet_count_total: total retweets on the terms on the day of publicationterms_ini_retweet_count_mean: average retweets on the terms on the day of publicationterms_ini_favorite_count_total: total of favorites on the terms on the day of publicationterms_ini_favorite_count_mean: average of favorites on the terms on the day of publicationterms_ini_followers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the terms on the day of publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms on the day of publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who spoke about the terms on the day of publicationterms_ini_user_age_mean: average age in days of users who have spoken of the terms on the day of publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms on the day of publicationterms_end_num_tweets: total tweets on terms 14 days after publicationterms_ini_retweet_count_total: total retweets on terms 14 days after publicationterms_ini_retweet_count_mean: average retweets on terms 14 days after publicationterms_ini_favorite_count_total: total bookmarks on terms 14 days after publicationterms_ini_favorite_count_mean: average of favorites on terms 14 days after publicationterms_ini_followers_talking_rate: ratio of media Twitter account followers who have recently posted a tweet talking about the terms 14 days after publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms 14 days after publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who have spoken about the terms 14 days after publicationterms_ini_user_age_mean: the average age in days of users who have spoken of the terms 14 days after publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms 14 days after publication.tesis_terms: data of the terms (tags) related to the processed articles.stats_id: Analysis IDtime: "0" if at the time of publication, "1" if 14 days laterterm_id: Term ID (tag) in WordPressname: Name of the termslug: URL of the termnum_tweets: number of tweetsretweet_count_total: total retweetsretweet_count_mean: average retweetsfavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesfollowers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the termuser_num_followers_mean: average followers of users who were talking about the termuser_num_tweets_mean: average number of tweets published by users who were talking about the termuser_age_mean: average age in days of users who were talking about the termurl_inclusion_rate: URL inclusion ratio

Difference uses Google Analytics as the Baseline. Results based on Paired t-Test for Hypotheses Supported.

Statistics of visits to the pages of the website of the City of Brussels (in 2014) with the address of the page (according to www.bruxelles.be, the number of pages viewed, the number of unique consultations, the average time spent on the page.Source: Google Analytics.Each page of the City of Brussels website is identified by 4 digits appearing in its address.A search on these 4 digits provides statistics relating to the page in question.

As many general retailers or mass distribution channels experienced an exponential growth during the months of the COVID-19 induced lockdown in France, the source wanted to measure the traffic growth of the different retailers on a yearly basis. Thus, since March 2019, Auchan has been doing better on a year on year basis, with 90.23 percent more traffic in march 2020. In terms of page views, Auchan was able to score 5.9 for the average number of pages views on its website.

Website type for the 86 websites in study.

As of August 2024, among the top ten classified websites worldwide, craigslist.org experienced the highest number of pages per visit. During a session, online consumers browsed an average of 28.47 pages on craigslist. The website finn.no followed with around 19.68 pages per visit.

Cars.com is a U.S.- based automotive classifieds website which recorded nearly ** million average monthly unique visitors from the start of 2019 to June 30th, 2019. In the same period of 2023, average monthly unique visitors to the site raised to **** million.

In November 2024, WhatsApp.com was the most engaging website worldwide, with users spending approximately 31 minutes and 17 seconds per visit on the website. YouTube.com was second in user engagement, with an average visit duration of 22 minutes and 24 minutes and 15 seconds. X.com, which ranks as the tenth most visited website worldwide, reported an average session length of 15 minutes and 26 seconds.

Introduction

Fitbit statistics: In today's health-conscious society, monitoring personal wellness metrics has become increasingly important. Fitbit, a leader in wearable technology, offers users detailed insights into their daily activities, sleep patterns, and heart health. On average, Fitbit users take between 10,000 to 18,000 steps per day, aligning with general fitness recommendations.

Sleep tracking data reveals that users typically achieve about 6.5 hours of sleep each night, accompanied by an average Sleep Score of 77. Regarding cardiovascular health, the average resting heart rate among Fitbit users is approximately 65 beats per minute, with variations influenced by factors such as age and gender. These statistics underscore Fitbit's role in providing users with actionable data to support their health and wellness goals.

Let's delve into the fascinating insights through Fitbit statistics and explore what they can tell us about the brand’s performance in 2025.

Comparison of user, site, and network-centric approaches to web analytics data collection showing advantages, disadvantages, and examples of each approach at the time of the study.