As of early 2023, approximately ** percent of consumers in the United States said they would prefer to shop mostly online rather than in-store, making it the country with highest online shopping preference. In contrast, more shoppers preferred visiting physical stores in countries such as Austria, Finland, and New Zealand.

Context

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

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

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

Saree Retailers Database in India

Accurate Up-to-Date Data for All Types of Business Purposes

By Amresh [source]

About this dataset

This All India Saree Retailers Database is a comprehensive collection of up-to-date information on 10,000 Saree Retailers located all over India. The database is updated in April 2021 and offers an overall accuracy rate of around 90%.

For business owners, marketers, and data analysts and researchers, this dataset is an invaluable resource. It contains contact details of store name, contact person names, phone number and email address along with store location information like city state and pin code to help you target the right audience precisely.

The database can be accessed in Microsoft Excel (.xlsx) format which makes it easy to read or manipulate the file according to your needs. Apart from this wide range of payment options like Credit/Debit Card; Online Transfer; NEFT; Cash Deposit; Paytm; PhonePe; Google Pay or PayPal allow quick download access within 2-3 business hours.

So if you are looking for reliable business intelligence data related to Indian saree retailers that can help you unlock incredible opportunities for your business then make sure to download our All India Saree Retailers Database at the earliest!

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For more datasets, click here.

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How to use the dataset

This dataset provides a comprehensive list of Saree retailers in India, including store name, contact person, email address, mobile number, phone number, address details like city and state along with pin code. It contains 10 thousand records updated in April 2021 with an overall accuracy rate of around 90%. This data can be used to understand customer behaviour as well as to analyse geographical customer pattern.

Using this dataset you can: - Target specific states or cities where potential customers are located for your Saree business. - Get in touch with local Saree retailers for possible collaborations and partnerships. - Learn more about industry trends from actual store owners who can offer insights into the latest ongoing trends and identify new opportunities for you to grow your business. 4 .Analyse existing competitors’ market share by studying the cities/states where they operate and their contact information such as Mobile Number & Email Ids .
5 .Identify potential new customers for better sales conversion rates by understanding who is already operating in similar products nearby or have similar target audience as yours that help your company reach out to them quickly & effectively using direct marketing techniques such as emails & SMS etc.,

Research Ideas

• Creating targeted email campaigns to increase Saree sales: The dataset can be used to create targeted email campaigns that can reach the 10,000 Saree Retailers in India. This will allow businesses to increase sales by directing their message about promotions and discounts directly to potential customers.
• Customizing online product recommendations for each retailer: The dataset can be used to identify the specific products that each individual retailer is interested in selling, so product recommendations on an e-commerce website could be tailored accordingly. This would optimize customer experience giving them more accurate and relevant results when searching for a particular item they are looking for while shopping online.
• Using GPS technology to generate location-based marketing campaigns: By creating geo-fenced areas around each store using the pin code database, it would be possible to send out marketing messages based on people's physical location instead of just sending them out in certain neighborhoods or cities without regard for store locations within those areas. This could help reach specific customers with relevant messages about products or promotions that may interested them more effectively than a standard marketing campaign with no location targeting involved

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: 301-Saree-Garment-Retailer-Database-Sample.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Amresh.

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

Retail Sales in the United States increased 0.20 percent in September of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.

About Dataset

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

🛍️ The "orders" table includes the following columns:

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

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

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

🛒 The "products" table includes the following columns:

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

📦 The "order_items" table includes the following columns:

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

🏢 The "suppliers" table includes the following columns:

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

🌟 The "reviews" table includes the following columns:

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

💳 The "payments" table includes the following columns:

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

🚚 The "shipments" table includes the following columns:

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

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

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

https://raw.githubusercontent.com/Masterx-AI/Project_Retail_Analysis_with_Walmart/main/Wallmart1.jpg" alt="">

Description:

One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to the inappropriate machine learning algorithm. An ideal ML algorithm will predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc.

Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of all, which are the Super Bowl, Labour Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data. Historical sales data for 45 Walmart stores located in different regions are available.

Acknowledgements

The dataset is taken from Kaggle.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t single & multiple features.
• Also evaluate the models & compare their respective scores like R2, RMSE, etc.

Retail Sales in China increased 2.90 percent in October of 2025 over the same month in the previous year. This dataset provides - China Retail Sales YoY - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The B2C E-commerce Market size was valued at USD 6.23 trillion in 2023 and is projected to reach USD 21.18 trillion by 2032, exhibiting a CAGR of 19.1 % during the forecasts period. Recent developments include: In March 2024, Blink, an Amazon company, launched the Blink Mini 2 camera. The new compact plug-in camera offers enhanced features such as person detection, a broader field of view, a built-in LED spotlight for night view in color, and improved image quality. The Blink Mini 2 is designed to work indoors and outdoors, with the option to purchase the Blink Weather Resistant Power Adapter for outdoor use. , In October 2023, Flipkart.com introduced the 'Flipkart Commerce Cloud,' a customized suite of AI-driven retail technology solutions for global retailers and e-commerce businesses. This extensive offering includes marketplace technology, retail media solutions, pricing, and inventory management features rigorously assessed by Flipkart.com. The company aims to equip international sellers with reliable and secure tools to enhance business expansion and efficiency within the competitive global market. , In August 2023, Shopify and Amazon.com, Inc. announced a strategic partnership that will allow Shopify merchants to seamlessly implement Amazon's "Buy with Prime" option on their sites. As a result of the agreement, Amazon.com, Inc. Prime customers will enjoy a more efficient checkout process on various platforms. This collaboration allows Amazon Prime members to utilize their existing Amazon payment options, while Shopify will handle the transaction processing through its system, showcasing a partnership between the two leading companies. , In February 2023, eBay acquired 3PM Shield, a developer of AI-powered online retail solutions. 3PM Shield uses machine learning and artificial intelligence to analyze extensive data sets, enhancing marketplace compliance and user experience. This acquisition aligns with eBay's goal to offer a "safe and reliable" platform by boosting its ability to block the sale of counterfeit and prohibited items. By incorporating 3PM Shield's sophisticated monitoring technologies, eBay seeks to enhance its capability to address problematic seller behavior and spot problematic listings, fostering a safer e-commerce space for its worldwide community of sellers and buyers. .

Unlock the door to business expansion by investing in our real-time eCommerce leads list. Gain direct access to store owners and make informed decisions with data fields including Store Name, Website, Contact First Name, Contact Last Name, Email Address, Physical Address, City, State, Country, Zip Code, Phone Number, Revenue Size, Employee Size, and more on demand.

Ensure a lifetime of access for continuous growth and tailor your campaigns with accurate and reliable information, initiating targeted efforts that align with your marketing goals. Whether you're targeting specific industries or global locations, our database provides up-to-date and valuable insights to support your business journey.

• 4M+ eCommerce Companies • 40M+ Worldwide eCommerce Leads • Direct Contact Info for Shop Owners • 47+ eCommerce Platforms • 40+ Data Points • Lifetime Access • 10+ Data Segmentations • Sample Data

Grocery Inventory and Sales Dataset

Dataset Overview:

This dataset provides detailed information on various grocery items, including product details, supplier information, stock levels, reorder data, pricing, and sales performance. The data covers 990 products across various categories such as Grains & Pulses, Beverages, Fruits & Vegetables, and more. The dataset is useful for inventory management, sales analysis, and supply chain optimization.

Columns:

• Product_ID: Unique identifier for each product.
• Product_Name: Name of the product.
• Category: The product category (e.g., Grains & Pulses, Beverages, Fruits & Vegetables).
• Supplier_ID: Unique identifier for the product supplier.
• Supplier_Name: Name of the supplier.
• Stock_Quantity: The current stock level of the product in the warehouse.
• Reorder_Level: The stock level at which new stock should be ordered.
• Reorder_Quantity: The quantity of product to order when the stock reaches the reorder level.
• Unit_Price: Price per unit of the product.
• Date_Received: The date the product was received into the warehouse.
• Last_Order_Date: The last date the product was ordered.
• Expiration_Date: The expiration date of the product, if applicable.
• Warehouse_Location: The warehouse address where the product is stored.
• Sales_Volume: The total number of units sold.
• Inventory_Turnover_Rate: The rate at which the product sells and is replenished.
• Status: Current status of the product (e.g., Active, Discontinued, Backordered).

Dataset Usage:

• Inventory Management: Analyze stock levels and reorder strategies to optimize product availability and reduce stockouts or overstock.
• Sales Performance: Track sales volume and inventory turnover rate to understand product demand and profitability.
• Supplier Analysis: Evaluate suppliers based on product availability, pricing, and delivery frequency.
• Product Lifecycle: Identify discontinued or backordered products and analyze expiration dates for perishable goods.

How to Use:

This dataset can be used for various tasks such as: - Predicting reorder quantities using machine learning. - Analyzing inventory turnover to optimize stock levels. - Conducting sales trend analysis to identify popular or slow-moving items. - Improving supply chain efficiency by analyzing supplier performance.

Notes:

• This dataset is fictional and used for educational or demonstration purposes only.
• The expiration dates and last order dates should be considered for time-sensitive or perishable items.
• Some products have been marked as discontinued or backordered, indicating their current status in the inventory system.

License:

This dataset is released under the Creative Commons Attribution 4.0 International License. You are free to share, adapt, and use the data, provided proper attribution is given.

Factori is a compliant, flexible, and adaptable data provider. We help you make smarter decisions, fill all the gaps in your data, uncover patterns, gain a competitive advantage, and build better solutions by bringing accurate, holistic, privacy-compliant global consumer data.

We specialize in building the world’s largest consumer graph that ingests, de-dupes, and transforms premium data from over 2.3 billion anonymous customer profiles with 800+ attributes, which powers insights for smarter decision-making and building adequate solutions. We take privacy and personal information very seriously and are committed to adhering to all applicable data privacy and security laws and regulations, including the GDPR, CCPA, and ISO 27001.

In the dynamic realm of business, the perpetual challenge of maintaining current customer data is ever-present. Factori’s People Data API efficiently manages the ingestion, deduplication, and transformation of premium data sources, saving you valuable time and effort.

With our API, you can access and utilize subsets of our comprehensive person dataset, empowering you to gain actionable intelligence, make data-driven decisions, and build innovative products and services. Whether you're a marketer, data scientist, or business analyst, our US People Data can unlock new opportunities for your organization.

Designed as a comprehensive data enrichment solution, our US People database fills gaps in your customer data, offering profound insights into your consumers. Encompassing over 300 million profiles with more than 40 variables spanning location, demographics, lifestyle, hobbies, and behaviors, it acts as a guiding compass for understanding your customers' past, present, and potential future behaviors. This enables you to navigate the business landscape with clarity, making decisions grounded in comprehensive and informed perspectives.

Here are some of the data categories and attributes we offer within the US People Data Graph: Geography: City, State, ZIP, County, CBSA, Census Tract, etc. Demographics: Gender, Age Group, Marital Status, Language, etc. Financial: Income Range, Credit Rating Range, Credit Type,etc. Persona: Consumer type, Communication preferences, Family type, etc. Interests: Content, Brands, Shopping, Hobbies, Lifestyle, etc. Household: Number of Children, Number of Adults, IP Address, etc. Behaviors: Brand Affinity, App Usage, Web Browsing, etc. Firmographics: Industry, Company, Occupation, Revenue, etc. Retail Purchase: Store, Category, Brand, SKU, Quantity, Price, etc. Auto: Car Make, Model, Type, Year, etc. Housing: Home type, Home value, Renter/Owner, Year Built, etc.

Use Cases: Sales Intelligence: Precision Market Analysis and Segmentation Engage with personalized campaigns Enhance Lead Scoring and Qualification Strategic Marketing: Precision Market Analysis and Segmentation Engage with personalized campaigns Enhance Lead Scoring and Qualification Fraud and Cybersecurity: Unlock comprehensive identity insights Seamless KYC Compliances. Real-time Threat Detection HR Tech: Elevate Candidate Profiles Forge Talent Pathways Track role transitions

Discover the unparalleled potential of our comprehensive eCommerce leads database, featuring essential data fields such as Store Name, Website, Contact First Name, Contact Last Name, Email Address, Physical Address, City, State, Country, Zip Code, Phone Number, Revenue Size, Employee Size, and more on demand.

With a focus on Shopify, Amazon, eBay, and other global retail stores, this database equips you with accurate information for successful marketing campaigns. Supercharge your marketing efforts with our enriched contact and company database, providing real-time, verified data insights for strategic market assessments and effective buyer engagement across digital and traditional channels.

• 4M+ eCommerce Companies • 40M+ Worldwide eCommerce Leads • Direct Contact Info for Shop Owners • 47+ eCommerce Platforms • 40+ Data Points • Lifetime Access • 10+ Data Segmentations • Sample Data"

Address Lookup Service is a web service providing lookup function on Hong Kong address records in both Chinese and English aggregated from various Government Bureaux/Departments. This web service allows data consumers to look up address records in machine-readable format (XML or JSON) using address element information. It aims to facilitate development of applications with the need to capture Hong Kong address information more efficiently and accurately. The Address Lookup Service provides formatted addresses of the premises in Hong Kong, including private and public housing estates, commercial and industrial buildings, government buildings and offices, markets and shopping centers; and common facilities such as recreation and sports centres. Some addresses provided are representing a complex of buildings, such as schools or universities. Most of the addresses in Address Lookup Service are available in 2-Dimensional format which typically includes up to street name, building number and building name. 3-Dimensional formatted addresses, such as addresses include flat number and floor number, they are only available for public housing estates. For more details, please refer the Data Dictionary. This service also includes unofficial descriptions of buildings which are long established addresses in rural areas of the New Territories and are generally accepted by the public. The choice of name for a building is a matter for the owner, and at present there is no controlling legislation. The inclusion of a building name in this service confers no proprietary right to it or any part of it.

This survey collected data to generate a comprehensive review of the economic and policy status of the recreational for-hire sector in the U.S. Gulf of Mexico, including charter, head, and guide boats. The survey created a socioeconomic dataset that can be used to analyze future economic, environmental, and policy questions, including those related to natural disturbances and the ongoing regulation of resource utilization in the Gulf. The specific project objectives included a) collecting economic, social, and policy data for all segments of the for-hire sector b) identifying groups of respondents with relatively homogeneous characteristics, thereby defining operational classes that may be the focus of targeted, management-based economic and policy analysis and c) constructing costs, earnings, and attitudinal profiles by operational class and state/region. The survey was conducted by mail, internet, and in-person interviews in 2010.

Dataset containing data about the Trade index of the British Chamber of Commerce for Switzerland for the year 1925. The original focument is on e-manuscripta (https://doi.org/10.7891/e-manuscripta-174832)

This dataset contains

• Mid-resolution imagery used for processing (=images of the pages)
• Page.XML (=Transkribus export data)
• Parsed JSON data (=data of project-specific parser, built to extract named entities)
• Resulting JSON data (=proper data files with authentication data)
• List of consolidated companies

The resulting data contains:

• Information about companies and people (actors) listed in the trade index
• The parser recognizes company and person's names, locations, traded goods,section
• Found company (see list of consolidated companies)
• Found locations and their geo-info (geonames id, label, lat, lng, country, region)
• Found goods and their info (WZ 2008 and ISIC Rev04)
• Section where the actor is listed (WZ 2008 and ISIC Rev04)
• Source metadata and IIIF Snippet data

This data has been created and published as part of the dissertation project 'Lessons to learn? Unfolding a Global Market in Difficult Times'; The British Chamber of Commerce for Switzerland 1920 – 1950 (https://data.snf.ch/grants/grant/211961)