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.

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.

Below you’ll find a month by month breakdown of traffic on the australia.gov.au website along the following lines:

• Pageviews
• Visits
• Pages per visit
• Average time on page
• Devices

This data is generated using Google analytics.

Please Note: This is an initial version of the data only. We’re looking forward to hearing your feedback on what other metrics are of interest to you. Please let us know by sending an email to data@digital.gov.au.

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.

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.

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?

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

Per the Federal Digital Government Strategy, the Department of Homeland Security Metrics Plan, and the Open FEMA Initiative, FEMA is providing the following web performance metrics with regards to FEMA.gov.rnrnInformation in this dataset includes total visits, avg visit duration, pageviews, unique visitors, avg pages/visit, avg time/page, bounce ratevisits by source, visits by Social Media Platform, and metrics on new vs returning visitors.rnrnExternal Affairs strives to make all communications accessible. If you have any challenges accessing this information, please contact FEMAWebTeam@fema.dhs.gov.

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.

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.

While kmart.com.au generated one of the highest web traffics across popular department store websites in Australia in February 2025 at over 20.5 million site visits, users of the department store website viewed only an average of 4.95 pages per visit. While harrisscarfe.com.au had a lower overall web traffic, it scored a higher page engagement rate, with an average of 6.28 pages per visit. Nonetheless, higher page visits in a single session does not necessarily equate to greater overall engagement, as more pages may have been viewed due to user difficulties in navigating web layouts and finding products.

Visitor statistics of the pages of the website of the City of Brussels (2015) with the title of the page, the number of pages viewed, the number of unique visits, the average time spent on the page. Source: Google Analytics.

Visitor statistics of the pages of the website of the City of Brussels with the title of the page, the number of pages viewed, the number of unique visits, the average time spent on the page. Source: Google Analytics.

The dataset contains information, divided by month, on the accesses made to the online services offered by the citizen's file and provided by the municipality of Milan. The pageviews column represents the total number of web pages, which have been displayed within the time frame used. The visitors column represents the total number of unique visitors who have accessed the web pages. By unique visitor, we mean a visitor counted only once within the time frame used.

This is a combination of the following datasets: * Icons website Municipality of Utrecht * Most visited topics website municipality of Utrecht < a name="iconen-website-gemeente-utrecht"> #### Iconen website Gemeente Utrecht For the website of the Municipality of Utrecht, 45 different icons have been developed. The datasets in PNG and PSD (Photoshop) format are shown below, and there is also a preview in which the icons are shown. Examples of available icons are: * zoning plan; * notification; * to marry; * integration. #### Most visited topics website municipality of Utrecht Overview of the topics most searched for on the website municipality of Utrecht (www.utrecht .NL). This information is presented per month and includes the following data per object: * click path on website (where subject can be found); * number of page views; * average time of visit to website. * link web site.

The dataset contains information, divided by month, on accesses made to the online services offered by the opendata portal and provided by the municipality of Milan. The pageviews column represents the total number of web pages that have been viewed within the time frame used. The visits column represents the total visits made, within the time frame used. The visitors column represents the total number of unique visitors who have accessed the web pages. By unique visitor, we mean a visitor counted only once within the time frame used.

amazon.com.au generated one of the highest web traffics across popular online marketplace websites in Australia in February 2025 at around 60 million site visits, with users of the online marketplace website viewing an average of 7.64 pages per visit in February 2025. While catch.com.au had a lower overall web traffic, it scored a higher page engagement rate, with an average of 7.82 pages per visit. Nonetheless, higher page visits in a single session does not necessarily equate to greater overall engagement, as more pages may have been viewed due to user difficulties in navigating web layouts and finding products.

The dataset contains information, divided by month, on the accesses made to the online services offered by the institutional portal and provided by the municipality of Milan. The pageviews column represents the total number of web pages, which have been displayed within the time frame used. The visits column represents the total number of visits made, within the time frame used. The visitors column represents the total number of unique visitors who have accessed the web pages. By unique visitor, we mean a visitor counted only once within the time frame used.

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

Background: Web portals providing health information online have the potential to reach large numbers of people. Yet few studies have assessed usage and costs of health portals, especially over an extended period of time. Objectives: First, to assess the usage of a web portal on prevention and health promotion that was initiated by the German states of Berlin and Brandenburg. Second, to estimate average cost per pageview over the full web portal life-cycle. Methods: Usage data were gathered through Google Analytics. The main usage metric assessed was pageviews originating from an IP address in Berlin or Brandenburg. Project cost data were reported by the local government and from own records. Descriptive and regression analyses were used to determine time trends in pageviews. Results: The redesigned web portal Praeventionsatlas.de launched August 2011 and was discontinued March 2018. Analyzing the full calendar years only, the annual pageview number increased from 2012 (8492) to its peak in 2014 (11,876) and steadily decreased to its minimum in 2017 (7232). Pages of the web portal were most often viewed around lunchtime (12 to 1 p.m.), in the middle of the week (Wed to Thu), and at the beginning of the year (Jan to Feb). Adjusting for year, month, and weekday, the number of daily pageviews dropped on major public holidays (Easter −9, Pentecost −12, Christmas −12 and New Year’s Eve and Day −20; P<.001) and increased during the fasting period before Easter (+6; P<.001). Average cost per page viewed from relaunch until the web portal went offline was estimated to be €0.38 (€0.33 to €1.67) per pageview. Conclusion: Usage of the web portal showed temporal variation and peaked before the middle of its 6.5 year life-cycle. Development and need for updates may require substantial investment in a web portal, such that a pageview can come at a considerable expense, even in small-scale projects. Assessing and discussing the cost-effectiveness of a web portal is therefore desirable.