Source:

Mariusz Šapczyński, Cracow University of Economics, Poland, lapczynm '@' uek.krakow.pl Sylwester Białowąs, Poznan University of Economics and Business, Poland, sylwester.bialowas '@' ue.poznan.pl

Data Set Information:

The dataset contains information on clickstream from online store offering clothing for pregnant women. Data are from five months of 2008 and include, among others, product category, location of the photo on the page, country of origin of the IP address and product price in US dollars.

Attribute Information:

The dataset contains 14 variables described in a separate file (See 'Data set description')

Relevant Papers:

N/A

Citation Request:

If you use this dataset, please cite:

Šapczyński M., Białowąs S. (2013) Discovering Patterns of Users' Behaviour in an E-shop - Comparison of Consumer Buying Behaviours in Poland and Other European Countries, “Studia Ekonomiczne†, nr 151, “La société de l'information : perspective européenne et globale : les usages et les risques d'Internet pour les citoyens et les consommateurs†, p. 144-153

Data description ìe-shop clothing 2008î

Variables:

1. YEAR (2008)

========================================================

2. MONTH -> from April (4) to August (8)

========================================================

3. DAY -> day number of the month

========================================================

4. ORDER -> sequence of clicks during one session

========================================================

5. COUNTRY -> variable indicating the country of origin of the IP address with the

following categories:

1-Australia 2-Austria 3-Belgium 4-British Virgin Islands 5-Cayman Islands 6-Christmas Island 7-Croatia 8-Cyprus 9-Czech Republic 10-Denmark 11-Estonia 12-unidentified 13-Faroe Islands 14-Finland 15-France 16-Germany 17-Greece 18-Hungary 19-Iceland 20-India 21-Ireland 22-Italy 23-Latvia 24-Lithuania 25-Luxembourg 26-Mexico 27-Netherlands 28-Norway 29-Poland 30-Portugal 31-Romania 32-Russia 33-San Marino 34-Slovakia 35-Slovenia 36-Spain 37-Sweden 38-Switzerland 39-Ukraine 40-United Arab Emirates 41-United Kingdom 42-USA 43-biz (.biz) 44-com (.com) 45-int (.int) 46-net (.net) 47-org (*.org)

========================================================

6. SESSION ID -> variable indicating session id (short record)

========================================================

7. PAGE 1 (MAIN CATEGORY) -> concerns the main product category:

1-trousers 2-skirts 3-blouses 4-sale

========================================================

8. PAGE 2 (CLOTHING MODEL) -> contains information about the code for each product

(217 products)

========================================================

9. COLOUR -> colour of product

1-beige 2-black 3-blue 4-brown 5-burgundy 6-gray 7-green 8-navy blue 9-of many colors 10-olive 11-pink 12-red 13-violet 14-white

========================================================

10. LOCATION -> photo location on the page, the screen has been divided into six parts:

1-top left 2-top in the middle 3-top right 4-bottom left 5-bottom in the middle 6-bottom right

========================================================

11. MODEL PHOTOGRAPHY -> variable with two categories:

1-en face 2-profile

========================================================

12. PRICE -> price in US dollars

========================================================

13. PRICE 2 -> variable informing whether the price of a particular product is higher than

the average price for the entire product category

1-yes 2-no

========================================================

14. PAGE -> page number within the e-store website (from 1 to 5)

++++++++++++++++++++++++++++++++++++++++++++++++++++++++

This dataset is having data of customers who buys clothes online. The store offers in-store style and clothing advice sessions. Customers come in to the store, have sessions/meetings with a personal stylist, then they can go home and order either on a mobile app or website for the clothes they want.

The company is trying to decide whether to focus their efforts on their mobile app experience or their website.

One of the reasons behind AI-powered customer service is the preference for conversational AI over phone calls. In 2024, 82 percent of consumers stated they would use a chatbot instead of waiting for a customer representative to take their call. An outstanding 96 percent of surveyed shoppers believed that more companies should opt for chatbots over traditional customer support services.

E-Commerce Sales Dataset

Analyzing and Maximizing Online Business Performance

By ANil [source]

About this dataset

This dataset provides an in-depth look at the profitability of e-commerce sales. It contains data on a variety of sales channels, including Shiprocket and INCREFF, as well as financial information on related expenses and profits. The columns contain data such as SKU codes, design numbers, stock levels, product categories, sizes and colors. In addition to this we have included the MRPs across multiple stores like Ajio MRP , Amazon MRP , Amazon FBA MRP , Flipkart MRP , Limeroad MRP Myntra MRP and PaytmMRP along with other key parameters like amount paid by customer for the purchase , rate per piece for every individual transaction Also we have added transactional parameters like Date of sale months category fulfilledby B2b Status Qty Currency Gross amt . This is a must-have dataset for anyone trying to uncover the profitability of e-commerce sales in today's marketplace

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How to use the dataset

This dataset provides a comprehensive overview of e-commerce sales data from different channels covering a variety of products. Using this dataset, retailers and digital marketers can measure the performance of their campaigns more accurately and efficiently.

The following steps help users make the most out of this dataset: - Analyze the general sales trends by examining info such as month, category, currency, stock level, and customer for each sale. This will give you an idea about how your e-commerce business is performing in each channel.
- Review the Shiprocket and INCREF data to compare and analyze profitability via different fulfilment methods. This comparison would enable you to make better decisions towards maximizing profit while minimizing costs associated with each method’s referral fees and fulfillment rates.
- Compare prices between various channels such as Amazon FBA MRP, Myntra MRP, Ajio MRP etc using the corresponding columns for each store (Amazon MRP etc). You can judge which stores are offering more profitable margins without compromising on quality by analyzing these pricing points in combination with other information related to product sales (TP1/TP2 - cost per piece).
- Look at customer specific data such as TP 1/TP 2 combination wise Gross Amount or Rate info in terms price per piece or total gross amount generated by any SKU dispersed over multiple customers with relevant dates associated to track individual item performance relative to others within its category over time periods shortlisted/filtered appropriately.. Have an eye on items commonly utilized against offers or promotional discounts offered hence crafting strategies towards inventory optimization leading up-selling operations.?
- Finally Use Overall ‘Stock’ details along all the P & L Data including Yearly Expenses_IIGF information record for takeaways which might be aimed towards essential cost cutting measures like switching amongst delivery options carefully chosen out of Shiprocket & INCREFF leadings away from manual inspections catering savings under support personnel outsourcing structures.?

By employing a comprehensive understanding on how our internal subsidiaries perform globally unless attached respective audits may provide us remarkably lower operational costs servicing confidence; costing far lesser than being incurred taking into account entire pallet shipments tracking sheets representing current level supply chains efficiencies achieved internally., then one may finally scale profits exponentially increases cut down unseen losses followed up introducing newer marketing campaigns necessarily tailored according playing around multiple goods based spectrums due powerful backing suitable transportation boundaries set carefully

Research Ideas

• Analysing the difference in profitability between sales made through Shiprocket and INCREFF. This data can be used to see where the biggest profit margins lie, and strategize accordingly.
• Examining the Complete Cost structure of a product with all its components and their contribution towards revenue or profitability, i.e., TP 1 & 2, MRP Old & Final MRP Old together with Platform based MRP - Amazon, Myntra and Paytm etc., Currency based Profit Margin etc.
• Building a predictive model using Machine Learning by leveraging historical data to predict future sales volume and profits for e-commerce products across multiple categories/devices/platforms such as Amazon, Flipkart, Myntra etc as well providing m...

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As of early 2023, approximately ** percent of consumers in the United States said they would prefer to shop mostly online rather than in-store, making it the country with highest online shopping preference. In contrast, more shoppers preferred visiting physical stores in countries such as Austria, Finland, and New Zealand.

This dataset contains survey responses of 430 online shopping customers collected through google forms.

Columns: 1. Name 2. E-mail ID 3. Age 4. Gender 5. City (from which you do online shopping) 6. State
7. Highest Education received 8. Profession
9. Annual Income
10. Which among the following E-Commerce Website have you ever opened?
11. Which shopping platforms did you shop from, in the last one year?
12. Which online shopping website do you use the most? Please specify the reason why you didn’t stick to one particular platform. 13. How many online shopping orders do you place annually?
14. What is your “Average Cart Value”?
15. What is the maximum cart value you ever shopped?
16. Thinking of the last time you put items in your shopping cart but did not finish the online purchase, which of the following describes why you didn’t complete the transaction? 17. Which device do you use for Online Shopping?
18. Which of the following value-addition services do you like the most on online shopping platform?
19. Which factors enhance your convenience for online shopping? Why do you prefer Offline shopping over Online Shopping? 20. Will you be more attracted to the platform with content in the native language? 21. Which payment option do you prefer the most while making an online purchase?
22. Do you prefer pre-online payment if there is an extra discount on it?
23. Will you buy a subscription service offered by an online shopping platform that includes one-day delivery, exclusive deals, and offers, video streaming?

Tasks: The following tasks can be achieved on the dataset: 1. Customer profiling of Online shoppers. What they like and what they don't? 2. Prescriptive analysis to improve online shopping experience 3. Machine Learning model to analyse factors that most influence online shoppers.

Happy Analysing:)))

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Title Asin Main Image Brand Name Description Availability Subcategory Categories Parent Asin Type Product Type Name Model Number Manufacturer Color Size Date First Available Released Model Year Item Model Number Part Number Price Total Reviews Total Ratings Average Rating Features Best Sellers Rank Subcategory Buybox Buybox Seller Id Buybox Is Amazon Images Product URL And more

The variety of products that can be purchased online is continuously growing. Among Indian consumers the two most popular categories for online purchases are ******** and *****. ** percent and ** percent of consumers respectively chose these answers in our representative online survey. The survey was conducted online among 5,463 respondents in India, in 2025. Looking to gain valuable insights about customers of online shops across the globe? Check out our reports about consumers of online shops worldwide. These reports offer the readers a comprehensive overview of customers of eCommerce brands: who they are; what they like; what they think; and how to reach them.

Data description “e-shop clothing 2008”

Variables:

1. YEAR (2008)

========================================================

1. MONTH -> from April (4) to August (8)

========================================================

1. DAY -> day number of the month

========================================================

1. ORDER -> sequence of clicks during one session

========================================================

1. COUNTRY -> variable indicating the country of origin of the IP address with the following categories:

1-Australia 2-Austria 3-Belgium 4-British Virgin Islands 5-Cayman Islands 6-Christmas Island 7-Croatia 8-Cyprus 9-Czech Republic 10-Denmark 11-Estonia 12-unidentified 13-Faroe Islands 14-Finland 15-France 16-Germany 17-Greece 18-Hungary 19-Iceland 20-India 21-Ireland 22-Italy 23-Latvia 24-Lithuania 25-Luxembourg 26-Mexico 27-Netherlands 28-Norway 29-Poland 30-Portugal 31-Romania 32-Russia 33-San Marino 34-Slovakia 35-Slovenia 36-Spain 37-Sweden 38-Switzerland 39-Ukraine 40-United Arab Emirates 41-United Kingdom 42-USA 43-biz (.biz) 44-com (.com) 45-int (.int) 46-net (.net) 47-org (*.org)

========================================================

1. SESSION ID -> variable indicating session id (short record)

========================================================

1. PAGE 1 (MAIN CATEGORY) -> concerns the main product category: 1-trousers 2-skirts 3-blouses 4-sale

========================================================

1. PAGE 2 (CLOTHING MODEL) -> contains information about the code for each product (217 products)

========================================================

1. COLOUR -> colour of product

1-beige 2-black 3-blue 4-brown 5-burgundy 6-gray 7-green 8-navy blue 9-of many colors 10-olive 11-pink 12-red 13-violet 14-white

========================================================

1. LOCATION -> photo location on the page, the screen has been divided into six parts:

1-top left 2-top in the middle 3-top right 4-bottom left 5-bottom in the middle 6-bottom right

========================================================

1. MODEL PHOTOGRAPHY -> variable with two categories:

1-en face 2-profile

========================================================

1. PRICE -> price in US dollars

========================================================

1. PRICE 2 -> variable informing whether the price of a particular product is higher than the average price for the entire product category

1-yes 2-no

========================================================

1. PAGE -> page number within the e-store website (from 1 to 5)

++++++++++++++++++++++++++++++++++++++++++++++++++++++++

I want to know how to solve this data regarding any problem (clustering, regression, classification, EDA)

Source: https://archive.ics.uci.edu/ml/datasets/clickstream+data+for+online+shopping

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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.

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In the fourth quarter of 2024, TikTok generated around 186 million downloads from users worldwide. Initially launched in China first by ByteDance as Douyin, the short-video format was popularized by TikTok and took over the global social media environment in 2020. In the first quarter of 2020, TikTok downloads peaked at over 313.5 million worldwide, up by 62.3 percent compared to the first quarter of 2019.

              TikTok interactions: is there a magic formula for content success?

              In 2024, TikTok registered an engagement rate of approximately 4.64 percent on video content hosted on its platform. During the same examined year, the social video app recorded over 1,100 interactions on average. These interactions were primarily composed of likes, while only recording less than 20 comments per piece of content on average in 2024.
              The platform has been actively monitoring the issue of fake interactions, as it removed around 236 million fake likes during the first quarter of 2024. Though there is no secret formula to get the maximum of these metrics, recommended video length can possibly contribute to the success of content on TikTok.
              It was recommended that tiny TikTok accounts with up to 500 followers post videos that are around 2.6 minutes long as of the first quarter of 2024. While, the ideal video duration for huge TikTok accounts with over 50,000 followers was 7.28 minutes. The average length of TikTok videos posted by the creators in 2024 was around 43 seconds.

              What’s trending on TikTok Shop?

              Since its launch in September 2023, TikTok Shop has become one of the most popular online shopping platforms, offering consumers a wide variety of products. In 2023, TikTok shops featuring beauty and personal care items sold over 370 million products worldwide.
              TikTok shops featuring womenswear and underwear, as well as food and beverages, followed with 285 and 138 million products sold, respectively. Similarly, in the United States market, health and beauty products were the most-selling items,
              accounting for 85 percent of sales made via the TikTok Shop feature during the first month of its launch. In 2023, Indonesia was the market with the largest number of TikTok Shops, hosting over 20 percent of all TikTok Shops. Thailand and Vietnam followed with 18.29 and 17.54 percent of the total shops listed on the famous short video platform, respectively.

This dataset includes bibliographic information for 501 papers that were published from 2010-April 2017 (time of search) and use online biodiversity databases for research purposes. Our overarching goal in this study is to determine how research uses of biodiversity data developed during a time of unprecedented growth of online data resources. We also determine uses with the highest number of citations, how online occurrence data are linked to other data types, and if/how data quality is addressed. Specifically, we address the following questions:

1.) What primary biodiversity databases have been cited in published research, and which

databases have been cited most often?

2.) Is the biodiversity research community citing databases appropriately, and are

the cited databases currently accessible online?

3.) What are the most common uses, general taxa addressed, and data linkages, and how

have they changed over time?

4.) What uses have the highest impact, as measured through the mean number of citations

per year?

5.) Are certain uses applied more often for plants/invertebrates/vertebrates?

6.) Are links to specific data types associated more often with particular uses?

7.) How often are major data quality issues addressed?

8.) What data quality issues tend to be addressed for the top uses?

Relevant papers for this analysis include those that use online and openly accessible primary occurrence records, or those that add data to an online database. Google Scholar (GS) provides full-text indexing, which was important to identify data sources that often appear buried in the methods section of a paper. Our search was therefore restricted to GS. All authors discussed and agreed upon representative search terms, which were relatively broad to capture a variety of databases hosting primary occurrence records. The terms included: “species occurrence” database (8,800 results), “natural history collection” database (634 results), herbarium database (16,500 results), “biodiversity database” (3,350 results), “primary biodiversity data” database (483 results), “museum collection” database (4,480 results), “digital accessible information” database (10 results), and “digital accessible knowledge” database (52 results)--note that quotations are used as part of the search terms where specific phrases are needed in whole. We downloaded all records returned by each search (or the first 500 if there were more) into a Zotero reference management database. About one third of the 2500 papers in the final dataset were relevant. Three of the authors with specialized knowledge of the field characterized relevant papers using a standardized tagging protocol based on a series of key topics of interest. We developed a list of potential tags and descriptions for each topic, including: database(s) used, database accessibility, scale of study, region of study, taxa addressed, research use of data, other data types linked to species occurrence data, data quality issues addressed, authors, institutions, and funding sources. Each tagged paper was thoroughly checked by a second tagger.

The final dataset of tagged papers allow us to quantify general areas of research made possible by the expansion of online species occurrence databases, and trends over time. Analyses of this data will be published in a separate quantitative review.

About

This file contains purchase data from April 2020 to November 2020 from a large home appliances and electronics online store.

Each row in the file represents an event. All events are related to products and users. Each event is like many-to-many relation between products and users.

Data collected by Open CDP project. Feel free to use open source customer data platform.

More datasets

Checkout another datasets:

1. https://www.kaggle.com/mkechinov/ecommerce-behavior-data-from-multi-category-store
2. https://www.kaggle.com/mkechinov/ecommerce-purchase-history-from-electronics-store - you're reading it right now
3. https://www.kaggle.com/mkechinov/ecommerce-events-history-in-cosmetics-shop
4. https://www.kaggle.com/mkechinov/ecommerce-purchase-history-from-jewelry-store
5. https://www.kaggle.com/mkechinov/ecommerce-events-history-in-electronics-store
6. [NEW] https://www.kaggle.com/datasets/mkechinov/direct-messaging

Multiple purchases per session

A session can have multiple purchase events. It's ok, because it's a single order.

Many thanks

Thanks to REES46 Marketing Platform for this dataset.

Using datasets in your works, books, education materials

You can use this dataset for free. Just mention the source of it: link to this page and link to REES46 Marketing Platform.

A summary of the FCA's work to tackle consumer harm in the investment market, between 1 January and 31 October 2020. Most of the consumer investment market meets the goals of retail investors. But there are some areas where the market is causing real consumer harm. Some of the most serious harms the FCA sees relates to investments outside its regulatory perimeter and online scams, many based overseas. The FCA is also seeing a concerted and conscious effort to promote higher risk investments that are not appropriate for the people being targeted. Most retail investors’ needs can and should be met by more straightforward, mass-market investments. The FCA research shows that consumers, particularly the more vulnerable, are less likely to understand the risks, read the small print and don’t know what to do when things go wrong. These consumers can also often neither afford the loss nor recover from it. Preventing this consumer harm is a key focus for us. The FCA seeks to prevent dishonest firms and individuals entering the perimeter and to act against firms and individuals we authorise who cause consumers harm. The FCA's Business Plan 2020/21 sets out its aim to change this market so that consumers have access to investment products that are appropriate, they can make effective decisions and the firms it authorises operate to high standards. The FCA aims to deliver a consumer investment market that works well for the millions of people who stand to benefit from it and the businesses for which it provides essential funding. As part of this, the FCA's Call for Input (CFI) on Consumer Investments looks at areas where the consumer investment market is not working well for customers and seeks views on what changes it can make to improve protections and outcomes in this market. What products or services fall under the FCA’s remit, and to what extent, is a complex question. The FCA's Perimeter Report sets out its assessment of some of the areas where the boundary between what it regulates and what it doesn't regulate can cause acute consumer harm. Because its powers for investigation and enforcement are sometimes limited in these areas, the FCA works closely with other regulatory and law enforcement agencies who can often be better placed to take action. Like other public services, the FCA makes difficult choices about where to prioritise its efforts to reduce consumer harm. This publication – the first in a regular series – offers greater transparency about the FCA's activities. It focuses on the period between 1 January and 31 October 2020.

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