Snapshot img

The online retail market share in the US is expected to increase to USD 460.13 billion from 2021 to 2026, and the market’s growth momentum will accelerate at a CAGR of 11.64%.

The report extensively covers online retail market in the US segmentation by the following:

Product - Apparel, footwear, and accessories, consumer electronics and electricals, food and grocery, home furniture and furnishing, and others
Device - Smartphones and tablets and PCs

The US online retail market report offers information on several market vendors, including Amazon.com Inc., Apple Inc., Best Buy Co. Inc., Costco Wholesale Corp., eBay Inc., Kroger Co., Target Corp., The Home Depot Inc., Walmart Inc., and Wayfair Inc. among others.

This online retail market in the US research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches.

What will the Online Retail Market Size in the US be During the Forecast Period?

Download the Free Report Sample to Unlock the Online Retail Market Size in the US for the Forecast Period and Other Important Statistics

Online Retail Market in the US: Key Drivers, Trends, and Challenges

The growing seasonal and holiday sales is notably driving the online retail market growth in the US, although factors such as transportation and logistics may impede the market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the online retail industry in the US. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key US Online Retail Market Driver

The growing seasonal and holiday sales is one of the key drivers supporting the US online retail market growth. For instance, from November 1 to December 24, e-commerce sales in the US increased by 11% in 2021, when compared to a massive 47.2% growth in the holiday season of 2020. E-commerce sales made up 20.9 % of total retail sales in the holiday season of 2021, slightly higher than 20.6 percent in 2020. Thanksgiving, Black Friday, and Cyber Monday are the days that see a high amount of online shopping. Apparel, footwear and accessories, consumer electronics, computer hardware, and toys are the largest gaining product categories during the holiday season. Consumers in the US spent $204.5 billion online in November and December 2021, up 8.6% over the same period in 2020. Such exciting sales and offers are driving the market growth.

Key US Online Retail Market Trend

Omni-channel retailing is one of the key US online retail market trends fueling the market growth. It is rapidly becoming the norm for many retailers in the US. It offers consumers the option to shop online and pick up the merchandise from the store nearest to their location on the same day. Retailers are observing a high web influence on their in-store sales. For instance, Best Buy is integrating its offline and online stores to boost revenues. As a part of its omnichannel strategy, the retailer is utilizing physical stores as distribution centers for online purchases. According to Best Buy, 40% of its online shoppers prefer picking up their purchases from physical stores. Best Buy also challenges online and discount retailers with its match-to-price strategy, claiming to offer gadgets at or below the price offered by competitors. Such strategies are expected to boost market growth during the forecast period.

Key US Online Retail Market Challenge

Transportation and logistics are some of the factors hindering the US online retail market growth. Product procurement or sourcing, shipment of ordered items, and delivery to customers are the three major processes where the intervention of transportation and logistics come into the picture. All these processes require a high investment of both time and money, which challenges the efficiency and effectiveness of retailers and their costing strategies. The higher cost incurred from transportation and logistics reduces the margin of retailers, and most of the time, retailers are unable to break even. Between rising fuel prices, driver shortages, as well as a governmental and societal push for increased digitization and sustainability, transport and logistics will continue to be under a lot of pressure. Such factors will negatively impact the market growth during the forecast period.

This online retail market in the US analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2022-2026.

Who are the Major Online Retail Market Vendors in the US?

The report analyzes the market’s competitive landscape and offers information on several market vendors, includi

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)

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

Massive eBay Marketplace Data Collection for E-commerce Intelligence

Unlock the power of online marketplace analytics with our comprehensive eBay products dataset. This premium collection contains 1.29 million products from eBay's global marketplace, providing extensive insights into one of the world's largest e-commerce platforms. Perfect for competitive analysis, pricing strategies, market research, and machine learning applications in e-commerce.

Dataset Overview

• Total Products: 1,290,000+ marketplace listings
• Source: eBay Global Marketplace
• Format: CSV, ZIP compressed
• File Size: Optimized compressed format
• Coverage: Multi-category product listings across eBay

Complete Data Fields Included

Product Identification

• id: Unique eBay product identifiers
• name: Complete product titles and names
• url: Direct eBay listing page links
• epid: eBay Product ID for catalog matching
• source: Data source identification

Product Details

• raw_product_description: Original unprocessed product descriptions
• product_description: Cleaned and formatted product descriptions
• brand: Brand names and manufacturer information
• mpn: Manufacturer Part Numbers
• gtin13: Global Trade Item Numbers (barcodes)

Pricing and Availability

• price: Current listing prices
• currency: Currency information for international listings
• in_stock: Stock availability status
• breadcrumbs: Category navigation paths

Visual and Technical Data

• images: Product image URLs and references
• crawled_at: Data collection timestamps

Key Use Cases

E-commerce Market Research

• Analyze eBay marketplace trends and patterns
• Study product category performance
• Monitor pricing strategies across sellers
• Identify high-demand product categories

Competitive Intelligence

• Benchmark pricing against eBay marketplace
• Analyze product positioning strategies
• Study seller competition and market share
• Monitor inventory levels and availability

Price Optimization

• Develop dynamic pricing algorithms
• Analyze price elasticity across categories
• Compare marketplace pricing trends
• Optimize listing prices for maximum visibility

Machine Learning Applications

• Train recommendation systems
• Develop price prediction models
• Create product categorization algorithms
• Build inventory forecasting systems

Target Industries

E-commerce Retailers

• Marketplace Strategy: Optimize eBay selling strategies
• Pricing Intelligence: Competitive price monitoring
• Product Research: Identify profitable product opportunities
• Inventory Planning: Demand forecasting and stock optimization

Technology Companies

• AI Training Data: Machine learning model development
• Analytics Platforms: E-commerce intelligence tools
• Price Comparison: Marketplace comparison services
• Search Enhancement: Product discovery optimization

Market Research Firms

• Industry Reports: E-commerce marketplace analysis
• Consumer Behavior: Online shopping pattern studies
• Brand Monitoring: Brand performance tracking
• Trend Analysis: Market trend identification

Academic Research

• E-commerce Studies: Online marketplace research
• Business Intelligence: Retail analytics case studies
• Data Science Projects: Large-scale dataset analysis
• Economic Research: Digital marketplace economics

Data Quality Features

• Comprehensive Coverage: 1.29 million unique products
• Rich Metadata: Complete product information included
• Validated Data: Quality-checked and processed
• Structured Format: Ready-to-use CSV format
• Global Scope: International marketplace coverage

Business Context: The client is one of the leading online market place in India and would like partner with Analytixlabs. Client wants help in measuring, managing and analysing performance of business. Analytixlabs has hired you as an analyst for this project where client asked you to provide data driven insights about business and understand customer, seller behaviors, product behavior and channel behavior etc... While working on this project, you are expected to clean the data (if required) before analyze it. Available Data: Data has been provided for the period of Sep 2016 to Oct 2018 and the below is the data model. Tables: Customers: Customers information Sellers: Sellers information Products: Product information Orders: Orders info like ordered, product id, status, order dates etc.. Order_Items: Order level information Order_Payments: Order payment information Order_Review_Ratings: Customer ratings at order level Geo-Location: Location details Data Model: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13449746%2Fdd2a9639372124fb12bfd630fd43e473%2FScreenshot%202024-01-29%20210318.png?generation=1706542441423684&alt=media" alt="">

Business Objective: The below are few Sample business questions to be addressed as part of this analysis. However this is not exhaustive list and you can add as many as analysis and provide insights on the same. 1. Perform Detailed exploratory analysis a. Define & calculate high level metrics like (Total Revenue, Total quantity, Total products, Total categories, Total sellers, Total locations, Total channels, Total payment methods etc…) b. Understanding how many new customers acquired every month c. Understand the retention of customers on month on month basis d. How the revenues from existing/new customers on month on month basis e. Understand the trends/seasonality of sales, quantity by category, location, month, week, day, time, channel, payment method etc… f. Popular Products by month, seller, state, category. g. Popular categories by state, month h. List top 10 most expensive products sorted by price 2. Performing Customers/sellers Segmentation a. Divide the customers into groups based on the revenue generated b. Divide the sellers into groups based on the revenue generated 3. Cross-Selling (Which products are selling together) Hint: We need to find which of the top 10 combinations of products are selling together in each transaction. (combination of 2 or 3 buying together) 4. Payment Behaviour a. How customers are paying? b. Which payment channels are used by most customers? 5. Customer satisfaction towards category & product a. Which categories (top 10) are maximum rated & minimum rated? b. Which products (top10) are maximum rated & minimum rated? c. Average rating by location, seller, product, category, month etc. Etc..

Dirty Retail Store Sales Dataset

Overview

The Dirty Retail Store Sales dataset contains 12,575 rows of synthetic data representing sales transactions from a retail store. The dataset includes eight product categories with 25 items per category, each having static prices. It is designed to simulate real-world sales data, including intentional "dirtiness" such as missing or inconsistent values. This dataset is suitable for practicing data cleaning, exploratory data analysis (EDA), and feature engineering.

File Information

• File Name: retail_store_sales.csv
• Number of Rows: 12,575
• Number of Columns: 11

Columns Description

Column Name	Description	Example Values
Transaction ID	A unique identifier for each transaction. Always present and unique.	TXN_1234567
Customer ID	A unique identifier for each customer. 25 unique customers.	CUST_01
Category	The category of the purchased item.	Food, Furniture
Item	The name of the purchased item. May contain missing values or None.	Item_1_FOOD, None
Price Per Unit	The static price of a single unit of the item. May contain missing or None values.	4.00, None
Quantity	The quantity of the item purchased. May contain missing or None values.	1, None
Total Spent	The total amount spent on the transaction. Calculated as Quantity * Price Per Unit.	8.00, None
Payment Method	The method of payment used. May contain missing or invalid values.	Cash, Credit Card
Location	The location where the transaction occurred. May contain missing or invalid values.	In-store, Online
Transaction Date	The date of the transaction. Always present and valid.	2023-01-15
Discount Applied	Indicates if a discount was applied to the transaction. May contain missing values.	True, False, None

Categories and Items

The dataset includes the following categories, each containing 25 items with corresponding codes, names, and static prices:

Electric Household Essentials

Item Code	Item Name	Price
Item_1_EHE	Blender	5.0
Item_2_EHE	Microwave	6.5
Item_3_EHE	Toaster	8.0
Item_4_EHE	Vacuum Cleaner	9.5
Item_5_EHE	Air Purifier	11.0
Item_6_EHE	Electric Kettle	12.5
Item_7_EHE	Rice Cooker	14.0
Item_8_EHE	Iron	15.5
Item_9_EHE	Ceiling Fan	17.0
Item_10_EHE	Table Fan	18.5
Item_11_EHE	Hair Dryer	20.0
Item_12_EHE	Heater	21.5
Item_13_EHE	Humidifier	23.0
Item_14_EHE	Dehumidifier	24.5
Item_15_EHE	Coffee Maker	26.0
Item_16_EHE	Portable AC	27.5
Item_17_EHE	Electric Stove	29.0
Item_18_EHE	Pressure Cooker	30.5
Item_19_EHE	Induction Cooktop	32.0
Item_20_EHE	Water Dispenser	33.5
Item_21_EHE	Hand Blender	35.0
Item_22_EHE	Mixer Grinder	36.5
Item_23_EHE	Sandwich Maker	38.0
Item_24_EHE	Air Fryer	39.5
Item_25_EHE	Juicer	41.0

Furniture

Item Code	Item Name	Price
Item_1_FUR	Office Chair	5.0
Item_2_FUR	Sofa	6.5
Item_3_FUR	Coffee Table	8.0
Item_4_FUR	Dining Table	9.5
Item_5_FUR	Bookshelf	11.0
Item_6_FUR	Bed F...

This dataset contains a list of sales and movement data by item and department appended monthly. Update Frequency : Monthly

Get access to the Walmart Basic Product Details Dataset, which includes essential information on a wide range of products available at Walmart.

This comprehensive dataset features product names, categories, descriptions, prices, and more. Ideal for market analysis, competitive research, and e-commerce applications.

Download now to enhance your data-driven strategies and insights with detailed Walmart product information.

The dataset having basic details of a dataset like title, id, image, price and descripton.

Records count: 2.5 million +

1 INTRODUCTION 1.1 Study Assumptions and Market Definition 1.2 Scope of the Study

   2 RESEARCH METHODOLOGY

   3 EXECUTIVE SUMMARY

   4 MARKET DYNAMICS
   4.1 Market Drivers
   4.1.1 Rise in Duty-Free Retailing Stores with Technology Integration will accelerate market growth
   4.1.2 Rise in Foreign Tourists to Boost the Market Growth
   4.2 Market Restraints
   4.2.1 Convenience and Variety of Online Shopping as more and more Customers Purchase Online
   4.2.2 Usage of Unsustainable Goods for Storage Affecting Market Growth
   4.3 Market Opportunities
   4.3.1 Increased Digitalization To Boost the Demand for Duty Free Products
   4.4 Porter's Five Forces Analysis
   4.4.1 Bargaining Power of Suppliers
   4.4.2 Bargaining Power of Buyers/Consumers
   4.4.3 Threat of New Entrants
   4.4.4 Threat of Substitute Products
   4.4.5 Intensity of Competitive Rivalry
   4.5 Impact of COVID-19 on the market

   5 MARKET SEGMENTATION
   5.1 By Product Type
   5.1.1 Fashion and Accessories
   5.1.2 Jewellery and Watches
   5.1.3 Wine and Spirits
   5.1.4 Food and Confectionery
   5.1.5 Fragrances and Cosmetics
   5.1.6 Tobacco
   5.1.7 Other Product Types
   5.2 By Distribution Channel
   5.2.1 Airports
   5.2.2 Airlines
   5.2.3 Ferries
   5.2.4 Other Distribution Channels
   5.3 By Geography
   5.3.1 North America
   5.3.2 South America
   5.3.3 Europe
   5.3.4 Asia-Pacific
   5.3.5 Middle East & Africa

   6 COMPETITIVE LANDSCAPE
   6.1 Market Concentration Overview
   6.2 Company Profiles
   6.2.1 Dufry
   6.2.2 Lotte Duty Free
   6.2.3 Lagardere Travel Retail
   6.2.4 DFS Group
   6.2.5 The Shilla Duty Free
   6.2.6 King Power International Group
   6.2.7 China Duty Free Group
   6.2.8 Dubai Duty Free
   6.2.9 Duty Free Americas
   6.2.10 Sinsegae Duty Free
   6.2.11 WH Smith*

   7 FUTURE MARKET TRENDS

   8 DISCLAIMER AND ABOUT US

   The Global Duty Free & Travel Retail Market Report is segmented by product type and distribution channel, offering a comprehensive industry analysis. The market is a significant revenue generator for aviation, tourism, and other travel-related industries. Airports, in particular, derive a considerable portion of their income from duty-free and travel retailing. Despite challenges such as trade tensions and protectionism between countries, the market is seeing an increased demand for duty-free alcohol, spurred by diversifying consumer buying habits and rising spending among the middle-class population.<br><br>The market's growth is fueled by the rapidly expanding international tourism market and the increasing number of new air routes in Asian countries. However, global currency fluctuations could potentially hamper product demand. The market is segmented by type, with perfumes expected to dominate the global duty-free retail market share. The rising popularity of premium beauty products is also fueling demand in the cosmetics space. In terms of sales channels, airports dominate product sales worldwide.<br><br>The Asia Pacific market is anticipated to witness significant growth over the forecast period, with Europe and North America also expected to see growth. The South America and Middle East and Africa markets are likely to experience an upward trend due to rising consumer demand for premium/luxury perfumes. Duty-Free & Travel Retail market share, size, and revenue growth rate statistics provide a comprehensive market overview, including market forecast and market trends. A sample of this industry report is available as a free report PDF download.<br><br>The industry outlook remains positive, with market leaders driving the market growth. Market segmentation by product type and distribution channel offers detailed market data. The market value is projected to rise, supported by industry statistics and market predictions. Research companies provide valuable industry information and industry research, contributing to market review and market analysis. The report example highlights the importance of understanding market dynamics to capitalize on growth opportunities.

   The Global Duty Free & Travel Retail Report Covers the Following Regions: NA, North America, North American, Northern America, Northern American, SA, South America, South American, EU, Europe, European, APAC, Asia-Pacific, Asian, MEA, Middle East and Africa, Middle Eastern and African, MENA, Middle East, Middle Eastern, Africa, African, Americas, American

Description: Explore a comprehensive dataset of e-commerce sales, encompassing a variety of product categories, pricing, customer reviews, and sales trends over the past year. This dataset is ideal for analyzing market trends, customer behavior, and sales performance. Explore into the data to uncover insights that can optimize product listings, pricing strategies, and marketing campaigns.

Columns:

product_id: Unique identifier for each product. product_name: Name of the product. category: Product category. price: Price of the product. review_score: Average customer review score (1 to 5). review_count: Total number of reviews. sales_month_1 to sales_month_12: Monthly sales data for each product over the past year. Potential Analyses:

Identify top-performing product categories. Analyze the impact of pricing on sales and customer reviews. Discover seasonal sales trends and patterns. Evaluate customer satisfaction based on review scores and counts.

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

📦 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!

Objectives:

• I leveraged advanced data visualization techniques to extract valuable insights from a comprehensive dataset. By visualizing sales patterns, customer behavior, and product trends, I identified key growth opportunities and provided actionable recommendations to optimize business strategies and enhance overall performance. you can find the GitHub repo here Link to GitHub Repository.

Data Description:

there are exactly 6 table and 1 is a fact table and the rest of them are dimension tables: Fact Table:

payment_key:
  Description: An identifier representing the payment transaction associated with the fact.
  Use Case: This key links to a payment dimension table, providing details about the payment method and related information.

customer_key:
  Description: An identifier representing the customer associated with the fact.
  Use Case: This key links to a customer dimension table, providing details about the customer, such as name, address, and other customer-specific information.

time_key:
  Description: An identifier representing the time dimension associated with the fact.
  Use Case: This key links to a time dimension table, providing details about the time of the transaction, such as date, day of the week, and month.

item_key:
  Description: An identifier representing the item or product associated with the fact.
  Use Case: This key links to an item dimension table, providing details about the product, such as category, sub-category, and product name.

store_key:
  Description: An identifier representing the store or location associated with the fact.
  Use Case: This key links to a store dimension table, providing details about the store, such as location, store name, and other store-specific information.

quantity:
  Description: The quantity of items sold or involved in the transaction.
  Use Case: Represents the amount or number of items associated with the transaction.

unit:
  Description: The unit or measurement associated with the quantity (e.g., pieces, kilograms).
  Use Case: Specifies the unit of measurement for the quantity.

unit_price:
  Description: The price per unit of the item.
  Use Case: Represents the cost or price associated with each unit of the item.

total_price:
  Description: The total price of the transaction, calculated as the product of quantity and unit price.
  Use Case: Represents the overall cost or revenue generated by the transaction.

Customer Table: customer_key:

Description: An identifier representing a unique customer.
Use Case: Serves as the primary key to link with the fact table, allowing for easy and efficient retrieval of customer-specific information.

name:

Description: The name of the customer.
Use Case: Captures the personal or business name of the customer for identification and reference purposes.

contact_no:

Description: The contact number associated with the customer.
Use Case: Stores the phone number or contact details for communication or outreach purposes.

nid:

Description: The National ID (NID) or a unique identification number for the customer.

Item Table: item_key:

Description: An identifier representing a unique item or product.
Use Case: Serves as the primary key to link with the fact table, enabling retrieval of detailed information about specific items in transactions.

item_name:

Description: The name or title of the item.
Use Case: Captures the descriptive name of the item, providing a recognizable label for the product.

desc:

Description: A description of the item.
Use Case: Contains additional details about the item, such as features, specifications, or any relevant information.

unit_price:

Description: The price per unit of the item.
Use Case: Represents the cost or price associated with each unit of the item.

man_country:

Description: The country where the item is manufactured.
Use Case: Captures the origin or manufacturing location of the item.

supplier:

Description: The supplier or vendor providing the item.
Use Case: Stores the name or identifier of the supplier, facilitating tracking of item sources.

unit:

Description: The unit of measurement associated with the item (e.g., pieces, kilograms).

Store Table: store_key:

Description: An identifier representing a unique store or location.
Use Case: Serves as the primary key to link with the fact table, allowing for easy retrieval of information about transactions associated with specific stores.

division:

Description: The administrative division or region where the store is located.
Use Case: Captures the broader geographical area in which...

Context

Typically e-commerce datasets are proprietary and consequently hard to find among publicly available data. However, The UCI Machine Learning Repository has made this dataset containing actual transactions from 2010 and 2011. The dataset is maintained on their site, where it can be found by the title "Online Retail".

Content

"This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers."

Acknowledgements

Per the UCI Machine Learning Repository, this data was made available by Dr Daqing Chen, Director: Public Analytics group. chend '@' lsbu.ac.uk, School of Engineering, London South Bank University, London SE1 0AA, UK.

Image from stocksnap.io.

Inspiration

Analyses for this dataset could include time series, clustering, classification and more.

Sales Data Description This dataset represents synthetic sales data generated for practice purposes only. It is not real-time or based on actual business operations, and should be used solely for educational or testing purposes. The dataset contains information that simulates sales transactions across different products, regions, and customers. Each row represents an individual sale event with various details associated with it.

Columns in the Dataset

1. Product_ID: Unique identifier for each product sold. Randomly generated for practice purposes.
2. Sale_Date: The date when the sale occurred. Randomly selected from the year 2023.
3. Sales_Rep: The sales representative responsible for the transaction. The dataset includes five random sales representatives (Alice, Bob, Charlie, David, Eve).
4. Region: The region where the sale took place. The possible regions are North, South, East, and West.
5. Sales_Amount: The total sales amount for the transaction, including discounts if any. Values range from 100 to 10,000 (in currency units).
6. Quantity_Sold: The number of units sold in that transaction, randomly generated between 1 and 50.
7. Product_Category: The category of the product sold. Categories include Electronics, Furniture, Clothing, and Food.
8. Unit_Cost: The cost per unit of the product sold, randomly generated between 50 and 5000 currency units.
9. Unit_Price: The selling price per unit of the product, calculated to be higher than the unit cost.
10. Customer_Type: Indicates whether the customer is a New or Returning customer.
11. Discount: The discount applied to the sale, randomly chosen between 0% and 30%.
12. Payment_Method: The method of payment used by the customer (e.g., Credit Card, Cash, Bank Transfer).
13. Sales_Channel: The channel through which the sale occurred. Either Online or Retail.
14. Region_and_Sales_Rep: A combined column that pairs the region and sales representative for easier tracking.

Disclaimer

Please note: This data was randomly generated and is intended solely for practice, learning, or testing. It does not reflect real-world sales, customers, or businesses, and should not be considered reliable for any real-time analysis or decision-making.

About Dataset Description: This dataset contains detailed information about retail sales and customer foot traffic in Australia. It includes data on sales trends, customer demographics, peak traffic times, and product categories, providing a comprehensive view of the retail landscape.

Context: Understanding customer behavior and sales trends is essential for optimizing retail operations. This dataset can be used to analyze patterns in sales by time of day, evaluate the effectiveness of staffing during peak hours, and gain insights into popular product categories and demographics.

Source: Data collected from various Australian retail outlets to capture a wide range of business types, including clothing stores, electronics shops, grocery stores, and bookstores. The information is anonymized and aggregated to protect customer privacy.

Inspiration: This dataset was inspired by the need for detailed retail analytics to improve business strategies. By examining foot traffic, sales, and customer satisfaction, retail managers can better understand consumer behavior and make informed decisions.

Dataset Description

The E-commerce Order Dataset provides comprehensive information related to orders, items within orders, customers, payments, and products for an e-commerce platform. This dataset is structured with multiple tables, each containing specific information about various aspects of the e-commerce operations.

Dataset Features

Orders Table:

• order_id: Unique identifier for an order, acting as the primary key.
• customer_id: Unique identifier for a customer. This table may not be unique at this level.
• order_status: Indicates the status of an order (e.g., delivered, cancelled, processing, etc.).
• order_purchase_timestamp: Timestamp when the order was made by the customer.
• order_approved_at: Timestamp when the order was approved from the seller's side.
• order_delivered_timestamp: Timestamp when the order was delivered at the customer's location.
• order_estimated_delivery_date: Estimated date of delivery shared with the customer while placing the order.

Order Items Table

• order_id: Unique identifier for an order.
• order_item_id: Item number in each order, acting as part of the primary key along with the order_id.
• product_id: Unique identifier for a product.
• seller_id: Unique identifier for the seller.
• price: Selling price of the product.
• shipping_charges: Charges associated with the shipping of the product.

Customers Table

• customer_id: Unique identifier for a customer, acting as the primary key.
• customer_zip_code_prefix: Customer's Zip code.
• customer_city: Customer's city.
• customer_state: Customer's state.

Payments Table

• order_id: Unique identifier for an order.
• payment_sequential: Provides information about the sequence of payments for the given order.
• payment_type: Type of payment (e.g., credit_card, debit_card, etc.).
• payment_installments: Payment installment number in case of credit cards.
• payment_value: Transaction value.

Products Table

• product_id: Unique identifier for each product, acting as the primary key.
• product_category_name: Name of the category the product belongs to.
• product_weight_g: Product weight in grams.
• product_length_cm: Product length in centimeters.
• product_height_cm: Product height in centimeters.
• product_width_cm: Product width in centimeters.

Overview This dataset contains 50,000 fictional e-commerce transaction records, making it ideal for data analysis, visualization, and machine learning experiments. It includes user demographics, product categories, purchase amounts, payment methods, and transaction dates to help understand consumer behavior and sales trends.

Columns Transaction_ID – Unique identifier for each transaction User_Name – Randomly generated user name Age – Age of the user (18 to 70) Country – Country where the transaction took place (randomly chosen from 10 countries) Product_Category – Category of the purchased item (e.g., Electronics, Clothing, Books) Purchase_Amount – Total amount spent on the transaction (randomly generated between $5 and $1000) Payment_Method – Method used for payment (e.g., Credit Card, PayPal, UPI) Transaction_Date – Date of the purchase (randomly selected within the past two years)

Use Cases Sales and trend analysis – Identify which product categories are most popular Customer segmentation – Analyze spending behavior based on age and country Fraud detection – Detect unusual purchase patterns Machine learning projects – Train models for recommendation systems or revenue predictions

This dataset is synthetic and does not contain real user data. It can be used for research, experimentation, and educational purposes.

Dataset Overview

Dataset Name: "Nuestro Amazon" E-Commerce Dataset

General Description: This dataset represents an e-commerce database containing information about products, categories, customers, orders, and more. The data is structured to facilitate analysis and insights into various aspects of an e-commerce business.

Structure and Attributes: The dataset consists of eight tables: categories, customers, employees, orders, ordersdetails, products, shippers, and suppliers. These tables encompass key information such as product details, customer information, order details.

Data Source: The data was generated for educational and demonstration purposes to simulate an e-commerce environment. It is not sourced from a real-world e-commerce platform.

Usage and Applications: This dataset can be utilized for various purposes, including market basket analysis, customer segmentation, sales trends analysis, and supply chain optimization. Analysts and data scientists can derive valuable insights to improve business strategies.

Acknowledgments and References: The dataset was created for educational use. No specific external sources were referenced for this dataset.

Explore Interactive Visualizations

"Quantity per country" in this Kaggle notebook or on Tableau.

"Orders by country" in this Kaggle notebook or on Tableau.

Data Analysis

"Data Analysis of Online Orders" in this Kaggle notebook

"Data Visualization and Analysis in R" in this Kaggle notebook

Explore the rich world of online retail with this comprehensive dataset, featuring product listings from Amazon's apparel category. Whether you're a data scientist, market researcher, or e-commerce enthusiast, this dataset provides an in-depth view of the fashion and apparel landscape on one of the world's largest online marketplaces.

Download full dataset: https://app.datastock.shop/?site_name=Amazon_Product_Listing_1st_Oct_2023_-_31st_Oct_2023

What's Inside?

This dataset contains detailed information on thousands of apparel products listed on Amazon, including:

Product Titles: Concise descriptions of the apparel items. Brand Names: Insights into popular and emerging brands. Categories: A breakdown of product types such as shirts, dresses, footwear, and more. Pricing Information: Current and historical prices to analyze trends. Ratings & Reviews: Average ratings and review counts to gauge customer satisfaction. Product Features: Key details like material, fit, color, and size availability. Availability & Discounts: Details on stock status and promotional offers.

Why Use This Dataset?

Market Analysis: Identify top-performing apparel categories and trends. Competitive Research: Understand pricing strategies and brand popularity. Customer Insights: Analyze customer reviews and ratings for sentiment analysis. Build AI Models: Train machine learning models for recommendation systems, pricing optimization, or demand forecasting. E-Commerce Strategy: Gain insights to enhance your own online store\u2019s performance.

Who Is It For?

E-commerce professionals looking to understand the apparel market. Data scientists exploring NLP, sentiment analysis, or pricing strategies. Students and researchers studying retail trends and consumer behavior. Developers building product recommendation engines or trend analysis dashboards.

Key Features:

Structured Data: Delivered in clean, analysis-ready formats like CSV or JSON. Up-to-Date Listings: Reflecting current trends in the online apparel market. Diverse Attributes: Covering a wide array of product features for multifaceted analyses. Call to Action Unlock the potential of this dataset to gain actionable insights into the dynamic world of online apparel retail. Perfect for analysis, academic research, or powering innovative AI solutions.

Need customized datasets or specific product categories? PromptCloud can provide tailored web scraping services to match your unique requirements. Partner with us to access clean, structured, and actionable data for your next big project! https://www.promptcloud.com/web-scraping-services/

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The process of extracting and analyzing supermarket data involves an intricate series of steps, from web scraping product details directly from the websites of leading supermarkets like Aldi, ASDA, Morrisons, Sainsbury's, and Tesco, to processing and analyzing this data for actionable insights. This comprehensive approach leverages Python's powerful libraries such as Pandas for data manipulation, Selenium for web scraping, and Urllib.3 for URL handling, ensuring a robust data extraction foundation.

Web scraping is the first critical step in this process. Customized functions are developed for each supermarket to systematically navigate through their web pages, extract essential product information like names, prices, price per unit, and images, and handle various common exceptions gracefully. This meticulous data collection is structured to restart automatically in case of any hitches, ensuring no data loss and maintaining the integrity of the extraction process.

Once the data is scraped, it undergoes a detailed processing phase. This involves consolidating the collected information into unified datasets, performing spatial joins to align data accurately, and applying category simplification for better analysis. Notably, for supermarkets like Tesco, additional steps are taken to incorporate Clubcard data, ensuring the most competitive prices are captured. This phase is critical for preparing the data for in-depth analysis by cleaning, structuring, and ensuring it is comprehensive.

Quality assurance plays a pivotal role throughout the process. A dedicated data quality script scrutinizes the extracted data for discrepancies, checks the completeness of the web scraping effort, and validates the processed data for any null values or inconsistencies. This step is crucial for ensuring the reliability of the data before it moves to the analysis stage.

The analysis of the data is multifaceted, focusing on pricing strategies, brand popularity, and product categorization. Through the use of tables, graphs, word clouds, and treemaps, the analysis reveals insights into pricing patterns, brand preferences, and category distributions. Additionally, a recommender system based on Singular Value Decomposition (SVD) enhances the analysis by providing personalized product recommendations, demonstrating the application of advanced machine learning techniques in understanding customer preferences.

Moreover, the analysis extends to price comparisons using TF-ID matrices and examines pricing psychology to uncover tactics used in product pricing. This nuanced analysis offers a deep dive into how pricing strategies might be influenced by psychological factors, competitive pressures, or inflation.

An interesting aspect of the analysis is monitoring price changes over time, which involves calculating average prices per category on a weekly basis and analyzing the percentage changes. This dynamic view of pricing helps in understanding market trends and making informed decisions.

Finally, the culmination of this extensive process is the deployment of the application to the cloud via Streamlit, facilitated through GitHub. This deployment not only makes the application accessible but also showcases the integration of various components into a streamlined, user-friendly interface.

In summary, the end-to-end process of web scraping, data processing, and analysis of supermarket data is a comprehensive effort that combines technical prowess with analytical insight. It underscores the power of Python in handling complex data tasks, the importance of data quality in analytical projects, and the potential of data analysis in unveiling market trends and consumer preferences, all while ensuring accessibility through cloud deployment. This meticulous approach not only aids in strategic decision-making but also sets a precedent for the application of data science in the retail industry.