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

This statistic shows the retail sales value in Saudi Arabia in 2018, with estimates from 2019 to 2025. In 2018, the retail sales value amounted to ***** billion U.S. dollars. It was estimated that the retail sales value would grow until 2025, reaching around ***** billion U.S. dollars.

In 2020, global retail sales fell by 2.9 percent as a result of the COVID-19 pandemic, bouncing back in 2021 with a growth of 9.7 percent Global retail sales were projected to amount to around 27.3 trillion U.S. dollars by 2022, up from approximately 23.7 trillion U.S. dollars in 2020.

American retailers worldwide
As a result of globalization and various trade agreements between markets and countries, many retailers are capable of doing business on a global scale. Many of the world’s leading retailers are American companies. Walmart and Amazon are examples of such American retailers. The success of U.S. retailers can also be seen through their performance in online retail.

Retail in the U.S.
The domestic retail market in the United States is a lucrative market, in which many companies compete. Walmart, a retail chain offering low prices and a wide selection of products, is the leading retailer in the United States. Amazon, The Kroger Co., Costco, and Target are a selection of other leading U.S. retailers.

Grocery Sales Prediction

This dataset provides a rich resource for researchers and practitioners interested in retail sales prediction and analysis. It contains information about various grocery products, the outlets where they are sold, and their historical sales data.

Product Characteristics:

Item_Identifier: Unique identifier for each product. Item_Weight: Weight of the product item. Item_Fat_Content: Categorical variable indicating the fat content of the product (e.g., low fat, regular). Item_Visibility: Numerical attribute reflecting the visibility of the product in the store (likely a promotional measure). Item_Type: Category of the product (e.g., Snacks, Beverages, Bakery). Item_MRP: Maximum Retail Price of the product. Outlet Information:

Outlet_Identifier: Unique identifier for each outlet (store). Outlet_Establishment_Year: Year the outlet was established. Outlet_Size: Categorical variable indicating the size of the outlet (e.g., Small, Medium, Large). (Note: This data may have missing values) Outlet_Location_Type: Categorical variable indicating the type of location the outlet is in (e.g., Tier 1 City, Tier 2 City, Upstate). Outlet_Type: Categorical variable indicating the type of outlet (e.g., Supermarket, Grocery Store, Convenience Store). Sales Data:

Item_Outlet_Sales: The historical sales data for each product-outlet combination. Profit: The profit margin earned on each product sold. Potential Uses

This dataset can be used for various retail sales analysis and prediction tasks, including:

Demand forecasting: Build models to predict future sales of individual products or product categories at specific outlets. Promotion optimization: Analyze the effectiveness of different promotional strategies (reflected by Item_Visibility) on sales. Assortment planning: Optimize product selection and placement within stores based on sales history and outlet characteristics. Outlet performance analysis: Compare the performance of different outlets based on sales figures and profit margins. Customer segmentation: Identify customer segments with distinct purchasing behavior based on product types and outlet locations. By analyzing these rich data points, retailers can gain valuable insights to improve their sales strategies, optimize inventory management, and maximize profits.

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!

Retail Sales in Finland increased 1.20 percent in July of 2025 over the previous month. This dataset provides - Finland Retail Sales MoM - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States Retail Sales Nowcast: sa: YoY data was reported at 4.089 % in 12 May 2025. This records an increase from the previous number of 3.963 % for 05 May 2025. United States Retail Sales Nowcast: sa: YoY data is updated weekly, averaging 3.924 % from Feb 2020 (Median) to 12 May 2025, with 274 observations. The data reached an all-time high of 44.471 % in 17 May 2021 and a record low of -13.873 % in 25 May 2020. United States Retail Sales Nowcast: sa: YoY data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s United States – Table US.CEIC.NC: CEIC Nowcast: Retail Sales.

Graph and download economic data for Advance Retail Sales: Retail Trade (RSXFS) from Jan 1992 to Aug 2025 about retail trade, sales, retail, services, and USA.

Based on a forecast, retail sales revenues in Germany will amount to over *** billion euros in 2025. Figures are expected to increase annually. This timeline shows the retail sales revenue development in Germany from 2011 to 2025.

Key information about France Retail Sales Growth

• France Retail Sales grew 1.6 % YoY in Dec 2024, compared with a 1.8 % increase in the previous month.
• France Retail Sales Growth YoY data is updated monthly, available from Jan 1996 to Dec 2024, with an average growth rate of 2.4 %.
• The data reached an all-time high of 43.9 % in Apr 2021 and a record low of -31.5 % in Apr 2020.
• In the latest reports, Car Sales of France recorded 1,929,554.0 units in Dec 2022, representing a drop of 9.9 %.

CEIC calculates monthly Retail Sales: Excl. Motor Vehicles Growth from monthly Retail Trade Index. The National Institute of Statistics and Economic Studies provides Retail Trade Index with base 2021=100. Retail Sales: Excl. Motor Vehicles Growth prior to January 2006 is calculated from Retail Trade Index with base 2010=100.

Key information about Georgia Retail Sales Growth

• Georgia Retail Sales grew 1.1 % YoY in Dec 2024, compared with a 6.8 % increase in the previous quarter.
• Georgia Retail Sales Growth YoY data is updated quarterly, available from Mar 2010 to Dec 2024, with an average growth rate of 14.6 %.
• The data reached an all-time high of 109.2 % in Dec 2012 and a record low of -14.5 % in Jun 2020.
• In the latest reports, Car Sales of Georgia recorded 3,400.0 units in Dec 2019, representing a drop of 3.4 %.

CEIC calculates quarterly Retail Sales: Excl. Motor Vehicles Growth from quarterly Retail Trade Turnover. The National Statistics Office of Georgia provides Retail Trade Turnover in local currency. Retail Sales: Excl. Motor Vehicles Growth prior to Q1 2018 is based on NACE Rev. 1.1.

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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 opportunities. Augment

This statistic shows the forecast retail sales of dessert mix in the United States from 2018 to 2022. By 2022, retail sales of dessert mix in the United States were forecast to reach *** billion U.S. dollars.

Retail Sales in European Union decreased 0.40 percent in July of 2025 over the previous month. This dataset provides - European Union Retail Sales MoM - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The historical sales dataset for this research is obtained from a Bangladeshi retailer. The dataset covers a period of 1826 days and includes daily sales data for a particular product from 01 January 2013 to 31 December 2017. The raw sales data has 2 columns: the first column contains timestamps, while the remaining column reflects the quantity sold.

In 2021, retail sales of frozen baked goods in the United States were approximately **** billion U.S. dollars. By 2026, they are forecast to reach almost **** billion U.S. dollars.

Graph and download economic data for Retailers Sales (RETAILSMSA) from Jan 1992 to Jul 2025 about retail trade, sales, retail, and USA.

Retail Sales in Estonia decreased 0.50 percent in July of 2025 over the previous month. This dataset provides - Estonia Retail Sales MoM - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Retail Fashion Boutique Data Sales Analytics 2025

Overview

This comprehensive fashion retail synthetic dataset contains 2,176 real-world style records spanning seasonal collections, customer purchasing behavior, pricing strategies, and return analytics. Perfect for data science projects, machine learning models, and business intelligence dashboards focused on retail analytics and e-commerce insights.

Dataset Highlights

• 📊 Complete Sales Cycle: Purchase patterns, pricing strategies, and customer feedback
• 🔄 Return Analytics: Detailed return tracking with specific reasons and patterns
• 🛍️ Multi-Brand Coverage: 8 major fashion brands across diverse product categories
• 📈 Seasonal Intelligence: Four-season data with realistic markdown strategies
• ⭐ Customer Insights: Rating systems and purchasing behavior analysis
• 💰 Pricing Analytics: Original pricing, markdown percentages, and final pricing data

Key Applications

• Retail Analytics: Sales performance analysis and trend identification
• Customer Segmentation: Behavior analysis and purchasing pattern recognition
• Inventory Management: Stock optimization and seasonal demand forecasting
• Return Prediction: Machine learning models for return likelihood prediction
• Pricing Strategy: Dynamic pricing and markdown optimization analysis
• Business Intelligence: Comprehensive retail KPI dashboards and reporting

Column Details

Column Name	Data Type	Description	Business Impact
product_id	String	Unique product identifier (FB000001-FB002176)	Product tracking and inventory management
category	Categorical	Product type (Dresses, Tops, Bottoms, Outerwear, Shoes, Accessories)	Category performance analysis
brand	Categorical	Fashion brand name (Zara, H&M, Forever21, Mango, Uniqlo, Gap, Banana Republic, Ann Taylor)	Brand comparison and market positioning
season	Categorical	Collection season (Spring, Summer, Fall, Winter)	Seasonal trend analysis and forecasting
size	Categorical	Clothing size (XS, S, M, L, XL, XXL) - Null for accessories	Size demand optimization
color	Categorical	Product color (Black, White, Navy, Gray, Beige, Red, Blue, Green, Pink, Brown, Purple)	Color preference analysis
original_price	Numerical	Base product price ($15.14 - $249.98)	Pricing strategy development
markdown_percentage	Numerical	Discount percentage (0% - 59.9%)	Markdown effectiveness analysis
current_price	Numerical	Final selling price after discounts	Revenue and margin analysis
purchase_date	Date	Transaction date (2024-2025 range)	Time series analysis and seasonality
stock_quantity	Numerical	Available inventory (0-50 units)	Inventory optimization
customer_rating	Numerical	Product rating (1.0-5.0 scale) - Includes nulls	Quality assessment and customer satisfaction
is_returned	Boolean	Return status (True/False)	Return rate calculation and analysis
return_reason	Categorical	Specific return reason (Size Issue, Quality Issue, Color Mismatch, Damaged, Changed Mind, Wrong Item)	Return pattern analysis

Data Quality Features

• ✅ Realistic Business Logic: 15% return rate matching industry standards
• ✅ Seasonal Pricing: Authentic markdown patterns aligned with retail cycles
• ✅ Missing Data Handling: Strategic nulls for data cleaning practice (15% in ratings, size nulls for accessories)
• ✅ Balanced Distribution: Even representation across brands, categories, and seasons
• ✅ Price Consistency: Mathematically accurate pricing with discount calculations

Perfect For

• Data Analytics Projects: Retail KPI analysis, sales forecasting, customer behavior studies
• Machine Learning Models: Return prediction, demand forecasting, recommendation systems
• Business Intelligence: Executive dashboards, performance tracking, trend analysis
• Academic Research: Retail analytics case studies, pricing strategy research
• Portfolio Development: Comprehensive data science project demonstrations

File Formats Available

• CSV: Universal compatibility for data analysis tools
• Excel: Business reporting and stakeholder presentations
• JSON: API integration and web applications
• SQL: Database integration and advanced querying

Sample Use Cases

1. Return Prediction Model: Build ML models to predict return likelihood based on product attributes
2. Seasonal Demand Forecasting: Analyze purchasing patterns across different seasons and categories
3. Pricing Optimization: Study markdown effectiveness and optimal pricing strategies
4. Customer Satisfaction Analysis: Correlate ratings with return patterns and product characteristi...

Retail Sales - Table 620-67001 : Total Retail Sales