Dataset: Online Shopping Dataset;

CustomerID

Description: Unique identifier for each customer. Data Type: Numeric;

Gender:

Description: Gender of the customer (e.g., Male, Female). Data Type: Categorical;

Location:

Description: Location or address information of the customer. Data Type: Text;

Tenure_Months:

Description: Number of months the customer has been associated with the platform. Data Type: Numeric;

Transaction_ID:

Description: Unique identifier for each transaction. Data Type: Numeric;

Transaction_Date:

Description: Date of the transaction. Data Type: Date;

Product_SKU:

Description: Stock Keeping Unit (SKU) identifier for the product. Data Type: Text;

Product_Description:

Description: Description of the product. Data Type: Text;

Product_Category:

Description: Category to which the product belongs. Data Type: Categorical;

Quantity:

Description: Quantity of the product purchased in the transaction. Data Type: Numeric;

Avg_Price:

Description: Average price of the product. Data Type: Numeric;

Delivery_Charges:

Description: Charges associated with the delivery of the product. Data Type: Numeric;

Coupon_Status:

Description: Status of the coupon associated with the transaction. Data Type: Categorical;

GST:

Description: Goods and Services Tax associated with the transaction. Data Type: Numeric;

Date:

Description: Date of the transaction (potentially redundant with Transaction_Date). Data Type: Date;

Offline_Spend:

Description: Amount spent offline by the customer. Data Type: Numeric;

Online_Spend:

Description: Amount spent online by the customer. Data Type: Numeric;

Month:

Description: Month of the transaction. Data Type: Categorical;

Coupon_Code:

Description: Code associated with a coupon, if applicable. Data Type: Text;

Discount_pct:

Description: Percentage of discount applied to the transaction. Data Type: Numeric;

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.

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)

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

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.

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.

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.

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

Percentage of individuals who shopped online and percentage of online shoppers by type of good and service purchased over the Internet during the past 12 months.

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.

Online Shopping Consumer Behavior Dataset

Consumer Buying Patterns in E-Commerce

By Weitong Li [source]

About this dataset

This dataset is a rich compilation of data that thoroughly guides us through consumers' behavior and their buying intentions while engaged in online shopping. It has been constructed with immense care to ensure it effectively examines an array of factors that influence customers' purchasing intentions in the increasingly significant realm of digital commerce.

The dataset is exhaustively composed with careful attention to collecting a diverse set of information, thus allowing a broad view into what affects online shopping behavior. Specific columns included cover customer's existing awareness about the website or source from where they are shopping, their information regarding the products they wish to purchase, and more importantly, their satisfaction level related to previous purchases.

Additionally, the dataset delves deep into investigating both objective and subjective aspects impacting customer behavior online. As such, it includes data on various webpage factors like loading speed, user-friendly interface design, webpage aesthetics, etc., which could significantly persuade the consumer's decision-making process during online shopping. The completion and submission convenience provided by those websites also form part of this database.

In order to fully understand consumer behavior within an online environment from multiple facets', individual consumers' subjective views are also captured in this dataset; it explores how consumers perceive their trust towards an e-commerce site or if they believe it’s convenient for them to shop via these platforms versus traditional methods? Do they feel relaxed when doing so?

In recognizing how crucial products competitiveness within such landscapes influences buyer intention - columns that provide details on critical characteristics like price comparisons against offline stores or similar product competitors across different websites have been included too.

Overall this comprehensive aggregated data collection aims not only at understanding fundamental consumer preferences but also towards predicting future buying behaviors hence forth enabling businesses capitalize on emerging trends within online retail spaces more efficiently & profitably

How to use the dataset

In an online-focused world, understanding consumer behavioral data is crucial. The 'Online Shopping Purchasing Intention Dataset' provides a comprehensive collection of consumer-based insights based on their behavior in virtual shopping environments. This dataset explores various factors that might affect a customer's decision to purchase. Here's how you can harness this dataset:

Defining the Problem

Identify a problem or question this data may answer. This might be: understanding what factors influence buying decisions, predicting whether a visit will result in a purchase based on user behavior, analyzing the impact of the month, operating system or traffic type on online purchasing intention etc.

Data Exploration

Understand the structure of the dataset by getting to know each variable and its meaning: - Administrative: Counting different types of pages visited by the user in that session. - Informational & Product Related: Measures how many informational/product related pages are viewed. - Bounce Rates, Exit Rate, Page Values: Assess these metrics as they provide significant insight about visitor activity. - Special Day: Explore correlation between proximity to special days (like Mother’s day and Valentine’s Day) with transactions. - Operating Systems / Browser / Region / Traffic Type: Uncover behavioral patterns associated with technical specs/geo location/ source of traffic.

Analysis and Visualization

Use appropriate statistical analysis techniques to scrutinize relationships between variables such as correlation analysis or chi-square tests for independence etc.

Visualize your findings using plots like bar graphs for categorical features comparison or scatter plots for multivariate relationships etc.

Model Building

Use machine learning algorithms (like logistic regression or decision tree models) potentially useful if your goal is predicting purchase intention based on given features.

This could also involve feature selection - choosing most relevant predictors; training & testing model and finally assessing model performance through metrics like accuracy score, precision-recall scores etc.

Remember to appropriately handle missing values if any before diving into predictive modeling

The comprehens...

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

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For more datasets, click here.

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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...

Graph and download economic data for E-Commerce Retail Sales as a Percent of Total Sales (ECOMPCTSA) from Q4 1999 to Q2 2025 about e-commerce, retail trade, percent, sales, retail, and USA.

The Online Shopping Behavior Dataset contains records of 999 individuals, providing insights into their purchasing habits, spending patterns, and platform preferences. It includes demographic details such as age group (19-30, 31-50) and gender (Male, Female), along with the preferred e-commerce platform (Amazon, Flipkart, Myntra, etc.). The dataset also captures average monthly spending (INR), categorized as "1000-5000," "5000-10000," or "10000+," as well as the device used for shopping (Laptop, Tablet, etc.). Additionally, it records payment methods (UPI, Cash on Delivery, etc.), purchase frequency (Daily, Weekly, Monthly), and the return rate (%) of purchases. A key feature of this dataset is the most purchased category, which highlights the type of products consumers buy most frequently, such as Electronics, Clothing, or Groceries. This dataset is valuable for businesses looking to analyze consumer behavior, optimize marketing strategies, and enhance customer engagement. Researchers and data analysts can use it for trend analysis, customer segmentation, and predictive modeling, making it an excellent resource for e-commerce analytics and decision-making.

The dataset "isoc_ec_ibhv" has been discontinued since 08/02/2024.

In 2024, global retail e-commerce sales reached an estimated ************ U.S. dollars. Projections indicate a ** percent growth in this figure over the coming years, with expectations to come close to ************** dollars by 2028. World players Among the key players on the world stage, the American marketplace giant Amazon holds the title of the largest e-commerce player globally, with a gross merchandise value of nearly *********** U.S. dollars in 2024. Amazon was also the most valuable retail brand globally, followed by mostly American competitors such as Walmart and the Home Depot. Leading e-tailing regions E-commerce is a dormant channel globally, but nowhere has it been as successful as in Asia. In 2024, the e-commerce revenue in that continent alone was measured at nearly ************ U.S. dollars, outperforming the Americas and Europe. That year, the up-and-coming e-commerce markets also centered around Asia. The Philippines and India stood out as the swiftest-growing e-commerce markets based on online sales, anticipating a growth rate surpassing ** percent.

Dataset Card for Online Shoppers Purchasing Intention Dataset

  Dataset Summary

This dataset is a reupload of the Online Shoppers Purchasing Intention Dataset from the UCI Machine Learning Repository.

NOTE: The information below is from the original dataset description from UCI's website.

  Overview

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… See the full description on the dataset page: https://huggingface.co/datasets/jlh/uci-shopper.

Dataset

This dataset was created by Tanya

Contents

This dataset contains longitudinal purchases data from 5027 Amazon.com users in the US, spanning 2018 through 2022: amazon-purchases.csv It also includes demographic data and other consumer level variables for each user with data in the dataset. These consumer level variables were collected through an online survey and are included in survey.csv fields.csv describes the columns in the survey.csv file, where fields/survey columns correspond to survey questions. The dataset also contains the survey instrument used to collect the data. More details about the survey questions and possible responses, and the format in which they were presented can be found by viewing the survey instrument. A 'Survey ResponseID' column is present in both the amazon-purchases.csv and survey.csv files. It links a user's survey responses to their Amazon.com purchases. The 'Survey ResponseID' was randomly generated at the time of data collection. amazon-purchases.csv Each row in this file corresponds to an Amazon order. Each such row has the following columns: Survey ResponseID Order date Shipping address state Purchase price per unit Quantity ASIN/ISBN (Product Code) Title Category The data were exported by the Amazon users from Amazon.com and shared by users with their informed consent. PII and other information not listed above were stripped from the data. This processing occurred on users' machines before sharing with researchers.

E-commerce adoption in Israel shows significant variation across age groups, with the 25-44 year demographic the most prolific shoppers. In 2023, 69.2 percent of this age group engaged in online shopping in the previous three months, highlighting the strong digital commerce presence among younger adults. This trend aligns with broader internet usage patterns in the country, where Israelis spend considerable time online for various activities. Demographic profile of e-commerce shoppers The data reveals a clear generational divide in e-commerce participation. Purchase frequency decreased with age, with those 75 and older showing the lowest participation at just under 20 percent. Other factors associated with higher shopping engagement were income level and religious and ethnic background. For example, among adults belonging to higher income brackets, almost two-thirds were found to have completed a recent online purchase. Internet usage and e-commerce potential The robust e-commerce engagement among younger adults correlates with overall internet usage in Israel. Israelis spend an average of seven hours and 25 minutes online daily, with mobile devices accounting for a significant portion of this time. This extensive internet usage provides an important factor in e-commerce growth, particularly as users become more comfortable with online transactions across various platforms. High internet usage in the market demonstrated the potential for targeted marketing and product discovery in the e-commerce space.

Snapshot img

Poland E-Commerce Market Size 2024-2028

The Poland e-commerce market size is forecast to increase by USD 46.9 billion at a CAGR of 20.5% between 2023 and 2028.

The market is significantly driven by the availability of multiple payment options. Offering diverse methods such as credit cards, debit cards, bank transfers, online wallets, and cash on delivery provides Polish consumers with flexibility and convenience in their online purchases. This accessibility to varied payment choices not only enhances the shopping experience but also encourages more people to engage in e-commerce payment, thereby fueling market growth. 
The market showcases dynamic growth, driven by various sectors and factors. With a strong presence in the fashion industry and an expanding showroom culture, Poland contributes significantly to the worldwide growth rate of e-commerce sales. From electronics to furniture and homeware, the market caters to diverse consumer needs, encompassing hobby, leisure, and care product segments. As eCommerce continues to thrive, Poland emerges as a pivotal player in the global digital marketplace, offering a wide array of products and services to online shoppers. This market research and growth report includes in-depth information about key market drivers, trends, and challenges. 

What will be the Size of the Market During the Forecast Period?

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The market has been experiencing significant growth in recent years. According to the latest reports, the E-Commerce sector in Poland is expected to show a CAGR of 12.5% between 2021 and 2026. This growth can be attributed to several factors, including the increasing popularity of online shopping, the growing number of internet users, and the entry of global players into the Polish market. The Retail sector in Poland is one of the largest contributors to the E-Commerce market, with sales expected to reach €22.5 billion by 2026. E-Commerce platforms like Allegro, Amazon, and eBay have a strong presence in the Polish market, offering a wide range of products and services.
Additionally, the use of technologies like Artificial Intelligence and Machine Learning is also on the rise, helping to improve the customer experience and drive sales. The ECDB (European Commission Database) reports that the number of E-Commerce users in Poland is expected to reach 18.5 million by 2026, making it an attractive market for businesses looking to expand their online presence. The use of mobile devices for shopping is also increasing, with over 50% of E-Commerce transactions in Poland being made on mobile devices. In conclusion, the market is growing rapidly, driven by increasing internet penetration, the popularity of online shopping, and the entry of global players. The Retail sector, particularly Fashion and Footwear, is expected to see significant growth in the coming years. The use of advanced technologies and the increasing number of E-Commerce users make Poland an attractive market for businesses looking to expand their online presence.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  B2B
  B2C


Application

  Home appliances
  Fashion products
  Groceries
  Books
  Others


Geography

  Poland

By Type Insights

The B2B segment is estimated to witness significant growth during the forecast period.

The eCommerce market in Poland is experiencing robust growth, driven by the expansion of business reach for B2B companies in a cost-effective manner. This trend is particularly notable in sectors such as Hobby & Leisure, Electronics, Furniture & Homeware, DIY, Care Products, Fashion, and Grocery. The competitive rivalry among companies is intensifying, with logistics companies playing a crucial role in ensuring efficient delivery. The worldwide growth rate of global eCommerce sales is anticipated to continue, making Poland an attractive market for companies seeking to expand their reach. The ECDB (Electronic Data Interchange for Administration, Commerce and Transport in Europe) is facilitating cross-border sales, further fueling growth in the B2B segment.

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The B2B segment was valued at USD 9.22 billion in 2018 and showed a gradual increase during the forecast period.

Market Dynamics

Our researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

What are the key market drivers leading to the rise in adoption of Poland E-Commerce Market?

The advantages of e-commerce platforms are the key