Introduction

Google Analytics Statistics: Google Analytics is one of the most popular tools to monitor your website’s performance, as it gathers data regarding customer behaviour, engagement, and preferences. They are segmented into two different versions: Google Analytics 4 (GA4) and Google Analytics 360 (GA360). Google Analytics was developed by Google and was released on November 14, 2005.

This article includes several detailed analyses from different insights, including overall market analysis, user bases, visitors' interaction with a website, such as page views, session duration, traffic sources, and conversion 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.

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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?

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.

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

Universal Analytics data from Google Analytics for the CalHHS Open Data Portal. This data was captured using the depreciated Universal Analytics tool and is no longer available on the web via Google UI or Google APIs. It has been loaded here so that users and the metrics dashboard can access the data.

Google dominated the web analytics industry in 2024, with ***** of its web analytics technologies maintaining the top three positions in the global market. Google Global Site Tag was first with a market share of over ** percent, followed by Google Analytics and Google Universal Analytics who had market shares of approximately ** and ** percent, respectively. When all ***** technologies were combined, Google maintained more than ** percent of the total market share.

This dataset contains basis performance data from data.kcmo.org. The data is tracked via google analytics.

Google Analytics data for the Queensland Government website (qld.gov.au) (Date range: 1 July 2017 to 30 June 2018)

View metadata for key information about this dataset.The city chose metrics based on the results of a 2020 survey of open data end users. OIT then developed datasets to track these metrics over time and a dashboard to display them visually and in a way accessible to a broad audience of users.For questions about this dataset, contact kistine.carolan@phila.gov. For technical assistance, email maps@phila.gov.

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 dataset tracks the updates made on the dataset "Google Analytics Data for ODP" as a repository for previous versions of the data and metadata.

Dataset

This dataset was created by Ankit Sati

Contents

During an April 2024 survey carried out among retail and e-commerce advertising decision-makers from the United Kingdom (UK), 84 percent of respondents stated they planned to use Google Analytics 4 (GA4) after the phase-out of third-party cookies in Chrome in 2024. GA4 was ruled uncompliant with the European Union's General Data Protection Regulation (GDPR) in 2022.

This dataset contains Google Analytics trace data describing user interactions with the Inter-university Consortium for Political and Social Research (ICPSR) website.

The City uses Google Analytics to track data about use of the City's website.

Auto-generated structured data of Google Analytics 4 Field Reference from table Fields

Context Google Analytics 4 is Google analytics latest service that enables you to measure traffic and engagement across your website as well as app.

Content This is a sample dataset of GA4 for Google Merchendise store for the month of Jan 2021.

Acknowledgements This information is exported from the google bigquery public data set.

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:ga4_obfuscated_sample_ecommerce.events_*

Inspiration To study google analytics it is really very difficult to get sample data so here's making it easy for some in need of one.

Analysis of ‘Google Analytics Sessions on Austintexas.gov’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/04d9d2eb-bbab-44fe-a22b-042b40ab6687 on 27 January 2022.

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

This data, exported from Google Analytics, demonstrates group of interactions that took place on Austintexas.gov pages within a one-month time frame. A single session can contain multiple screen or page views, events, social interactions, and ecommerce transactions.

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

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.

This data about nola.gov provides a window into how people are interacting with the the City of New Orleans online. The data comes from a unified Google Analytics account for New Orleans. We do not track individuals and we anonymize the IP addresses of all visitors.