Retail Sales in the United States increased 0.60 percent in June 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.

Welcome to the Retail Sales and Customer Demographics Dataset! This synthetic dataset has been meticulously crafted to simulate a dynamic retail environment, providing an ideal playground for those eager to sharpen their data analysis skills through exploratory data analysis (EDA). With a focus on retail sales and customer characteristics, this dataset invites you to unravel intricate patterns, draw insights, and gain a deeper understanding of customer behavior.

****Dataset Overview:**

This dataset is a snapshot of a fictional retail landscape, capturing essential attributes that drive retail operations and customer interactions. It includes key details such as Transaction ID, Date, Customer ID, Gender, Age, Product Category, Quantity, Price per Unit, and Total Amount. These attributes enable a multifaceted exploration of sales trends, demographic influences, and purchasing behaviors.

Why Explore This Dataset?

• Realistic Representation: Though synthetic, the dataset mirrors real-world retail scenarios, allowing you to practice analysis within a familiar context.
• Diverse Insights: From demographic insights to product preferences, the dataset offers a broad spectrum of factors to investigate.
• Hypothesis Generation: As you perform EDA, you'll have the chance to formulate hypotheses that can guide further analysis and experimentation.
• Applied Learning: Uncover actionable insights that retailers could use to enhance their strategies and customer experiences.

Questions to Explore:

• How does customer age and gender influence their purchasing behavior?
• Are there discernible patterns in sales across different time periods?
• Which product categories hold the highest appeal among customers?
• What are the relationships between age, spending, and product preferences?
• How do customers adapt their shopping habits during seasonal trends?
• Are there distinct purchasing behaviors based on the number of items bought per transaction?
• What insights can be gleaned from the distribution of product prices within each category?

Your EDA Journey:

Prepare to immerse yourself in a world of data-driven exploration. Through data visualization, statistical analysis, and correlation examination, you'll uncover the nuances that define retail operations and customer dynamics. EDA isn't just about numbers—it's about storytelling with data and extracting meaningful insights that can influence strategic decisions.

Embrace the Retail Sales and Customer Demographics Dataset as your canvas for discovery. As you traverse the landscape of this synthetic retail environment, you'll refine your analytical skills, pose intriguing questions, and contribute to the ever-evolving narrative of the retail industry. Happy exploring!

This statistic gives information on the year-on-year revenue growth of leading e-retail categories in the United States in 2017. During the measured period, online apparel sales grew **** percent compared to the previous year.

In 2024, the online retail sales of food increased by around ** percent in China compared to the previous year. Meanwhile, clothing saw an increase of *** percent in online retail sales.

Graph and download economic data for Retail Sales: Department Stores (SM4522USN) from Jan 1992 to May 2025 about retail trade, sales, retail, and USA.

Descriptions and categories of the Internet Sales Index and their percentage of all retailing for Great Britain.

This dataset contains comprehensive sales transaction data from the retail sector in India, specifically focusing on the processed meats industry. It spans various retail segments including personal usage, restaurants, hotels, and hospitals. Each record in the dataset represents a sales order with information about the product category, pricing, shipping methods, profit margins, and geographic details across different regions of India.

Key Features: Order Priority: Defines the priority of the sales order (e.g., High, Low). Discount Offered: The discount applied to each sale. Unit Price: The price per unit of the product sold. Freight Expenses: Shipping costs associated with each order. Freight Mode: The mode of transportation used (e.g., Regular Air, Express Air). Segment: Retail segment such as Personal Usage, Hotels, Hospitals, or Restaurant Chains. Product Information: Includes the product type, sub-category, and packaging information. Geographic Information: State, city, and region within India where the transaction took place. Order and Ship Dates: Date of order placement and shipment. Profit: Profit margin from the sale. Quantity Ordered: Number of units ordered. Sales: Total sales amount generated.

Graph and download economic data for Retail Sales: Discount Department Stores (DISCONTINUED) (MRTSMPCSM452112EUSN) from Feb 1992 to Feb 2025 about discount, retail trade, sales, retail, and USA.

United States Retail Sales: sa: Department stores ex Leased Departments (DS) data was reported at 12.360 USD bn in Sep 2018. This records a decrease from the previous number of 12.454 USD bn for Aug 2018. United States Retail Sales: sa: Department stores ex Leased Departments (DS) data is updated monthly, averaging 16.813 USD bn from Jan 1992 (Median) to Sep 2018, with 321 observations. The data reached an all-time high of 19.904 USD bn in Jan 2001 and a record low of 12.325 USD bn in Nov 2016. United States Retail Sales: sa: Department stores ex Leased Departments (DS) data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H001: Retail Sales: By NAIC System. All estimates for department stores exclude leased departments.

Graph and download economic data for Advance Retail Sales: Department Stores (4522MPCSMN) from Feb 1992 to May 2025 about retail trade, sales, retail, and USA.

A comprehensive dataset providing key insights into the eCommerce industry, including global retail online sales projections, number of eCommerce stores, digital buyer statistics, revenue growth in the United States, sector-wise revenue details with a focus on consumer electronics, average conversion rates, and mobile commerce sales forecasts.

In 2017, global retail sales of licensed apparel amounted to ***** billion U.S. dollars. The United States and Canada were the largest market for licensed merchandise and services as of 2018, accounting for ** percent of the world's retail sales.

A detailed dataset exploring the retail industry in 2025, including market size, store counts, revenue trends, AI integration, and consumer behavior across the US and globally.

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E-Commerce Retail Market Size 2025-2029

The e-commerce retail market size is forecast to increase by USD 4,833.5 billion at a CAGR of 12% between 2024 and 2029.

The market is experiencing significant growth, driven by the advent of personalized shopping experiences. Consumers increasingly expect tailored recommendations and seamless interactions, leading retailers to integrate advanced technologies such as Artificial Intelligence (AI) to enhance the shopping journey. However, this market is not without challenges. Strict regulatory policies related to compliance and customer protection pose obstacles for retailers, requiring continuous investment in technology and resources to ensure adherence.
Retailers must navigate these challenges to effectively capitalize on the market's potential and deliver value to customers. By focusing on personalization and regulatory compliance, e-commerce retailers can differentiate themselves, build customer loyalty, and ultimately thrive in this dynamic market. Balancing the need for innovation with regulatory requirements is a delicate task, necessitating strategic planning and operational agility. Fraud prevention and customer retention are crucial aspects of e-commerce, with payment gateways ensuring secure transactions.

What will be the Size of the E-Commerce Retail Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic market, shopping carts and checkout processes streamline transactions, while sales forecasting and marketing automation help businesses anticipate consumer demand and optimize promotions. SMS marketing and targeted advertising reach customers effectively, driving sales growth. Warranty claims and customer support chatbots ensure post-purchase satisfaction, bolstering customer loyalty. Retail technology advances, including sustainable packaging, green logistics, and mobile optimization, cater to environmentally-conscious consumers. Legal compliance, data encryption, and fraud detection safeguard businesses and consumer trust. Product reviews, search functionality, and personalized recommendations enhance the shopping experience, fostering customer engagement.
Dynamic pricing and delivery networks adapt to market fluctuations and consumer preferences, respectively. E-commerce software integrates various functionalities, from circular economy initiatives and website accessibility to email automation and real-time order tracking. Overall, the e-commerce landscape continues to evolve, with businesses adopting innovative strategies to meet the needs of diverse customer segments and stay competitive.

How is this E-Commerce Retail Industry segmented?

The e-commerce retail industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Apparel and accessories
  Groceries
  Footwear
  Personal and beauty care
  Others


Modality

  Business to business (B2B)
  Business to consumer (B2C)
  Consumer to consumer (C2C)


Device

  Mobile
  Desktop


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Product Insights

The apparel and accessories segment is estimated to witness significant growth during the forecast period. The market for apparel and accessories is experiencing significant growth, fueled by several key trends. Increasing consumer affluence and a shift toward premiumization are driving this expansion, with the organized retail sector seeing particular growth. Influenced by social media trends, the Gen Z demographic is a major contributor to this rise in online shopping. This demographic is known for their preference for the latest fashion trends and their willingness to invest in premium products, making them a valuable market segment. Machine learning and artificial intelligence are increasingly being used for returns management and personalized recommendations, enhancing the customer experience.

Ethical sourcing and supply chain optimization are also essential, as consumers demand transparency and sustainability. Cybersecurity threats continue to pose challenges, requiring robust strategies and technologies. B2C and C2C e-commerce are thriving, with influencer marketing and e-commerce analytics playing significant roles. Customer reviews are essential for building trust and brand loyalty, while reputation management and affiliate marketing help expand reach. Sustainable e-commerce and b2b e-commerce are also gaining traction, with third-party logistics and social commerce offering new opportunitie

Retail Sales Survey: Retail Sales Index data was reported at 236.674 2010=100 in Mar 2025. This records an increase from the previous number of 218.486 2010=100 for Feb 2025. Retail Sales Survey: Retail Sales Index data is updated monthly, averaging 197.739 2010=100 from Jan 2010 (Median) to Mar 2025, with 183 observations. The data reached an all-time high of 249.786 2010=100 in May 2019 and a record low of 90.600 2010=100 in Feb 2010. Retail Sales Survey: Retail Sales Index data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Global Database’s Indonesia – Table ID.HD001: Retail Sales Survey: Real Retail Sales Index by Categories. [COVID-19-IMPACT]

United States Retail Sales: Department Stores inc Leased Departments data was reported at 12.444 USD bn in Mar 2018. This records an increase from the previous number of 10.352 USD bn for Feb 2018. United States Retail Sales: Department Stores inc Leased Departments data is updated monthly, averaging 16.083 USD bn from Jan 1992 (Median) to Mar 2018, with 315 observations. The data reached an all-time high of 35.437 USD bn in Dec 2000 and a record low of 9.712 USD bn in Jan 2017. United States Retail Sales: Department Stores inc Leased Departments data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H001: Retail Sales: By NAIC System.

Analysis of ‘USA Monthly Retail Sales’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/landlord/usa-monthly-retail-trade on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Introduction

The dataset contains the Monthly sales for retail trade and food services in USA, adjusted and unadjusted for seasonal variations for various categories. These categories shows various kind of Business categories operating in USA. These categories are based on North American Industry Classification System (NAICS).

Dataset Description

• The dataset contains the estimates of Monthly Retail and Food Services Sales by Kind of Business from the year 1992 - 2020. These estimates are shown in millions of dollars and are based on data from the Monthly Retail Trade Survey, Annual Retail Trade Survey, * Service Annual Survey, and administrative records.
• Their are another to files that contain the monthly data for the code NAICS code 44X72: Retail Trade and Food Services: U.S. Total for both Seasonally Adjusted Sales and non Seasonally Adjusted Sales in Millions of Dollars from 1992 to 2020.
• An helper file for NAICS code for retail and food industry is also provided for reference

Acknowledgements

The Dataset was published on U.S. Census Bureau website (https://www.census.gov)

--- Original source retains full ownership of the source dataset ---

Retail Trade, sales by industries based on North American Industry Classification System (NAICS), monthly.

Graph and download economic data for Value Added by Industry: Retail Trade (Chain-Type Price Index) (VAPIPR) from Q2 2005 to Q1 2025 about value added, chained, retail trade, private industries, sales, retail, private, industry, rate, price index, indexes, price, and USA.

Overview This dataset contains 100,000 records of retail store sales data from various outlets across India over the past 5 years (2019-2023). It includes information about customer purchases, product categories, sales, discounts, and profit margins across different regions and outlet types.

This dataset is useful for sales analysis, customer segmentation, trend forecasting, and machine learning applications in the retail industry.

Dataset Features This dataset consists of 21 columns:

1. Customer & Order Information Order ID – Unique identifier for each transaction Order Date – Date when the order was placed Ship Date – Date when the order was shipped Ship Mode – Shipping method (Standard Class, Second Class, First Class, Same Day) Customer ID – Unique customer identifier Customer Name – First name of the customer Last Name – Last name of the customer Segment – Customer type (Consumer, Corporate)
2. Store & Location Information City Type – Tier 1, Tier 2, or Village Region – North, South, East, or West State – Indian state where the purchase was made Postal Code – Postal code for the purchase location
3. Product Information Product ID – Unique identifier for each product Category of Goods – Product category (Furniture, Electric Appliances, Fruits & Vegetables, Household, Dairy, Fast Food) Sub-Category – Specific subcategories under each category Product Name – Randomized product names based on subcategory
4. Sales & Profit Information Sales – Purchase amount in Indian Rupees (₹) Quantity – Number of items purchased Discount – Discount applied (0% - 50%) Profit – Profit calculated after applying discount Possible Use Cases Sales Trend Analysis – Identify yearly and regional sales patterns Customer Segmentation – Analyze customer behavior across different demographics Market Basket Analysis – Find product associations using ML techniques Sales Forecasting – Predict future sales based on past trends Profitability Analysis – Assess store and product category profitability Why Use This Dataset? ✔ Large Dataset (100K rows) – Ideal for machine learning & analytics ✔ Multi-Year Data (2019-2023) – Enables trend analysis over time ✔ Retail Industry Focused – Useful for sales and marketing strategies ✔ Includes Discounts & Profits – Helps in financial modeling

License & Citation This dataset is provided for educational and research purposes. If you use it, please cite it accordingly.

Would you like me to provide a dataset-metadata.json file for Kaggle, which will make uploading even easier? 😊