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)

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

Dataset

This dataset was created by Tanya

Contents

This dataset provides a curated subset of the anonymized Google Analytics event data for three months of the Google Merchandise Store. The full dataset is available as a BigQuery Public Dataset.

The data includes information on items sold in the store and how much money was spent by users over time. It is both comprehensive enough to invite real analysis yet simple enough to facilitate teaching.

Original Data

Acknowledgement

Foto von Arthur Osipyan auf Unsplash

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

📦 Ecommerce Dataset (Products & Sizes Included)

🛍️ Essential Data for Building an Ecommerce Website & Analyzing Online Shopping Trends 📌 Overview This dataset contains 1,000+ ecommerce products, including detailed information on pricing, ratings, product specifications, seller details, and more. It is designed to help data scientists, developers, and analysts build product recommendation systems, price prediction models, and sentiment analysis tools.

🔹 Dataset Features

Column Name Description product_id Unique identifier for the product title Product name/title product_description Detailed product description rating Average customer rating (0-5) ratings_count Number of ratings received initial_price Original product price discount Discount percentage (%) final_price Discounted price currency Currency of the price (e.g., USD, INR) images URL(s) of product images delivery_options Available delivery methods (e.g., standard, express) product_details Additional product attributes breadcrumbs Category path (e.g., Electronics > Smartphones) product_specifications Technical specifications of the product amount_of_stars Distribution of star ratings (1-5 stars) what_customers_said Customer reviews (sentiments) seller_name Name of the product seller sizes Available sizes (for clothing, shoes, etc.) videos Product video links (if available) seller_information Seller details, such as location and rating variations Different variants of the product (e.g., color, size) best_offer Best available deal for the product more_offers Other available deals/offers category Product category

📊 Potential Use Cases

📌 Build an Ecommerce Website: Use this dataset to design a functional online store with product listings, filtering, and sorting. 🔍 Price Prediction Models: Predict product prices based on features like ratings, category, and discount. 🎯 Recommendation Systems: Suggest products based on user preferences, rating trends, and customer feedback. 🗣 Sentiment Analysis: Analyze what_customers_said to understand customer satisfaction and product popularity. 📈 Market & Competitor Analysis: Track pricing trends, popular categories, and seller performance. 🔍 Why Use This Dataset? ✅ Rich Feature Set: Includes all necessary ecommerce attributes. ✅ Realistic Pricing & Rating Data: Useful for price analysis and recommendations. ✅ Multi-Purpose: Suitable for machine learning, web development, and data visualization. ✅ Structured Format: Easy-to-use CSV format for quick integration.

📂 Dataset Format CSV file (ecommerce_dataset.csv) 1000+ samples Multi-category coverage 🔗 How to Use? Download the dataset from Kaggle. Load it in Python using Pandas: python Copy Edit import pandas as pd
df = pd.read_csv("ecommerce_dataset.csv")
df.head() Explore trends & patterns using visualization tools (Seaborn, Matplotlib). Build models & applications based on the dataset!

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...

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.

Purchase_basketValueGross: The total value of the purchase including taxes and any other charges. Purchase_purchaseType: The type of purchase made, such as online, in-store, or pickup. Purchase_overallBasketSavings: The total savings made on the purchase, which could be through discounts, coupons, or other promotions. Purchase_storeId: The unique identifier for the store where the purchase was made. Purchase_paymentType_category: The category of payment type used, such as credit card, debit card, or cash. Purchase_paymentType_amount: The amount of payment made using the payment type specified. Purchase_timeStamp: The date and time when the purchase was made. Purchase_basketValueNet: The total value of the purchase excluding taxes and any other charges. Purchase_says: Any comments or notes related to the purchase. Purchase_storeName: The name of the store where the purchase was made. Purchase_storeFormat: The format of the store where the purchase was made, such as a supermarket, hypermarket, or convenience store. Purchase_product_name: The name of the product purchased. Purchase_product_quantity: The quantity of the product purchased. Purchase_product_channel: The channel through which the product was purchased, such as in-store, online, or through a third-party seller. Purchase_product_price: The price of the product purchased.

Context

The Customer Shopping Preferences Dataset offers valuable insights into consumer behavior and purchasing patterns. Understanding customer preferences and trends is critical for businesses to tailor their products, marketing strategies, and overall customer experience. This dataset captures a wide range of customer attributes including age, gender, purchase history, preferred payment methods, frequency of purchases, and more. Analyzing this data can help businesses make informed decisions, optimize product offerings, and enhance customer satisfaction. The dataset stands as a valuable resource for businesses aiming to align their strategies with customer needs and preferences. It's important to note that this dataset is a Synthetic Dataset Created for Beginners to learn more about Data Analysis and Machine Learning.

Content

This dataset encompasses various features related to customer shopping preferences, gathering essential information for businesses seeking to enhance their understanding of their customer base. The features include customer age, gender, purchase amount, preferred payment methods, frequency of purchases, and feedback ratings. Additionally, data on the type of items purchased, shopping frequency, preferred shopping seasons, and interactions with promotional offers is included. With a collection of 3900 records, this dataset serves as a foundation for businesses looking to apply data-driven insights for better decision-making and customer-centric strategies.

Dataset Glossary (Column-wise)

• Customer ID - Unique identifier for each customer
• Age - Age of the customer
• Gender - Gender of the customer (Male/Female)
• Item Purchased - The item purchased by the customer
• Category - Category of the item purchased
• Purchase Amount (USD) - The amount of the purchase in USD
• Location - Location where the purchase was made
• Size - Size of the purchased item
• Color - Color of the purchased item
• Season - Season during which the purchase was made
• Review Rating - Rating given by the customer for the purchased item
• Subscription Status - Indicates if the customer has a subscription (Yes/No)
• Shipping Type - Type of shipping chosen by the customer
• Discount Applied - Indicates if a discount was applied to the purchase (Yes/No)
• Promo Code Used - Indicates if a promo code was used for the purchase (Yes/No)
• Previous Purchases - The total count of transactions concluded by the customer at the store, excluding the ongoing transaction
• Payment Method - Customer's most preferred payment method
• Frequency of Purchases - Frequency at which the customer makes purchases (e.g., Weekly, Fortnightly, Monthly)

Structure of the Dataset

https://i.imgur.com/6UEqejq.png" alt="">

Acknowledgement

This dataset is a synthetic creation generated using ChatGPT to simulate a realistic customer shopping experience. Its purpose is to provide a platform for beginners and data enthusiasts, allowing them to create, enjoy, practice, and learn from a dataset that mirrors real-world customer shopping behavior. The aim is to foster learning and experimentation in a simulated environment, encouraging a deeper understanding of data analysis and interpretation in the context of consumer preferences and retail scenarios.

Cover Photo by: Freepik

Thumbnail by: Clothing icons created by Flat Icons - Flaticon

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...

Online Retail E-Commerce Data Hey everyone! 👋

This dataset contains real e-commerce transaction data from 2009 to 2011. It comes from a UK-based online store that sells a variety of products. The data includes details like invoices, product codes, descriptions, prices, and even customer IDs.

What’s Inside? Each row represents a transaction, and the dataset has the following key columns: 🛒 Invoice – Unique order ID 📦 StockCode – Product code 📝 Description – Name of the product 📊 Quantity – Number of units sold ⏳ InvoiceDate – When the purchase happened 💰 Price – Unit price of the product 👤 Customer ID – Unique identifier for each customer 🌍 Country – Where the customer is from

Why is this dataset useful? This dataset is great for exploring: Customer Segmentation (Find high-value customers) Customer Lifetime Value (LTV) Analysis Sales & Revenue Trends Market Basket Analysis (Which products are bought together?) Predicting Churn & Retention Strategies

How Can You Use It? If you're into data science, machine learning, or business analytics, this dataset is perfect for hands-on projects. You can analyze customer behavior, predict sales, or even build recommendation systems.

Hope this dataset helps with your projects! Let me know if you find something interesting.

1. InvoiceNo: Invoice number. It appears to be a unique identifier for each transaction.
2. StockCode: Product (item) code.
3. Description: Product (item) description.
4. Quantity: The quantity of the product purchased in the transaction.
5. InvoiceDate: The date and time when the transaction occurred.
6. UnitPrice: Price per unit of the product.
7. CustomerID: Customer identifier.
8. Country: The country where the customer resides.

Let's start with the data types and number of unique values for each column in this sample.

1. InvoiceNo: Data type is int64. There are 2 unique values in our sample.
2. StockCode: Data type is object (indicative of string/text data). There are 9 unique values.
3. Description: Data type is object. There are 9 unique descriptions.
4. Quantity: Data type is int64. There are 3 unique quantity values.
5. InvoiceDate: Data type is datetime64[ns]. There are 2 unique dates in our sample.
6. UnitPrice: Data type is float64. There are 6 unique unit prices.
7. CustomerID: Data type is int64. There's only 1 unique customer ID in this sample.
8. Country: Data type is object. All rows in our sample have the same country (United Kingdom).

CONTEXT

E-commerce (electronic commerce) is the buying and selling of goods and services, or the transmitting of funds or data, over an electronic network, primarily the internet. These business transactions occur either as business-to-business (B2B), business-to-consumer (B2C), consumer-to-consumer or consumer-to-business

CONTENT

This is simple data set of US online_store from 2020.

INSPIRATION

So, the data cames with some questions !!

What was the highest Sale in 2020? What is average discount rate of charis? What are the highest selling months in 2020? What is the Profit Margin for each sales record? How much profit is gained for each product? What is the total Profit & Sales by Sub-Category? People from city/state shop the most? Develop a function, to return a dataframe which is grouped by a particular column (as an input)

If you have wonderful idea about this dataset, welcome to contribute !!! Happy Kaggling, please up-vote if you find this dataset helpful!🖤!

Dataset Description: E-commerce Customer Behavior

Overview: This dataset provides a comprehensive view of customer behavior within an e-commerce platform. Each entry in the dataset corresponds to a unique customer, offering a detailed breakdown of their interactions and transactions. The information is crafted to facilitate a nuanced analysis of customer preferences, engagement patterns, and satisfaction levels, aiding businesses in making data-driven decisions to enhance the customer experience.

Columns:

1. Customer ID:

   • Type: Numeric
   • Description: A unique identifier assigned to each customer, ensuring distinction across the dataset.

2. Gender:

   • Type: Categorical (Male, Female)
   • Description: Specifies the gender of the customer, allowing for gender-based analytics.

3. Age:

   • Type: Numeric
   • Description: Represents the age of the customer, enabling age-group-specific insights.

4. City:

   • Type: Categorical (City names)
   • Description: Indicates the city of residence for each customer, providing geographic insights.

5. Membership Type:

   • Type: Categorical (Gold, Silver, Bronze)
   • Description: Identifies the type of membership held by the customer, influencing perks and benefits.

6. Total Spend:

   • Type: Numeric
   • Description: Records the total monetary expenditure by the customer on the e-commerce platform.

7. Items Purchased:

   • Type: Numeric
   • Description: Quantifies the total number of items purchased by the customer.

8. Average Rating:

   • Type: Numeric (0 to 5, with decimals)
   • Description: Represents the average rating given by the customer for purchased items, gauging satisfaction.

9. Discount Applied:

   • Type: Boolean (True, False)
   • Description: Indicates whether a discount was applied to the customer's purchase, influencing buying behavior.

10. Days Since Last Purchase:

    • Type: Numeric
    • Description: Reflects the number of days elapsed since the customer's most recent purchase, aiding in retention analysis.

11. Satisfaction Level:

    • Type: Categorical (Satisfied, Neutral, Unsatisfied)
    • Description: Captures the overall satisfaction level of the customer, providing a subjective measure of their experience.

Use Cases:

1. Customer Segmentation:

   • Analyze and categorize customers based on demographics, spending habits, and satisfaction levels.

2. Satisfaction Analysis:

   • Investigate factors influencing customer satisfaction and identify areas for improvement.

3. Promotion Strategy:

   • Assess the impact of discounts on customer spending and tailor promotional strategies accordingly.

4. Retention Strategies:

   • Develop targeted retention strategies by understanding the time gap since the last purchase.

5. City-based Insights:

   • Explore regional variations in customer behavior to optimize marketing efforts based on location-specific trends.

Note: This dataset is synthetically generated for illustrative purposes, and any resemblance to real individuals or scenarios is coincidental.

E-Commerce Product Catalogue Description: This dataset, titled "E-Commerce Product Catalogue with IDs," presents a rich collection of product listings sourced from various e-commerce websites. It is scraped from a variety of e-commerce platforms, each provided with a unique identifier. It serves as a valuable resource for educational and research purposes, aimed at facilitating various analyses such as market trends, inventory management, and recommendation system design.

Features:

product_id: A unique identifier for each product. name: The product's name as it appears on the e-commerce site. main_category: The primary classification of the product. sub_category: The more specific category for the product. ratings: The average customer ratings, indicative of product satisfaction. no_of_ratings: The total count of customer ratings, reflecting product engagement. Price: The product's price

E-Commerce Consumer Purchase Patterns Description: "E-Commerce Consumer Purchase Patterns " is a dataset that encapsulates actual customer purchase histories without revealing customer details, from an e-commerce website. This dataset is specifically curated for educational and research use, providing insights into consumer purchase behaviors, trends, and product popularity.

Features:

customer_id: A unique identifier for each customer. product_ids: A list of product IDs purchased by the customer, mirroring real transaction records. user_rating

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 ...

🏢 USA Online Shopping Companies Dataset (2024) This dataset showcases a snapshot of the top-performing online shopping companies in the United States as of 2024. It includes crucial business insights such as company names, their respective industries, annual revenues, growth rates, employee counts, and headquarters locations.

📦 Dataset Overview: Year Covered: 2024

Total Companies: N (replace with actual count)

Columns Included:

Rank – Position based on revenue or market performance

Name – Name of the company

Industry – Type of e-commerce business (e.g., Electronics, Fashion, Retail)

Revenue (USD millions) – Annual revenue in millions of USD

Revenue Growth – Percentage growth compared to the previous year

Employees – Number of employees in the company

Headquarters – City and state where the company is based

💡 Use Cases: Market research and industry analysis

Business intelligence dashboards

Growth trend modeling in e-commerce

Employment and revenue correlation studies

Geographic distribution of successful shopping companies

This dataset is ideal for analysts, students, and professionals aiming to explore the structure and performance of the leading online shopping companies in the USA.

E-Commerce Dataset: Products, Customers, and Trends

Description

This dataset provides a comprehensive view of an e-commerce platform, featuring detailed information about products, customers, pricing, and sales trends. It is designed for data analysis, machine learning, and insights into online retail operations. The dataset is structured to help researchers and analysts explore various aspects of e-commerce, such as product popularity, customer preferences, and shipping performance.

Columns and Their Descriptions

• Product ID: Unique identifier for each product.
• Product Name: The name or title of the product listed in the catalog.
• Category: The category or type of the product (e.g., Electronics, Clothing, Home Decor).
• Price: The price of the product in USD.
• Discount: The discount applied to the product as a percentage of the original price.
• Tax Rate: The applicable tax rate for the product as a percentage.
• Stock Level: The number of units currently available in inventory.
• Supplier ID: A unique identifier for the supplier of the product.
• Customer Age Group: The age group of customers who frequently purchase this product (e.g., Teens, Adults, Seniors).
• Customer Location: The geographical location of customers (e.g., Country, State, or City).
• Customer Gender: The gender(s) of customers most likely to purchase this product (e.g., Male, Female, Both).
• Shipping Cost: The cost of shipping the product in USD.
• Shipping Method: The method of shipping used (e.g., Standard, Express, Overnight).
• Return Rate: The percentage of orders for this product that are returned by customers.
• Seasonality: The season(s) during which the product is most popular (e.g., Winter, Summer, All-Year).
• Popularity Index: A score indicating the product's popularity on a scale of 0 to 100.

Use Cases

This dataset is ideal for: - Exploratory Data Analysis (EDA): Analyze sales trends, product popularity, and customer preferences. - Visualization: Create insightful charts to visualize product performance, regional sales, and shipping trends. - Customer Insights: Understand customer segmentation based on demographics, preferences, and location. - Machine Learning Applications: - Regression: Predict product popularity based on price, discount, and stock level. - Clustering: Identify similar product categories for targeted marketing. - Classification: Predict whether a product will be returned based on its features.

Sample Data

Deluxe is an online retailer based in UK that deals in a wide range of products in the following categories: 1. Clothing 2. Games 3. Appliances 4. Electronics 5. Books 6. Beauty products 7. Smartphones 8. Outdoors products 9. Accessories 10. Other Basic household products are classified as 'Other' in the category column since they have small value to the business.

Data Description: dates: sale date order_value_EUR : sale price in EUR cost: cost of goods sold in EUR category: item category country: customers' country at the time of purchase customer_name: name of customer device_type: The gadget used by customer to access our online store(PC, mobile, tablet) sales_manager: name of the sales manager for each sale sales_representative: name of the sales rep for each sale order_id: unique identifier of an order

The data was recorded for the period 1/2/2019 and 12/30/2020 with an aim to generate business insights to guide business direction. We would like to see what interesting insights the Kaggle community members can produce from this data.