Total retail sales in the United States were forecast to amount to **** trillion U.S. dollars in 2025, up by ** billion U.S. dollars in the previous year. Retail establishments come in many forms such as grocery stores, restaurants, and bookstores. There are around ************ retail establishments in the United States. Leading companies in U.S. retail The domestic retail market in the United States is very competitive, with many companies recording substantial retail sales. 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. American retailers worldwide Many of the world’s leading retailers are American companies. Walmart and Amazon are examples of American retailers doing business on a global scale. The success of U.S. retailers can also be seen through their performance in online retail. Amazon is a prime example of this, with the company’s sales revenue flourishing over the previous years.

This dataset provides a comprehensive collection of key performance indicators (KPIs) for retail stores, offering insights into factors influencing store performance, customer engagement, and financial outcomes. The dataset is suitable for various machine learning and data analysis tasks, including regression, classification, and clustering. It can help in understanding the relationships between operational metrics, store characteristics, and sales performance.

Global retail sales were projected to amount to around **** trillion U.S. dollars by 2026, up from approximately **** trillion U.S. dollars in 2021. The retail industry encompasses the journey of a good or service. This typically starts with the manufacturing of a product and ends with said product being purchased by a consumer from a retailer. Retail establishments come in many forms such as grocery stores, restaurants, and bookstores. 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.

Retailers using artificial intelligence (AI) and machine learning (ML) technologies performed better than their competitors. Both in 2023 and 2024, retail companies using this kind of technologies saw a ********* growth of their sales compared to the respective previous years. Similarly, their annual profit grew by roughly ***** percent, outperforming retailers who did not use AI or ML solutions.

This dataset simulates data for a retail business, designed for Business Intelligence (BI) analysis, data visualization, and machine learning applications. The data covers multiple aspects of a retail environment, including sales, customer behavior, employee performance, inventory management, marketing campaigns, and operational costs.

It is ideal for exploring topics like sales forecasting, customer segmentation, inventory optimization, campaign ROI analysis, and performance evaluation.

Features: The dataset is structured into multiple tables, each representing a key entity in the retail business:

Lojas (Stores):

Loja_ID: Unique identifier for the store. Nome: Name of the store. Regiao: Region where the store is located. Cidade: City where the store is located. Tipo: Type of store (e.g., physical, online). Produtos (Products):

Produto_ID: Unique identifier for the product. Nome: Name of the product. Categoria: Category of the product. Preco: Price of the product. Custo_Aquisicao: Acquisition cost. Clientes (Customers):

Cliente_ID: Unique identifier for the customer. Nome: Name of the customer. Idade: Age of the customer. Genero: Gender. Cidade: City of residence. Canal_Compra: Preferred purchase channel. Total_Compras: Total spending. Vendas (Sales):

Venda_ID: Unique identifier for the sale. Loja_ID: Store where the sale occurred. Produto_ID: Product sold. Cliente_ID: Customer making the purchase. Colaborador_ID: Employee involved in the sale. Quantidade: Quantity sold. Preco_Unitario: Price per unit. Data: Date of sale. Canal: Sales channel (e.g., online, in-store). Colaboradores (Employees):

Colaborador_ID: Unique identifier for the employee. Loja_ID: Store where the employee works. Nome: Employee's name. Funcao: Job role. Horas_Trabalhadas_Semanais: Weekly working hours. Avaliacao_Desempenho: Performance rating. Vendas_Realizadas: Sales completed by the employee. Naturalidade: Place of origin. Campanhas (Marketing Campaigns):

Campanha_ID: Unique identifier for the campaign. Nome: Campaign name. Canal: Marketing channel. Investimento: Investment made in the campaign. Vendas_Geradas: Sales generated by the campaign. Data_Inicio: Start date. Data_Fim: End date. Stock (Inventory):

Produto_ID: Product identifier. Quantidade: Current stock level. Max: Maximum stock level. Min: Minimum stock level. Tempo_Entrega: Delivery time. Devolucoes (Returns):

Devolucao_ID: Unique identifier for the return. Venda_ID: Sale associated with the return. Produto_ID: Product being returned. Cliente_ID: Customer making the return. Quantidade: Quantity returned. Motivo_Devolucao: Reason for return. Data_Devolucao: Return date. Custos_Operacionais (Operational Costs):

Custo_ID: Unique identifier for the cost. Loja_ID: Store associated with the cost. Tipo_Custo: Type of cost (e.g., rent, utilities). Valor_Mensal: Monthly cost amount. Data: Cost recording date. Product Reviews:

Review_ID: Unique review identifier. Produto_ID: Reviewed product. Avaliacao: Rating (e.g., 1-5 stars). Comentario: Customer comment. Data: Date of the review. Use Cases: Data Visualization: Create dashboards for tracking sales, inventory, and employee performance. Machine Learning: Build models for predicting sales, identifying customer churn, or optimizing stock levels. Statistical Analysis: Analyze customer demographics, product performance, or campaign ROI. Scenario Simulation: Explore the impact of marketing campaigns or inventory changes on sales. Data Format: All tables are provided as CSV files. Each table is normalized to reflect relational database structures, with foreign keys linking related tables. Additional Notes: All data is synthetic and generated using Python scripts with libraries like Faker and pandas. The dataset does not represent real-world entities or behaviors but is modeled to closely mimic actual retail operations.

Data Overview

• Scale: All sales figures are reported in millions of dollars.
• Size: The dataset contains 40,479 rows and 5 columns.
• Time Frame: January 1992 - September 2024.
• Industries Covered: Over 60 industries, including food, clothing, footwear, office supplies, automobiles, electronics, books, beverages, furniture, grocery and many more.
• Attributes:
  • naics_code
  • kind_of_business
  • sales_month
  • sales
  • estimate_type
• Source: This dataset was sourced from the publicly available U.S. Census Bureau retail sales data.

Cleaning & Preprocessing

• Missing Values:
  Some entries contained (NA) and (S) values, which were converted to null values.
  • (S): Estimate does not meet publication standards due to high sampling variability (coefficient of variation greater than 30%) or poor response quality (low total quantity response rate).
• Formatting:
  The downloaded data included headings, subheadings, and notes embedded within the tables. These extraneous elements were removed to ensure a clean and consistent dataset.
• Data Compilation:
  The original dataset was spread across multiple sheets, with each sheet containing data for a specific year. These sheets were consolidated into a single, unified table.
• Feature Engineering:
  A new column was created to provide both seasonally adjusted and non-seasonally adjusted sales values, enabling more nuanced analysis. Estimates are adjusted for seasonal variations, as well as holiday and trading-day differences, but not for price changes.

Use Cases

This dataset can be applied to a variety of analytical and machine learning tasks, including:

• Data Cleaning: Practice handling missing values, stray entries, and working with datetime data.
• Time Series Analysis: Perform trend analysis, seasonality detection, and forecasting.
• Exploratory Data Analysis (EDA): Gain insights into industry-specific trends and patterns.
• Machine Learning: Use it for predictive modeling and classification tasks.
• Market Research: Analyze industry performance to inform business strategies.

View monthly updates and historical trends for US Retail Sales. from United States. Source: Census Bureau. Track economic data with YCharts analytics.

All restaurants and food stores selling perishable items are required to apply for a Retail Food License (RFL). This metric tracks the average number of days the Department of Business Affairs and Consumer Protection (BACP) takes to issue RFLs. The target response time for processing is within 15 days.

The Retail Trade sector entered 2025 on a muted footing, with revenue growth of just 0.2% to reach $7.4 trillion. E-commerce remains a bright spot, with steady mid-single-digit gains in recent years, boosted by younger consumers' strong preference for digital channels. Yet, the sector's gains in digital shopping are balanced by ongoing challenges in discretionary spending, high operating costs and tariffs that threaten earnings. Profit has been pressured by steep price competition online and inflation-related expenses, though essential retailers in sub-sectors like food and health have managed steadier performance. Current efforts around omnichannel strategies, technology-driven efficiencies and sustainability reflect the sector's dual focus: capturing digital momentum while offsetting erosion in traditional store-based sales. Over the current period, the sector's revenue expanded at a modest CAGR of 2.2%, highlighting how the pandemic's volatility gave way to cautious but relatively stable expansion. Revenue streams benefited from major operations like Target, Walmart and Amazon reshaping retail into one-stop ecosystems that blend products and services, diversifying into groceries, healthcare, beauty and wellness. Automation adoption--from self-checkout kiosks to advanced inventory management--helped mitigate rising wage costs and sharpened efficiency, while marketing automation improved customer engagement through more tailored promotions. Still, profit took hits from inflation, heightened competition and consumers trading down to value alternatives amid tightening budgets. Consumer priorities for sustainability have altered market dynamics, leading to investments in resale programs and greener programs. The sector's growth is expected to slow, with revenue climbing at an anticipated 1.3% CAGR through 2030, reaching $7.9 trillion. While consumer disposable income is set to strengthen modestly, fragile sentiment from inflation, tariffs and labor market uncertainty may temper spending power. Technology will be a key driver in reshaping operations and growth opportunities. AI is poised to enhance inventory control, price optimization, delivery logistics and fraud prevention. Extended reality innovations, from AR try-ons to immersive VR shopping, will engage younger consumers and potentially redefine customer experiences, though costs and adoption hurdles remain. Reverse logistics and the circular economy will gain ground as sustainability priorities align with value-seeking behavior. Discounters and warehouse clubs are expected to capture share in the near term as households continue trading down, though specialty and discretionary retail could stage a rebound later in the outlook period as consumer confidence improves.

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.

Description

This dataset is designed for analyzing customer behavior and predicting customer churn in a retail store. With 5,329 samples and 19 independent variables, the dataset provides a comprehensive view of various factors that influence whether a customer will continue their engagement with the store or not. The primary goal is to derive actionable insights and trends that can improve overall business performance, particularly in reducing customer churn.

Dependent Variable

Customer Churn Indicator: This binary variable indicates whether a customer has churned (i.e., stopped engaging with the retail store) or not. It serves as the target variable for the machine learning model.

Independent Variables

1. Customer Information: Customer ID: Unique identifier for each customer. Gender: Gender of the customer (Male/Female). Marital Status: Indicates whether the customer is single, married, divorced, etc. Number of Complaints: Total number of complaints filed by the customer to the retail store. Total Orders (1 month): Number of orders placed by the customer in the last month.

2. Transaction Information: Preferred Log-In Device: The type of device type used by the customer to connect to the retail store for purchases (e.g., mobile phone, computer). Payment Method: The payment method preferred by the customer (e.g., Credit Card, UPI). Product Category: The category to which the purchased products belong. Distance from Warehouse: The distance between the retail store's warehouse and the customer's location.

Objective

The main objective of analyzing this dataset is to predict customer churn and understand the factors contributing to it. By doing so, the retail store can develop targeted strategies for customer retention, optimize marketing efforts, and improve overall customer satisfaction.

Use Case

The insights gained from this analysis will be invaluable for the store's management and marketing teams. They can identify patterns and trends related to customer churn, enabling them to take proactive steps to retain valuable customers, address customer complaints effectively, and tailor marketing campaigns to specific customer segments. The ultimate goal is to enhance business performance by reducing churn and increasing customer loyalty.

Total retail sales in the United States were projected to amount to *** trillion U.S. dollars in 2028, up from around * trillion U.S. dollars in 2022. These figures included e-commerce and retail sales. Retail establishments come in many forms such as grocery stores, restaurants, and bookstores. There are around ************ retail establishments in the United States. Leading companies in U.S. retail The domestic retail market in the United States is very competitive, with many companies recording substantial retail sales. 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. American retailers worldwide Many of the world’s leading retailers are American companies. Walmart and Amazon are examples of American retailers doing business on a global scale. The success of U.S. retailers can also be seen through their performance in online retail. Amazon is a prime example of this, with the company’s sales revenue flourishing over the previous years.

In 2023, fashion and accessories contributed to ** percent of growth in the fashion industry in the United Arab Emirates. Watches and jewelry contributed the remaining ** percent of growth in the industry. The overall contribution of the fashion sector to the retail spending in this period was ** percent, which was a ** percent increase over the previous year.

E-commerce and retail datasets provide valuable insights into consumer behavior, market trends, and business performance. These datasets help companies optimize pricing, enhance marketing strategies, improve inventory management, and increase sales conversions. By leveraging data-driven decision-making, businesses can stay competitive and meet evolving customer demands. Benefits and Impact: Enhanced predictive accuracy for demand forecasting and price […]

In 2018, the total year-end net asset value of the ** surveyed key department store operators in China increased by *** percent compared to the previous year. The net profits, core operating profits, total sales revenue, and operating area also grew in 2018, whereas the average number of employees and total expenses decreased.

Store Performance Analytics Market Outlook

According to our latest research, the global store performance analytics market size reached USD 4.2 billion in 2024, reflecting robust adoption across the retail sector. The market is projected to grow at a CAGR of 15.7% from 2025 to 2033, with the market expected to reach USD 15.3 billion by 2033. This growth is propelled by the increasing focus on data-driven decision-making, the rising adoption of cloud-based analytics solutions, and the growing need for real-time insights to optimize retail operations.

One of the primary growth factors driving the store performance analytics market is the rapid digital transformation within the retail sector. Retailers are increasingly relying on analytics platforms to gain actionable insights into customer behavior, sales trends, and inventory management. The proliferation of omnichannel retailing and the integration of advanced technologies such as artificial intelligence and machine learning have further amplified the need for sophisticated analytics tools. These solutions enable retailers to personalize customer experiences, optimize product assortments, and streamline operations, thereby enhancing overall store performance and profitability. The competitive nature of the retail industry is compelling organizations to invest in analytics to maintain a strategic edge, minimize operational costs, and maximize revenue opportunities.

Another significant growth factor is the increasing volume and complexity of data generated by retail stores. With the advent of IoT devices, smart shelves, and connected POS systems, retailers are collecting vast amounts of data related to foot traffic, customer preferences, and transactional details. Store performance analytics platforms are essential for aggregating, processing, and analyzing this data to extract meaningful insights. The ability to visualize key performance indicators in real-time empowers store managers to make informed decisions quickly, such as adjusting staffing levels, modifying product displays, or launching targeted promotions. As retailers strive to deliver seamless and personalized shopping experiences, the demand for advanced analytics solutions will continue to surge, driving sustained growth in the market.

The shift towards cloud-based deployment models is another catalyst for market expansion. Cloud-based store performance analytics solutions offer scalability, flexibility, and cost-effectiveness, making them particularly attractive to small and medium-sized enterprises (SMEs) and large enterprises alike. These solutions facilitate seamless integration with existing IT infrastructure and provide access to advanced analytics capabilities without the need for significant upfront investments in hardware or software. Furthermore, cloud platforms enable retailers to access real-time insights from any location, supporting multi-store operations and facilitating centralized decision-making. The growing acceptance of cloud technology, coupled with advancements in data security and privacy, is expected to further accelerate the adoption of store performance analytics solutions globally.

From a regional perspective, North America continues to dominate the store performance analytics market, accounting for the largest share in 2024. This dominance is attributed to the high concentration of retail giants, early adoption of advanced technologies, and a strong emphasis on customer experience optimization. However, the Asia Pacific region is witnessing the fastest growth, driven by the rapid expansion of organized retail, increasing digitalization, and rising consumer expectations. Europe also holds a significant market share, supported by the presence of established retail chains and a growing focus on operational efficiency. Latin America and the Middle East & Africa are emerging markets, with increasing investments in retail infrastructure and technology adoption expected to drive future growth.

Component Analysis

The component segment of the store performance analytics market is bifurcated into software and services, each playing a pivotal role in the ecosystem. Software solutions are at the core of the market, providing robust platforms for data collection, integration, visualization, and reporting. These solutions are designed to handle massive data volumes and deliver actionable insights through intuitive dashboards and real-time analytics. The

The global retail industry, valued at $32.68 billion in 2025, is projected to experience robust growth, driven by several key factors. E-commerce continues its rapid expansion, fueled by increasing internet penetration and consumer preference for online convenience. Simultaneously, the rise of omnichannel strategies, integrating online and offline retail experiences, is enhancing customer engagement and driving sales. The increasing adoption of advanced technologies, such as artificial intelligence (AI) for personalized recommendations and supply chain optimization, is further boosting efficiency and profitability within the sector. Growth is also being fueled by shifting consumer preferences towards sustainable and ethically sourced products, prompting retailers to adapt their offerings and supply chains accordingly. Different product categories exhibit varying growth trajectories; for example, the food, beverage, and grocery segment is expected to maintain steady growth, while the personal and household care sector may experience accelerated growth due to changing lifestyle choices and heightened health consciousness. Geographical distribution reveals that North America and Asia-Pacific currently dominate the market, however, emerging markets in Africa and South America present significant untapped potential for future expansion. Competition remains fierce, with established giants like Walmart and Amazon facing challenges from smaller, agile businesses utilizing innovative marketing and fulfillment strategies. Despite the positive outlook, the retail industry faces certain headwinds. Supply chain disruptions, inflation, and fluctuating geopolitical landscapes pose ongoing threats to profitability and stability. The increasing complexity of regulations and compliance requirements also add to operational challenges. Furthermore, intensifying competition necessitates continuous innovation in business models, customer service, and technology adoption to maintain a competitive edge. Successfully navigating these challenges will depend on retailers’ ability to embrace digital transformation, optimize their operations for efficiency, and prioritize sustainable practices to meet evolving consumer demands. The forecast period of 2025-2033 presents a dynamic landscape where adaptability and strategic foresight will be critical for success within this ever-evolving sector. This report provides a detailed analysis of the global retail industry, encompassing historical data (2019-2024), the current market landscape (Base Year 2025), and future projections (Forecast Period: 2025-2033). With a focus on key players like Walmart, Amazon, and Alibaba, this in-depth study explores market trends, segment performance, and growth drivers, offering valuable insights for investors, businesses, and industry professionals. The report covers a market valued in the hundreds of billions, if not trillions of dollars, and utilizes a multi-faceted approach to understanding the evolving retail landscape. Recent developments include: October 2023: Amazon announced that it provides online shopping services in South Africa to assist independent retailers in starting, expanding, and growing their enterprises.August 2023: Italian luxury fashion brand Gucci and Chinese e-commerce giant JD.com, popularly known as Jingdong, have partnered digitally. With the launch of a new digital flagship shop on the e-commerce retailer's platform, the partnership will reach a significant milestone.May 2023: Walmart announced the launch of over 28 healthcare facilities in its Walmart Supercenters, providing value-based and dental care services, among others.. Key drivers for this market are: Rapid Expansion of Urban Areas, Rise of E-commerce and Omnichannel Retailing. Potential restraints include: Rapid Expansion of Urban Areas, Rise of E-commerce and Omnichannel Retailing. Notable trends are: E-commerce is the Fastest-growing Segment in the Retail Industry.

Key Statistics on Business Performance and Operating Characteristics of the Import/Export, Wholesale and Retail Trades Sector - Table 630-76001 : Principal Statistics for All Establishments by Industry Grouping (Import/Export, Wholesale and Retail Trades Sector)

Success.ai’s Retail Data for the Retail Sector in North America offers a comprehensive dataset designed to connect businesses with key players across the diverse retail industry. Covering everything from department stores and supermarkets to specialty shops and e-commerce platforms, this dataset provides verified contact details, business locations, and leadership profiles for retail companies in the United States, Canada, and Mexico.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures your outreach, marketing, and business development efforts are powered by accurate, continuously updated, and AI-validated data.

Backed by our Best Price Guarantee, this solution empowers businesses to thrive in North America’s competitive retail landscape.

Why Choose Success.ai’s Retail Data for North America?

1. Verified Contact Data for Precision Outreach

   • Access verified phone numbers, work emails, and LinkedIn profiles of retail executives, store managers, and decision-makers.
   • AI-driven validation ensures 99% accuracy, enabling confident communication and efficient campaign execution.

2. Comprehensive Coverage Across Retail Segments

   • Includes profiles of retail businesses across major markets, from large department stores and grocery chains to boutique retailers and online platforms.
   • Gain insights into the operational dynamics of retail hubs in cities such as New York, Los Angeles, Toronto, and Mexico City.

3. Continuously Updated Datasets

   • Real-time updates reflect leadership changes, new store openings, market expansions, and shifts in consumer preferences.
   • Stay aligned with evolving industry trends and emerging opportunities in the North American retail sector.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other privacy regulations, ensuring responsible and lawful use of data in your campaigns.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with executives, marketing directors, and operations managers across the North American retail sector.
• 30M Company Profiles: Access firmographic data, including revenue ranges, store counts, and geographic footprints.
• Store Location Data: Pinpoint retail outlets, regional offices, and distribution centers to refine supply chain and marketing strategies.
• Leadership Contact Details: Connect with CEOs, CMOs, and procurement officers influencing retail operations and vendor selections.

Key Features of the Dataset:

1. Retail Decision-Maker Profiles

   • Identify and engage with store owners, category managers, and marketing directors shaping customer experiences and product strategies.
   • Target professionals responsible for inventory planning, vendor contracts, and store performance.

2. Advanced Filters for Precision Targeting

   • Filter companies by industry segment (luxury, grocery, e-commerce), geographic location, company size, or revenue range.
   • Tailor outreach to align with regional market trends, customer demographics, and operational priorities.

3. Market Trends and Operational Insights

   • Analyze trends such as online shopping growth, sustainability practices, and supply chain optimization.
   • Leverage insights to refine product offerings, identify partnership opportunities, and design effective campaigns.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and enhance engagement outcomes.

Strategic Use Cases:

1. Sales and Lead Generation

   • Present products, services, or technology solutions to retail procurement teams, marketing departments, and operations managers.
   • Build relationships with retailers seeking innovative tools, efficient supply chain solutions, or unique product offerings.

2. Market Research and Consumer Insights

   • Analyze retail trends, customer behaviors, and seasonal demands to inform marketing strategies and product launches.
   • Benchmark against competitors to identify gaps, emerging niches, and growth opportunities.

3. E-Commerce and Digital Strategy Development

   • Target e-commerce managers and digital transformation teams driving online retail initiatives and omnichannel integration.
   • Offer solutions to enhance online shopping experiences, logistics, and customer loyalty programs.

4. Recruitment and Workforce Solutions

   • Engage HR professionals and hiring managers in recruiting talent for store operations, customer service, or marketing roles.
   • Provide workforce optimization tools, training platforms, or staffing services tailored to retail environments.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality retail data at competitive prices, ensuring strong ROI for your marketing and outreach efforts in North America.

2. Seamless Integration
   ...

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.