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;

pritamdeb68/Online-Shopping-Product-Catalog dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Tanya

Contents

1) Data Introduction • The Clickstream Data for Online Shopping is an e-commerce analysis dataset that summarizes user clickstream, product information, country, price, and other session-specific behavior data from April to August 2008 at an online shopping mall specializing in maternity clothing.

2) Data Utilization (1) Clickstream Data for Online Shopping has characteristics that: • Each row contains 14 key variables: year, month, day, click order, country (by access IP), session ID, main category, product code, color, photo location, model photo type, price, category average price, page number, etc. • Data is configured to enable analysis of various consumer behaviors such as click flows for each session, product attributes, and country-specific access patterns. (2) Clickstream Data for Online Shopping can be used to: • Online Shopping Mall User Behavior Analysis: Using clickstream, session, and product information, you can analyze purchase conversion routes, popular products, and behavioral patterns by country and category. • Improve marketing strategies and UI/UX: analyze the relationship between product photo location, color, price, etc. and click behavior and apply to establish effective marketing strategies and improvement of shopping mall UI/UX.

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)

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

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3. DAY -> day number of the month

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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

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

(217 products)

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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)

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

The behavioral and ERP Data of online shopping festival experiment

Online shopping data set for Prof KM Makhitha; Department of Marketing and Retail Management; College of Economic and Management Sciences; University of South Africa.

Online shopping is the process of purchasing goods or services over the internet. It allows customers to browse various products, compare prices, and make purchases conveniently from their home or anywhere else. This mode of shopping has become increasingly popular due to its ease, speed, and accessibility, transforming the traditional retail experience into a digital one. Advantages of Online Shopping: Saves Time and Effort: No need to travel or spend hours looking for items. Better Deals: Access to exclusive online discounts and sales. Variety: Offers access to products from different regions or countries. Ease of Comparison: Price, features, and user reviews are easy to compare. Disadvantages of Online Shopping: Lack of Physical Inspection: Customers cannot touch or try the product before purchasing. Shipping Delays: Delivery times may vary, and there can be delays in some cases. Scams and Fraud: There is a risk of counterfeit products or unreliable sellers. Return Policies: Returning items may be more complicated than in physical stores. 1. Flipkart Overview: Flipkart is one of India’s largest e-commerce platforms, offering a wide range of products, including electronics, clothing, home appliances, books, and groceries. It is known for its competitive pricing, frequent sales, and a vast selection of brands. Strengths: Strong electronics section, excellent customer service, reliable delivery network, and regular sales like the "Big Billion Days." Popular For: Smartphones, gadgets, home appliances, and fashion. 2. Amazon India Overview: Amazon is a global e-commerce giant that offers an extensive range of products across various categories, including electronics, fashion, home essentials, books, and groceries. It is renowned for its fast delivery service (Prime) and reliable return policies. Strengths: Fast delivery (Amazon Prime), a wide variety of products, user reviews and ratings, and excellent customer support. Popular For: Electronics, books, daily essentials, and fashion items. 3. Meesho Overview: Meesho is an online platform focusing on affordable fashion, home decor, and lifestyle products. It caters mainly to budget-conscious shoppers and small business owners who use the platform for reselling purposes. Strengths: Low-cost products, deals on bulk purchases, suitable for small resellers. Popular For: Affordable fashion, home decor, and beauty products. 4. Myntra Overview: Myntra is a popular fashion e-commerce site in India, known for its wide selection of clothing, footwear, accessories, and beauty products. It hosts both high-end brands and affordable options, making it suitable for diverse customers. Strengths: Extensive fashion collection, premium brand offerings, regular sales, and a user-friendly app. Popular For: Fashion apparel, footwear, and beauty products. 5. Ajio Overview: Ajio, owned by Reliance, is a fashion-focused e-commerce platform with a curated collection of Indian and international brands. It offers trendy apparel, footwear, and accessories, with a mix of casual, ethnic, and high-street fashion. Strengths: Unique collection, regular discounts, and trendy ethnic wear. Popular For: Fashion-forward clothing, ethnic wear, and accessories. 6. Nykaa Overview: Nykaa is a leading online platform in India for beauty and wellness products. It offers a range of makeup, skincare, haircare, fragrances, and wellness products, including both Indian and international brands. Strengths: Large variety of beauty products, trusted for authentic brands, beauty advice, and reviews. Popular For: Cosmetics, skincare, and haircare products. 7. Westside Overview: Westside is a retail brand by the Tata Group, offering contemporary fashion for men, women, and children. Its online platform mirrors the quality of its brick-and-mortar stores, with a focus on exclusive, in-house collections. Strengths: Quality fashion, contemporary designs, affordable pricing. Popular For: Casual and formal wear, accessories, and home decor. 8. Lifestyle Overview: Lifestyle is an e-commerce and retail brand that offers fashion apparel, footwear, accessories, and home furnishings. It caters to mid-range and premium customers with a mix of in-house and branded products. Strengths: Wide variety, trusted brands, in-store experience replicated online. Popular For: Clothing, footwear, accessories, and home products. 9. Snapdeal Overview: Snapdeal is a general e-commerce platform in India, known for offering budget-friendly products across multiple categories, including fashion, electronics, home, and lifestyle. Strengths: Affordable pricing, variety of products, frequent discounts. Popular For: Budget-friendly electronics, apparel, and lifestyle products. 10. Max Fashion Overview: Max is a fashion brand offering value-for-money clothing and accessories for men, women, and children. Its online store provides an array of trendy yet ...

Employment statistics on the Online Shopping industry in China

Number of Businesses statistics on the Online Shopping industry in China

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.

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

About Dataset

📑 The structure of the online_shop dataset consists of interconnected tables that simulate a real-world e-commerce platform. Each table represents a key aspect of the business, such as products, orders, customers, suppliers, and reviews. Below is a detailed breakdown of each table and its columns:

🛍️ The "orders" table includes the following columns:

• order_id: A unique identifier for each order.
• order_date: The date when the order was placed.
• customer_id: A reference to the customer who placed the order (linked to the customers table).
• total_price: The total cost of the order, calculated as the sum of all items in the order.

👩‍💼 The "customers" table includes the following columns:

• customer_id: A unique identifier for each customer.
• first_name: The customer's first name.
• last_name: The customer's last name.
• address: The address of the customer.
• email: The email address of the customer (unique for each customer).
• phone_number: The phone number of the customer.

🛒 The "products" table includes the following columns:

• product_id: A unique identifier for each product.
• product_name: The name of the product.
• category: The category to which the product belongs (e.g., Electronics, Home & Kitchen).
• price: The price of the product.
• supplier_id: A reference to the supplier providing the product (linked to the suppliers table).

📦 The "order_items" table includes the following columns:

• order_item_id: A unique identifier for each item in an order.
• order_id: A reference to the order containing the item (linked to the orders table).
• product_id: A reference to the product being ordered (linked to the products table).
• quantity: The quantity of the product ordered.
• price_at_purchase: The price of the product at the time of the order.

🏢 The "suppliers" table includes the following columns:

• supplier_id: A unique identifier for each supplier.
• supplier_name: The name of the supplier.
• contact_name: The name of the contact person at the supplier.
• address: The address of the supplier.
• phone_number: The phone number of the supplier.
• email: The email address of the supplier.

🌟 The "reviews" table includes the following columns:

• review_id: A unique identifier for each product review.
• product_id: A reference to the product being reviewed (linked to the products table).
• customer_id: A reference to the customer who wrote the review (linked to the customers table).
• rating: The rating given to the product (1-5, where 5 is the best).
• review_text: The text content of the review.
• review_date: The date when the review was written.

💳 The "payments" table includes the following columns:

• payment_id: A unique identifier for each payment.
• order_id: A reference to the order being paid for (linked to the orders table).
• payment_method: The method of payment (e.g., Credit Card, PayPal).
• payment_date: The date when the payment was made.
• amount: The amount of the payment.
• transaction_status: The status of the payment (e.g., Pending, Completed, Failed).

🚚 The "shipments" table includes the following columns:

• shipment_id: A unique identifier for each shipment.
• order_id: A reference to the order being shipped (linked to the orders table).
• shipment_date: The date when the shipment was dispatched.
• carrier: The company responsible for delivering the shipment.
• tracking_number: The tracking number for the shipment.
• delivery_date: The date when the shipment was delivered (if applicable).
• shipment_status: The status of the shipment (e.g., Pending, Shipped, Delivered, Cancelled).

This dataset provides a comprehensive simulation of an e-commerce platform, covering everything from customer orders to supplier relationships, payments, shipments, and customer reviews. It is an excellent resource for practicing SQL, understanding relational databases, or performing data analysis and machine learning tasks.