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 ***** pages per online session. Travel and hospitality ranked second, with an average of almost *** pages visited. In terms of mobile users, travel and hospitality registered the highest number of page views, followed by retail.

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 ***** pages on craigslist. The website finn.no followed with around ***** pages per visit.

In November 2024, Xvideos.com and Instagram.com were the websites with the most pages per visit in Colombia, with an average of **** and **** pages visited per session each. Despite being the second most accessed in total visits and the website with the second highest average session length, YouTube.com ranked fourth in terms of pages per visit, with **** pages, while Google.com ranked tenth, with **** pages visited per session.

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 **** pages visited. Meanwhile, nbcsportsedge.com had an average of ***** pages viewed per visit.

General data recollected for the studio " Analysis of the Quantitative Impact of Social Networks on Web Traffic of Cybermedia in the 27 Countries of the European Union". Four research questions are posed: what percentage of the total web traffic generated by cybermedia in the European Union comes from social networks? Is said percentage higher or lower than that provided through direct traffic and through the use of search engines via SEO positioning? Which social networks have a greater impact? And is there any degree of relationship between the specific weight of social networks in the web traffic of a cybermedia and circumstances such as the average duration of the user's visit, the number of page views or the bounce rate understood in its formal aspect of not performing any kind of interaction on the visited page beyond reading its content? To answer these questions, we have first proceeded to a selection of the cybermedia with the highest web traffic of the 27 countries that are currently part of the European Union after the United Kingdom left on December 31, 2020. In each nation we have selected five media using a combination of the global web traffic metrics provided by the tools Alexa (https://www.alexa.com/), which ceased to be operational on May 1, 2022, and SimilarWeb (https:// www.similarweb.com/). We have not used local metrics by country since the results obtained with these first two tools were sufficiently significant and our objective is not to establish a ranking of cybermedia by nation but to examine the relevance of social networks in their web traffic. In all cases, cybermedia whose property corresponds to a journalistic company have been selected, ruling out those belonging to telecommunications portals or service providers; in some cases they correspond to classic information companies (both newspapers and televisions) while in others they refer to digital natives, without this circumstance affecting the nature of the research proposed.
Below we have proceeded to examine the web traffic data of said cybermedia. The period corresponding to the months of October, November and December 2021 and January, February and March 2022 has been selected. We believe that this six-month stretch allows possible one-time variations to be overcome for a month, reinforcing the precision of the data obtained. To secure this data, we have used the SimilarWeb tool, currently the most precise tool that exists when examining the web traffic of a portal, although it is limited to that coming from desktops and laptops, without taking into account those that come from mobile devices, currently impossible to determine with existing measurement tools on the market. It includes:

Web traffic general data: average visit duration, pages per visit and bounce rate Web traffic origin by country Percentage of traffic generated from social media over total web traffic Distribution of web traffic generated from social networks Comparison of web traffic generated from social netwoks with direct and search procedures

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.

amazon.com.au generated one of the highest web traffics across popular online marketplace websites in Australia in February 2025 at around ** million site visits, with users of the online marketplace website viewing an average of **** 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 **** 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.

In this table, you’ll see the average landing page conversions based on the subscription rate they generated across industries.

From September to November 2024, Facebook.com was the most-visited website in Vietnam with an average of **** pages per visit. The same report also revealed that Google.com was the website with the highest number of unique visits.

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

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.

In this table, we’re looking at whether adding video content (including links to your video hosting platforms) could help you boost your engagement metrics, primarily the average click-th rough and click-to-open rates.

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?

From January to March 2021, users worldwide averaged **** pages visited every time they accessed YouTube.com from any kind of device. In the examined period, the number of pages visited in one session has decreased by more than ** percent compared to the corresponding period in the previous year. As the beginning of 2021, users visiting the platform's website via mobile tended to visit more pages, resulting in an average of **** pages per visit from mobile devices. In the case of users accessing the website from desktop devices, the number of pages visited decreased to **** per session.

In March 2024, blivakker.no was the most visited beauty and cosmetic website in Norway. Nevertheless, out of the top ten most visited beauty websites, it did not have the highest average number of pages per visit. This title was taken by cosdna.com. On average, users clicked through ***** pages per visit on the website. Luxplus.no and blivakker.no followed with significantly fewer pages per visit, with **** and ****, respectively.

Here you'll find the average landing page conversion rate, based on the subscription rate, broken down by industries. Using this data, you can see how your landing pages perform compared to others in your sector. As you'll see from the table, the numbers vary significantly. That's because the nature of communication is different for each sector. In some industries, marketers use various opt-in incentives (lead magnets), like discount codes, coupons, or giveaways, and therefore can boost their conversion rates. In others, like nonprofits, this approach is less common and may not be appropriate.

What’s the right email frequency? What’s the potential increase in the number of conversions your email campaigns generate if you add an extra message to your schedule? The data in this table should help you find the right answers.

How many emails should you put into your autoresponder cycle? We’ve analyzed how the average engagement metrics change depending on the number of emails our customers used in their autoresp onder cycles.

In March 2024, lyko.com was the most visited beauty and cosmetic website in Sweden. However, it did not have the highest average number of pages viewed among the top ten most visited beauty sites. This title was taken by fragrantica.com. On average, users clicked through **** pages per visit on fragrantica.com. Notino.se and luxplus.se followed with **** and **** pages per visit, respectively.

Here, we’ve gathered email marketing benchmarks by industry. You can see how your average email open, click-through, click-to-open, unsubscribe, and spam complaint rates compare against other companies in your industry.