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?

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.

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.

The dataset consists of feature vectors belonging to 12,330 sessions. The dataset was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period. Of the 12,330 sessions in the dataset, 84.5% (10,422) were negative class samples that did not end with shopping, and the rest (1908) were positive class samples ending with shopping.The dataset consists of 10 numerical and 8 categorical attributes. The 'Revenue' attribute can be used as the class label.The dataset contains 18 columns, each representing specific attributes of online shopping behavior:Administrative and Administrative_Duration: Number of pages visited and time spent on administrative pages.Informational and Informational_Duration: Number of pages visited and time spent on informational pages.ProductRelated and ProductRelated_Duration: Number of pages visited and time spent on product-related pages.BounceRates and ExitRates: Metrics indicating user behavior during the session.PageValues: Value of the page based on e-commerce metrics.SpecialDay: Likelihood of shopping based on special days.Month: Month of the session.OperatingSystems, Browser, Region, TrafficType: Technical and geographical attributes.VisitorType: Categorizes users as returning, new, or others.Weekend: Indicates if the session occurred on a weekend.Revenue: Target variable indicating whether a transaction was completed (True or False).The original dataset has been picked up from the UCI Machine Learning Repository, the link to which is as follows :https://archive.ics.uci.edu/dataset/468/online+shoppers+purchasing+intention+datasetAdditional Variable InformationThe dataset consists of 10 numerical and 8 categorical attributes. The 'Revenue' attribute can be used as the class label. "Administrative", "Administrative Duration", "Informational", "Informational Duration", "Product Related" and "Product Related Duration" represent the number of different types of pages visited by the visitor in that session and total time spent in each of these page categories. The values of these features are derived from the URL information of the pages visited by the user and updated in real time when a user takes an action, e.g. moving from one page to another. The "Bounce Rate", "Exit Rate" and "Page Value" features represent the metrics measured by "Google Analytics" for each page in the e-commerce site. The value of "Bounce Rate" feature for a web page refers to the percentage of visitors who enter the site from that page and then leave ("bounce") without triggering any other requests to the analytics server during that session. The value of "Exit Rate" feature for a specific web page is calculated as for all pageviews to the page, the percentage that were the last in the session. The "Page Value" feature represents the average value for a web page that a user visited before completing an e-commerce transaction. The "Special Day" feature indicates the closeness of the site visiting time to a specific special day (e.g. Mother’s Day, Valentine's Day) in which the sessions are more likely to be finalized with transaction. The value of this attribute is determined by considering the dynamics of e-commerce such as the duration between the order date and delivery date. For example, for Valentina’s day, this value takes a nonzero value between February 2 and February 12, zero before and after this date unless it is close to another special day, and its maximum value of 1 on February 8. The dataset also includes operating system, browser, region, traffic type, visitor type as returning or new visitor, a Boolean value indicating whether the date of the visit is weekend, and month of the year.

This dataset is composed of the URLs of the top 1 million websites. The domains are ranked using the Alexa traffic ranking which is determined using a combination of the browsing behavior of users on the website, the number of unique visitors, and the number of pageviews. In more detail, unique visitors are the number of unique users who visit a website on a given day, and pageviews are the total number of user URL requests for the website. However, multiple requests for the same website on the same day are counted as a single pageview. The website with the highest combination of unique visitors and pageviews is ranked the highest

Raw website usage statistics for data.govt.nz including unique visitors, page views, click-thoughs to data hosting websites, cumulative number of dataset listing pages, and 25 most viewed datasets per month.

The Exhibit of Datasets was an experimental project with the aim of providing concise introductions to research datasets in the humanities and social sciences deposited in a trusted repository and thus made accessible for the long term. The Exhibit consists of so-called 'showcases', short webpages summarizing and supplementing the corresponding data papers, published in the Research Data Journal for the Humanities and Social Sciences. The showcase is a quick introduction to such a dataset, a bit longer than an abstract, with illustrations, interactive graphs and other multimedia (if available). As a rule it also offers the option to get acquainted with the data itself, through an interactive online spreadsheet, a data sample or link to the online database of a research project. Usually, access to these datasets requires several time consuming actions, such as downloading data, installing the appropriate software and correctly uploading the data into these programs. This makes it difficult for interested parties to quickly assess the possibilities for reuse in other projects.

The Exhibit aimed to help visitors of the website to get the right information at a glance by: - Attracting attention to (recently) acquired deposits: showing why data are interesting. - Providing a concise overview of the dataset's scope and research background; more details are to be found, for example, in the associated data paper in the Research Data Journal (RDJ). - Bringing together references to the location of the dataset and to more detailed information elsewhere, such as the project website of the data producers. - Allowing visitors to explore (a sample of) the data without downloading and installing associated software at first (see below). - Publishing related multimedia content, such as videos, animated maps, slideshows etc., which are currently difficult to include in online journals as RDJ. - Making it easier to review the dataset. The Exhibit would also have been the right place to publish these reviews in the same way as a webshop publishes consumer reviews of a product, but this could not yet be achieved within the limited duration of the project.

Note (1) The text of the showcase is a summary of the corresponding data paper in RDJ, and as such a compilation made by the Exhibit editor. In some cases a section 'Quick start in Reusing Data' is added, whose text is written entirely by the editor. (2) Various hyperlinks such as those to pages within the Exhibit website will no longer work. The interactive Zoho spreadsheets are also no longer available because this facility has been discontinued.

Summary time series data of the International Visitor Survey, the National Visitor Survey and the State Tourism Satellite Account, as published by Tourism Research Australia (TRA). These data sources estimate total visitor expenditure in South Australia, direct tourism jobs and regional tourism expenditure. Breakdowns of visitor origin are also provided, with time series of visitors from the UK, Germany, USA, China and New Zealand, as well as domestic visitors in South Australia.

For further details on these datasets please visit the TRA website: https://www.tra.gov.au/research

Context

There are a lot of unknowns when running an E-commerce store, even when you have analytics to guide your decisions.

Users are an important factor in an e-commerce business. This is especially true in a C2C-oriented store, since they are both the suppliers (by uploading their products) AND the customers (by purchasing other user's articles).

This dataset aims to serve as a benchmark for an e-commerce fashion store. Using this dataset, you may want to try and understand what you can expect of your users and determine in advance how your grows may be.

• For instance, if you see that most of your users are not very active, you may look into this dataset to compare your store's performance.

If you think this kind of dataset may be useful or if you liked it, don't forget to show your support or appreciation with an upvote/comment. You may even include how you think this dataset might be of use to you. This way, I will be more aware of specific needs and be able to adapt my datasets to suits more your needs.

This dataset is part of a preview of a much larger dataset. Please contact me for more.

Content

What is inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

The data was scraped from a successful online C2C fashion store with over 9M registered users. The store was first launched in Europe around 2009 then expanded worldwide.

Visitors vs Users: Visitors do not appear in this dataset. Only registered users are included. "Visitors" cannot purchase an article but can view the catalog.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Questions you might want to answer using this dataset:

• Are e-commerce users interested in social network feature ?
• Are my users active enough (compared to those of this dataset) ?
• How likely are people from other countries to sign up in a C2C website ?
• How many users are likely to drop off after years of using my service ?

License

CC-BY-NC-SA 4.0

For other licensing options, contact me.

Jordan Number of Visitors: Madaba Visit Center data was reported at 4,298.000 Person in Dec 2017. This records a decrease from the previous number of 6,930.000 Person for Nov 2017. Jordan Number of Visitors: Madaba Visit Center data is updated monthly, averaging 8,133.500 Person from Jan 2006 (Median) to Dec 2017, with 144 observations. The data reached an all-time high of 35,637.000 Person in Oct 2010 and a record low of 1,492.000 Person in Feb 2016. Jordan Number of Visitors: Madaba Visit Center data remains active status in CEIC and is reported by Ministry of Tourism and Antiquities. The data is categorized under Global Database’s Jordan – Table JO.Q009: Number of Visitors: by Tourist Sites.

Our Market Analysis dataset uncovers consumer movement patterns across brands and categories, helping you define your true trade area and optimize location strategy.

Using foot traffic data tied to specific POIs, this GDPR-compliant, non-PII dataset highlights where your visitors also shop — enabling smarter site selection, lease renegotiation, and competitive market analysis.

Key data points include: - Cross-visitation trends by brand/category - Consumer reach and trade area definition - Weekly, monthly, and quarterly aggregations - Cleaned, normalized, and updated data - Non-PII and fully GDPR-compliant

Focused on the U.S. market, this dataset is ideal for retailers, landlords, and consultants looking to map behavior, refine market coverage, and drive informed decisions.

The Great Britain Day Visits Survey measures the visits and expenditure of tourism day visitors to Scotland.

Jordan Number of Visitors: Jarash data was reported at 17,428.000 Person in Dec 2017. This records a decrease from the previous number of 23,095.000 Person for Nov 2017. Jordan Number of Visitors: Jarash data is updated monthly, averaging 18,492.000 Person from Jan 2004 (Median) to Dec 2017, with 168 observations. The data reached an all-time high of 59,150.000 Person in Oct 2010 and a record low of 5,945.000 Person in Jun 2016. Jordan Number of Visitors: Jarash data remains active status in CEIC and is reported by Ministry of Tourism and Antiquities. The data is categorized under Global Database’s Jordan – Table JO.Q009: Number of Visitors: by Tourist Sites.

Jordan Number of Visitors: Mukawir data was reported at 428.000 Person in Dec 2017. This records a decrease from the previous number of 756.000 Person for Nov 2017. Jordan Number of Visitors: Mukawir data is updated monthly, averaging 713.000 Person from Jan 2004 (Median) to Dec 2017, with 123 observations. The data reached an all-time high of 1,917.000 Person in May 2008 and a record low of 98.000 Person in Feb 2013. Jordan Number of Visitors: Mukawir data remains active status in CEIC and is reported by Ministry of Tourism and Antiquities. The data is categorized under Global Database’s Jordan – Table JO.Q009: Number of Visitors: by Tourist Sites.

Dataset from People's Association. For more information, visit https://data.gov.sg/datasets/d_4d2c99ea159f1f6dd67beb58bf9cbe8d/view

Abstract copyright UK Data Service and data collection copyright owner.The People and Nature Survey for England is one of the main sources of data and statistics on how people experience and think about the environment. It began collecting data in April 2020 and has been collecting data since. The survey builds on the Monitor of Engagement with the Natural Environment (MENE) survey which ran from 2009 to 2019. Data from the People and Nature Survey for England enables users to:understand how people use, enjoy, and are motivated to protect the natural environmentmonitor changes in use of the natural environment over time, at a range of different spatial scales and for key groups within the populationunderstand how being in the natural environment can influence wellbeingunderstand environmental attitudes and the actions people take at home, in the garden and in the wider community to protect the environmentThis data contributes to Natural England’s delivery of statutory duties, informs Defra policy and natural capital accounting, and contributes to the outcome indicator framework for the 25 Year Environment Plan.Different versions of the People and Nature Survey for England are available from the UK Data Archive under Open Access (SN 9092) conditions, End User Licence (SN 9093), and Secure Access (SN 9094). The Secure Access version includes the same data as the End User Licence version, but includes more detailed variables including:age as a continuous variablesexwhether gender is the same as at birthsexual orientationmore detailed ethnicitywhere journey to recent visit to green and natural space started fromvisit datedetailed home geography, including local authority district, urban/rural area, and Index of Multiple Deprivationa number of variables that have not been top-coded, including number of children and number of children in household, food and drink expenditure, and incomeThe Open Access version includes the same data as the End User Licence version, but does not include the following variables:age bandgender identitymarital statusnumber of children living in householdnumber of childrenwork statusstudent working statusincomequalificationethnicity and consent to answer ethnicity questionnumber of vehiclespresence of dog in householdphysical activityvarious health dataResearchers are advised to review the Open Access and/or the End User Licence versions to determine if these are adequate prior to ordering the Secure Access version.Accredited official statistics are called National Statistics in the Statistics and Registration Service Act 2007. An explanation can be found on the Office for Statistics Regulation website.Natural England's statistical practice is regulated by the Office for Statistics Regulation (OSR). OSR sets the standards of trustworthiness, quality and value in the Code of Practice for Statistics that all producers of official statistics should adhere to. These accredited official statistics were independently reviewed by the Office for Statistics Regulation in January 2023. They comply with the standards of trustworthiness, quality and value in the Code of Practice for Statistics and should be labelled ‘accredited official statistics’.Users are welcome to contact Natural England directly at people_and_nature@naturalengland.org.uk with any comments about how they meet these standards. Alternatively, users can contact OSR by emailing regulation@statistics.gov.uk or via the OSR website.Since the latest review by the Office for Statistics Regulation, Natural England have continued to comply with the Code of Practice for Statistics, and have made the following improvements:Published a development plan with timetables for future work, which will be updated annuallyEnsured that users have opportunities to contribute to development planning through their biannual Research User GroupEnabled wider access to the data by publishing raw data sets through the UK Data ServiceProvided users with guidance on how statistics from their products can be compared with those produced in the devolved nationsPublished guidance on the differences between PaNS and MENEImproved estimates of the percentage of people visiting nature in the previous 14 days by reducing the amount of respondents answering ‘don’t know’.These data are available in Excel, SPSS, as well as Open Document Spreadsheet (ODS) formats. For the fifth edition (June 2024), data for October to December 2023 have been added. Main Topics:

Abstract copyright UK Data Service and data collection copyright owner.The People and Nature Survey for England is one of the main sources of data and statistics on how people experience and think about the environment. It began collecting data in April 2020 and has been collecting data since. The survey builds on the Monitor of Engagement with the Natural Environment (MENE) survey which ran from 2009 to 2019. Data from the People and Nature Survey for England enables users to:understand how people use, enjoy, and are motivated to protect the natural environmentmonitor changes in use of the natural environment over time, at a range of different spatial scales and for key groups within the populationunderstand how being in the natural environment can influence wellbeingunderstand environmental attitudes and the actions people take at home, in the garden and in the wider community to protect the environmentThis data contributes to Natural England’s delivery of statutory duties, informs Defra policy and natural capital accounting, and contributes to the outcome indicator framework for the 25 Year Environment Plan.Different versions of the People and Nature Survey for England are available from the UK Data Archive under Open Access (SN 9092) conditions, End User Licence (SN 9093), and Secure Access (SN 9094). The Secure Access version includes the same data as the End User Licence version, but includes more detailed variables including:age as a continuous variablesexwhether gender is the same as at birthsexual orientationmore detailed ethnicitywhere journey to recent visit to green and natural space started fromvisit datedetailed home geography, including local authority district, urban/rural area, and Index of Multiple Deprivationa number of variables that have not been top-coded, including number of children and number of children in household, food and drink expenditure, and incomeThe Open Access version includes the same data as the End User Licence version, but does not include the following variables:age bandgender identitymarital statusnumber of children living in householdnumber of childrenwork statusstudent working statusincomequalificationethnicity and consent to answer ethnicity questionnumber of vehiclespresence of dog in householdphysical activityvarious health dataResearchers are advised to review the Open Access and/or the End User Licence versions to determine if these are adequate prior to ordering the Secure Access version.Accredited official statistics are called National Statistics in the Statistics and Registration Service Act 2007. An explanation can be found on the Office for Statistics Regulation website.Natural England's statistical practice is regulated by the Office for Statistics Regulation (OSR). OSR sets the standards of trustworthiness, quality and value in the Code of Practice for Statistics that all producers of official statistics should adhere to. These accredited official statistics were independently reviewed by the Office for Statistics Regulation in January 2023. They comply with the standards of trustworthiness, quality and value in the Code of Practice for Statistics and should be labelled ‘accredited official statistics’.Users are welcome to contact Natural England directly at people_and_nature@naturalengland.org.uk with any comments about how they meet these standards. Alternatively, users can contact OSR by emailing regulation@statistics.gov.uk or via the OSR website.Since the latest review by the Office for Statistics Regulation, Natural England have continued to comply with the Code of Practice for Statistics, and have made the following improvements:Published a development plan with timetables for future work, which will be updated annuallyEnsured that users have opportunities to contribute to development planning through their biannual Research User GroupEnabled wider access to the data by publishing raw data sets through the UK Data ServiceProvided users with guidance on how statistics from their products can be compared with those produced in the devolved nationsPublished guidance on the differences between PaNS and MENEImproved estimates of the percentage of people visiting nature in the previous 14 days by reducing the amount of respondents answering ‘don’t know’.These data are available in Excel, SPSS, as well as Open Document Spreadsheet (ODS) formats. For the fifth edition (June 2024), data for October to December 2023 have been added. Main Topics: The People and Nature Survey for England survey covers topics including: visits to green and natural spacesactivities in green and natural spaceschildren and green and natural spaces wellbeingaccess to natural and open spaces and gardens attitudes towards the natural environment and environmental problems pro-environmental behaviour human health

At Echo, our dedication to data curation is unmatched; we focus on providing our clients with an in-depth picture of a physical location based on activity in and around the point of interest (POI) over time. Our dataset empowers you to explore the cross-shopping patterns from your visitors by allowing you to dig deeper into consumer profiles, eliminate gaps in your trade area and discover untapped sites of action.

This sample of our Market Analysis solution helps you determine the geographical reach of your store or facility based on the brands or categories most visited by consumers who visit your specific POI. This empowers your location strategy. This particular dataset is for Europe.

Additional Information:

• Understand the actual movement patterns of consumers without using PII data, gaining a 360-degree consumer view. Complement your online behavior knowledge with actual offline actions, and better attribute intent based on real-world behaviors.
• Echo collects, cleans and updates its footfall on a daily basis. Normalization of the data occurs on a monthly basis.
• We provide data aggregation on a weekly, monthly and quarterly basis.

• Information about our country offering and data schema can be found here:

  1) Data Schema: https://docs.echo-analytics.com/activity/data-schema 2) Country Availability: https://docs.echo-analytics.com/activity/country-coverage 3) Methodology: https://docs.echo-analytics.com/activity/methodology

  Echo's commitment to customer service is evident in our exceptional data quality and dedicated team, providing 360° support throughout your location intelligence journey. We handle the complex tasks to deliver analysis-ready datasets to you.

Business Needs: - Site Selection and Lease Renegotiation: Leverage foot traffic data for optimal site selection and advantageous lease renegotiations. This approach enables you to pinpoint ideal store locations and secure lease terms that align with business objectives, optimizing operational efficiency and cost-effectiveness.

-Market Intelligence: Outsmart your competition by understanding competitor foot traffic trends, allowing you to identify growth opportunities and gain a competitive advantage. Analyze regional consumer behaviors and preferences to pinpoint new markets and assess the competitive landscape for strategic expansion.

Jordan Number of Visitors: Karak data was reported at 1,400.000 Person in Dec 2017. This records a decrease from the previous number of 2,300.000 Person for Nov 2017. Jordan Number of Visitors: Karak data is updated monthly, averaging 5,700.000 Person from Jan 2004 (Median) to Dec 2017, with 168 observations. The data reached an all-time high of 29,800.000 Person in Oct 2010 and a record low of 450.000 Person in Jun 2016. Jordan Number of Visitors: Karak data remains active status in CEIC and is reported by Ministry of Tourism and Antiquities. The data is categorized under Global Database’s Jordan – Table JO.Q009: Number of Visitors: by Tourist Sites.

The Points of Interest (POI) web service provides the identification and location of a feature, service or activity that people may want to see, know about or visit. POI features for this service …Show full descriptionThe Points of Interest (POI) web service provides the identification and location of a feature, service or activity that people may want to see, know about or visit. POI features for this service are primarily derived from features maintained within the Digital Topographic Database (DTDB). The POI feature class is maintained programmatically (automated) by sourcing spatial and aspatial attributes from other feature classes in the DTDB that contain POI features. The midpoint of a line or polygon features is used to define the POI. Points of Interest include features related to Community, Education, Recreation, Transportation, Utility, or Hydrography, Physiography and Place, and defined as a place with a prescribed name. The attribute information for an individual dataset may have been thinned or modifed to cater for the service. The service is available in a cached environment only. This dataset is compliant with the NSW FSDF and its specifications.