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)

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

A league table of the 120 cryptocurrencies with the highest market cap reveals how diverse each crypto is and potentially how much risk is involved when investing in one. Bitcoin (BTC), for instance, had a so-called "high cap" - a market cap worth more than 10 billion U.S. dollars - indicating this crypto project has a certain track record or, at the very least, is considered a major player in the cryptocurrency space. This is different in Decentralize Finance (DeFi), where Bitcoin is only a relatively new player. A concentrated market The number of existing cryptocurrencies is several thousands, even if most have a limited significance. Indeed, Bitcoin and Ethereum account for nearly 75 percent of the entire crypto market capitalization. As crypto is relatively easy to create, the range of projects varies significantly - from improving payments to solving real-world issues, but also meme coins and more speculative investments. Crypto is not considered a payment method While often talked about as an investment vehicle, cryptocurrencies have not yet established a clear use case in day-to-day life. Central bankers found that usefulness of crypto in domestic payments or remittances to be negligible. A forecast for the world's main online payment methods took a similar stance: It predicts that cryptocurrency would only take up 0.2 percent of total transaction value by 2027.

In 2024, convenience was the leading reason to spend more money online during Cyber Week than in the previous year. Prices being lower online was the second most common reason for U.S. Cyber Week shoppers.

Global C2C Fashion Store User Behaviour Analysis

Analyzing Buyer and Seller Profiles across Countries

By Jeffrey Mvutu Mabilama [source]

About this dataset

Welcome to an exciting exploration of global C2C fashion store user behaviour! This dataset seeks to serve as a benchmark by providing valuable insights into e-commerce users, enabling you to make informed decisions and effectively grow your business. Let's dive right into the data!

This dataset contains records on over 9 million registered users from a successful online C2C fashion store launched in Europe around 2009 and later expanded worldwide. It includes metrics such as country, gender, active users, top buyers/sellers/ratio*, products bought/sold/listed* and social network features (likes/follows). Furthermore this is just a preview of much larger data set which contains more detailed information including product listings, comments from listed products etc.

E-commerce has become an essential part of our lives - people are now accustomed to buying anything with a few clicks online. With so many unknown elements that come with not only selling but also providing good customer service - understanding user behavior is key for success in this domain. By utilizing this dataset you can answer questions such as 'how many customers are likely to drop off after years of using my service?,' 'are my users active enough compared to those in this dataset?,” or “how likely are people from other countries signing up in a C2C website?' In addition, if you think this kind odf dataset may be useful don't forget do show your support or appreciation by leaving an upvote or comment on the page!

My Telegram bot will answer any queries regarding the datasets as well allow you see contact me directly if necessary; also please don't forget check out the *[data.world page](https://data.world/jfreex/e-commerce-users-of-a-french-c2c

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides a useful overview of global users' behavior in an online C2C fashion store. The data includes metrics such as buyers, top buyers, top buyer ratio, female buyers and their respective ratios, etc., per country. This dataset can be used to gain insights into how global audiences interact with the store and draw conclusions from comparison between different countries.

In order to make use of this dataset, one must first familiarize themselves with the various metrics included in it. These include: country; number of overall buyers; number of top buyers; ratio(s) of them (top buyer to total buyer); female-related data (buyers, top female buyers); bought-to-wish/like ration (top and non-top separately); overall products bought/wished/liked; total products sold by tops sellers in the same country versus what they sold outside the country; mean value for product stats (sold/listed/etc...) from looking at the whole population or just users that make those actions multiple times; average days for user offline /lurking around on the site without posting anything or buying anything etc.; mean follower(s) count(s).

Using this data one could generate reports about user behavior within particular countries either manually by computing all statistics or by using libraries like Pandas or SQL with queries made toward this datasets which consists of columns representing individual countries with all values necessary to answer any questions you might have regarding how many people buy something out there per region and what type they are –– Are they Top Buyer? Female? Etc.

Further potential work could involve utilising machine learning tools such as clustering algorithms to group similar customers together based on certain traits like age group, profession etc., so that personalised marketing promotions can be targetted at these customer clusters rather than aiming more generic ads at everyone!

Finally combined with other related product datasets which is available upon request via JfreexDatasets_bot provided by Jfreex team , this dataset can become another powerful tool providing you actionable insights into customers today — allowing you build better strategies towards improving customer experience tomorrow!

Research Ideas

• Analyzing the conversion rate of users on a website - Comparing user metrics like the overall number of buyers, female buyers, top buyers ratio and top buyer gender can help determine if users in certain countries are more or less likely to convert into customers. Additionally, comparing average metrics like products bought or offl...

This collection automatically includes metadata, the source of which is the GOVERNMENT OF THE REPUBLIC OF SLOVENIA STATISTICAL USE OF THE REPUBLIC OF SLOVENIA and corresponding to the source database entitled “Number of individuals by motive for online shopping between e-buyers who have made an online purchase in the last 3 months, the degree of urbanisation of the area in which these individuals live, Slovenia, 2022”.

Actual data are available in Px-Axis format (.px). With additional links, you can access the source portal page for viewing and selecting data, as well as the PX-Win program, which can be downloaded free of charge. Both allow you to select data for display, change the format of the printout, and store it in different formats, as well as view and print tables of unlimited size, as well as some basic statistical analyses and graphics.

For 2024's Black Friday and Cyber Monday sales event, also known as the 'Cyber Week', approximately 77 percent of shoppers in the United States that planned to visit online retailers during Cyber Week specifically intended to buy clothing and accessories, making it the most popular product category. Just over 70 percent of respondents also planned to buy electronics.

Dataset

This dataset was created by Tanya

Contents

More than half of the interviewed Turkish people were somewhat concerned about sharing their personal data requested when shopping on websites or apps. On the other hand, ** percent of the respondents stated they were not concerned about their personal data when shopping online at all.

Context

E-commerce has become a new channel to support businesses development. Through e-commerce, businesses can get access and establish a wider market presence by providing cheaper and more efficient distribution channels for their products or services. E-commerce has also changed the way people shop and consume products and services. Many people are turning to their computers or smart devices to order goods, which can easily be delivered to their homes.

Content

This is a sales transaction data set of UK-based e-commerce (online retail) for one year. This London-based shop has been selling gifts and homewares for adults and children through the website since 2007. Their customers come from all over the world and usually make direct purchases for themselves. There are also small businesses that buy in bulk and sell to other customers through retail outlet channels.

The data set contains 500K rows and 8 columns. The following is the description of each column. 1. TransactionNo (categorical): a six-digit unique number that defines each transaction. The letter “C” in the code indicates a cancellation. 2. Date (numeric): the date when each transaction was generated. 3. ProductNo (categorical): a five or six-digit unique character used to identify a specific product. 4. Product (categorical): product/item name. 5. Price (numeric): the price of each product per unit in pound sterling (£). 6. Quantity (numeric): the quantity of each product per transaction. Negative values related to cancelled transactions. 7. CustomerNo (categorical): a five-digit unique number that defines each customer. 8. Country (categorical): name of the country where the customer resides.

There is a small percentage of order cancellation in the data set. Most of these cancellations were due to out-of-stock conditions on some products. Under this situation, customers tend to cancel an order as they want all products delivered all at once.

Inspiration

Information is a main asset of businesses nowadays. The success of a business in a competitive environment depends on its ability to acquire, store, and utilize information. Data is one of the main sources of information. Therefore, data analysis is an important activity for acquiring new and useful information. Analyze this dataset and try to answer the following questions. 1. How was the sales trend over the months? 2. What are the most frequently purchased products? 3. How many products does the customer purchase in each transaction? 4. What are the most profitable segment customers? 5. Based on your findings, what strategy could you recommend to the business to gain more profit?

Photo by CardMapr on Unsplash

As of June 2025, about ***** million people in China had purchased goods online. This represented a penetration rate of **** percent.E-commerce in ChinaThe past decade has seen rapid growth in the demand for online shopping opportunities in China. The number of online shoppers in China has been increasing exponentially from below ** million in 2006 to over *** million users a decade later, enabling this enormous spurt of China’s e-commerce sector. By 2022, digital buyer penetration rate in China has edged close to ** percent. China has been the world’s second-largest e-tailing market after the U.S. in recent years. As of 2023, the gross merchandise volume of online shopping in China had amounted to around ***** trillion yuan. By 2025, the volume of B2C e-commerce sales in China was expected to surpass *** trillion U.S. dollars. The largest B2C e-commerce retailer in China with regard to gross merchandise volume (GMV) had been Tmall. The B2C online retail platform operated by Alibaba Group had generated a transaction volume of about *** trillion yuan in 2020. The GMV of the leading C2C online retail platform taobao.com, also operated by Alibaba group, had reached almost *** trillion yuan that year.

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.

Context

In the field of e-commerce, the datasets are typically considered as proprietary, meaning they are owned and controlled by individual organizations and are not often made publicly available due to privacy and business considerations. In spite of this, The UCI Machine Learning Repository, known for its extensive collection of datasets beneficial for machine learning and data mining research, has curated and made accessible a unique dataset. This dataset comprises actual transactional data spanning from the year 2010 to 2011. For those interested, the dataset is maintained and readily available on the UCI Machine Learning Repository's site under the title "Online Retail".

Content

The dataset is a transnational one, capturing every transaction made from December 1, 2010, through December 9, 2011, by a UK-based non-store online retail company. As an online retail entity, the company doesn't have a physical store presence, and its operations and sales are conducted purely online. The company's primary product offering includes unique gifts for all occasions. While the company serves a diverse range of customers, a significant number of its clientele includes wholesalers.

Acknowledgements

In collaboration with the UCI Machine Learning Repository, the dataset was provided and made available by Dr. Daqing Chen. Dr. Chen is the Director of the Public Analytics group at London South Bank University, UK. Any correspondence regarding this dataset can be sent to Dr. Chen at 'chend' at 'lsbu.ac.uk'. We are grateful to him for providing such an invaluable resource for researchers and data science enthusiasts.

The image used has been sourced from Canva

Inspiration

The rich and extensive data within this dataset opens the door for a multitude of potential analyses. It lends itself well to various methods and techniques in data science, including but not limited to time series analysis, clustering, and classification. By exploring this dataset, one could derive key insights into customer behavior, transaction trends, and product performance, providing ample opportunities for deep and insightful explorations.

The pie chart showcases the distribution of app/software spending by store category in Nepal, providing insights into how eCommerce stores allocate their resources on the app or software they utilize. Among the store categories, Beauty & Fitness exhibits the highest spending, with a total expenditure of $13.59K units representing 17.00% of the overall spending. Following closely behind is Home & Garden with a spend of $10.75K units, comprising 13.44% of the total. Apparel also contributes significantly with a spend of $9.59K units, accounting for 11.99% of the overall app/software spending. This data sheds light on the investment patterns of eCommerce stores within each category, reflecting their priorities and resource allocation towards app or software solutions.

About this Dataset

This Data provide apparel Retail markets from 2013 - 2017 and forecasts from 2018 - 2022. On account of technological advances, people get used to purchase apparel online. It not only save time but more convenience for customer. This data provides apparel retail data to entrepreneur or the owner of online shop who aims to expand business.

Category

Business Information & Financials

Keywords

Online data,Online Retail

Row Count

56

Price

Free

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

This chart illustrates the estimated sales amounts generated by stores on various platforms within Israel. Magento shows a significant lead, with total sales amounting to $70.67B, which constitutes 98.85% of the region's total sales on platforms. Custom Cart reports sales of $341.96M, accounting for 0.48% of the total platform sales in Israel. WooCommerce also holds a notable share, with its sales reaching $274.14M, representing 0.38% of the overall sales amount. This data provides a comprehensive view of the market dynamics in Israel, highlighting which platforms are driving the most sales.

In the recent years, many Chinese e-commerce platforms, for example TEMU, SHEIN and Alibaba, have been gaining a remarkable traction on the global market. These platforms attract millions of consumers every year, with their affordable prices, trendy fast fashion items, and very aggressive online marketing. According to the latest data, TEMU has around 292 million monthly active users, and their annual sales are estimated at 70.8 billion American dollars (Backlinko, 2025). SHEIN is also a largely popular and used app; in 2024, it was the second most downloaded shopping app worldwide, with approximately 235 million downloads (Analyzify, 2025). However, this growth in popularity raises some important questions about consumer behavior, particularly about the gap between consumers expressed attitudes, which would be the concern for ethical production, sustainability, or fair labor, and their actual purchasing decisions. This phenomenon, known as the attitude-behavior gap, has become increasingly relevant in the digital retail age.Attitude-behavior gap, also known as the value-action gap or the intention-behavior gap, describes the discrepancy between our intentions, or what we plan to achieve, and our actual actions, or what we ultimately do (Ajzen/Fishbein 1975, p. 39). An example of such behavior is when someone claims they care about the environment and says they want to reduce waste but don’t engage in eco-friendly practices in their daily life. This gap happens for many reasons, such as habits, convenience, price, or simply not thinking about it in the moment. (Zhuo/Ren/Zhu 2022, p. 16)The topic of attitude-behavior gap has been explored in many academic publications and other works but mostly in the context of traditional retail and Western fast fashion brands, such as Zara and H&M. There is, however, a lack of focused research on Chinese e-commerce platforms, like SHEIN and TEMU. These platforms operate with unique business models and digital strategies and are therefore a suitable topic for closer examination, especially in relation to consumer perceptions of sustainability, which is a very current topic nowadays.In this thesis the focus will be on the two following companies, TEMU and SHEIN. These two companies are the biggest players on the Chinese e-commerce market and are also among the most popular platforms in the FMCG (Fast-Moving Consumer Goods) sector, as can be seen from the numbers above.The aim of this thesis is to explore the gap in customer behavior, as well as to understand the psychological, social, and economic factors that influence consumer choices, and finding an answer to why conscious intentions often fail to translate into ethical actions online.The research of this master thesis will focus on three central questions. First, it will seek to answer why do customers shop at these platforms (SHEIN and TEMU), and if they care about sustainability while doing so. Second, it will investigate what factors contribute to the attitude-behavior gap. Finally, it will explore what are consumers expressed attitudes towards sustainability in online shopping on these Chinese e-commerce platforms (SHEIN and TEMU).The planned methodology of this thesis focuses on empirical research in the form of a consumer survey. A questionnaire will be developed based on the theory of the attitude-behavior gap, and it will also explore real world shopping behavior in comparison to respondents stated attitudes toward sustainability.The target group will consist of people who shop on platforms, TEMU and SHEIN. Participants will be reached mainly through social media channels (e.g., Instagram, Facebook, LinkedIn), where a link to the questionnaire will be shared, as well as through a paid survey platform called SurveySwap. A portion of the respondents will also be reached through personal contact. The estimated number of participants is between 100 and 150.In this thesis, the focus will be first on providing the context and applicable theories on the topic of attitude-behavior gap. Second, the focus will be on ethical and sustainable consumerism, describing correlation to the topic of the thesis and providing more dept to the topic. The third section will provide an overview of the Chinese e-commerce ecosystem, subsequently the following two chapters will describe and explore the two mentioned Chinese companies, SHEIN and TEMU, and will provide an overview of their environmental and ethical role on the consumers. The last part will present new findings on how attitude-behavior gap effects the consumers shopping on these platforms. A concluding section will derive final insights from the empirical findings

Tunisia number dataset provides millions of powerful contacts for direct marketing. Our List To Data website gives an accurate and active phone numbers library. On the other hand, everybody can get all confirmed contact numbers from our site for any business to communicate with new clients. This Tunisia number dataset creates powerful options for promoting company sales. Likewise, this Tunisia number dataset is highly efficacious for business promotion through cold calls and text messages. For that reason, cell phone lead gives instant feedback from consumers and grows contracts. Our special team presents all number databases to you in CSV or Excel structure. However, anyone can download it in any CRM software without any risk. Tunisia phone data is a very helpful contact library for SMS and telemarketing. Mainly, the marketing tool plays a vital role in future business plans. Even, we prioritize security and privacy, so we strictly adhere to all the GDPR laws. In short, anyone can purchase this without any mistrust from the List To Data website. As a result, buy this contact number dataset for your benefit. Moreover, the Tunisia phone data helps in many ways to earn more money from this country. This country is very wealthy in all those business sectors, so you can buy this number package now. This website is an excellent place for its reputation to collect all authentic client mobile contact numbers. To that end, our skilled team is ready to assist you 24/7 in supplying your necessary leads. Tunisia phone number list makes your business more profitable in a couple of months. This country has the nominal GDP (US$53 billion) and the most vast by purchasing power parity (US$179 trillion). Moreover, it creates a big possibility to earn more from this place. Hence, you can get a consumer contact number lead from us to catch them easily through direct calls or SMS. Also, this Tunisia phone number list is far better for your business activities nationwide. Primarily, people can do the marketing with this enormous group of people. Actually, it will increase your profit rapidly and expand the return on investment [ROI]. Thus, as a businessman, anyone bears your needed B2C sales leads from our website at a cheap cost.

The pie chart showcases the distribution of app/software spending by store category in Lao People's Democratic Republic, providing insights into how eCommerce stores allocate their resources on the app or software they utilize. Among the store categories, Apparel exhibits the highest spending, with a total expenditure of $1.00K units representing 66.71% of the overall spending. Following closely behind is Arts & Entertainment with a spend of $0.00 units, comprising <0.01% of the total. Attractions also contributes significantly with a spend of $0.00 units, accounting for <0.01% of the overall app/software spending. This data sheds light on the investment patterns of eCommerce stores within each category, reflecting their priorities and resource allocation towards app or software solutions.

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.