In 2024, Walmart registered global net sales of about *** billion U.S. dollars, making it the leading U.S. retailer in terms of worldwide retail sales. Amazon was the closest challenger to Walmart's figures, with retail sales amounting to approximately *** billion U.S. dollars that year.

By 2027, the e-commerce retailer Amazon is forecast to be the leading retailer worldwide, just barely outdoing the Alibaba Group in terms of sales. Specifically, projections for 2027 show that the total chain retail sales of Amazon are going to reach a value of more than **** trillion U.S. dollars. Walmart would rank fifth, generating an estimated *** billion U.S. dollars in chain retail sales that year.

This ranking depicts the leading 100 American retailers in 2024, based on U.S. retail sales. Once again, Walmart was the leading American retailer. That year, the company accounted for retail sales of about *** billion U.S. dollars. Walmart was founded by Samuel Moore Walton (1918-1992) in 1962. Walmart's headquarters is situated in Bentonville, Arkansas and operates worldwide under different names (such as Walmex in Mexico and Best Price in India). Leading retailers in the United States Although Walmart is the unequivocal front-running retailer in terms of U.S. retail sales, Amazon.com inches closer each and every year. The retail industry is in the midst of a customer revolution. The collision of the virtual and physical worlds is fundamentally changing consumers' purchasing behaviors. Consumers are seeking an integrated shopping experience across all channels and expect retailers to deliver this experience. The key drivers of this customer revolution are the rapid adoption of mobile devices, digital media and tablets equipped with shopping apps. In fact, the share of smartphone users in the United States was around ** percent. In other words, nearly the entire country's population now uses smartphones. As such, the retail paradigm has shifted from a physical connection point with customers to a multi-pronged approach that crosses both physical and digital channels. The traditional brick-and-mortar retail store is no longer the dominant medium for purchasing all types of goods. Instead, it serves as one of many potential connection points between customers and a retailer's brand. Today's consumer is increasingly connected to both the physical and digital space and able to interact with retailers through multiple channels simultaneously. To stay competitive in this ever-evolving landscape, it is imperative for retailers to deliver a seamless customer experience across all channels and provide the right services and products at the right time.

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.

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

Context

This dataset contains lot of historical sales data. It was extracted from a Brazilian top retailer and has many SKUs and many stores. The data was transformed to protect the identity of the retailer.

Content

[TBD]

Acknowledgements

This data would not be available without the full collaboration from our customers who understand that sharing their core and strategical information has more advantages than possible hazards. They also support our continuos development of innovative ML systems across their value chain.

Inspiration

Every retail business in the world faces a fundamental question: how much inventory should I carry? In one hand to mush inventory means working capital costs, operational costs and a complex operation. On the other hand lack of inventory leads to lost sales, unhappy customers and a damaged brand.

Current inventory management models have many solutions to place the correct order, but they are all based in a single unknown factor: the demand for the next periods.

This is why short-term forecasting is so important in retail and consumer goods industry.

We encourage you to seek for the best demand forecasting model for the next 2-3 weeks. This valuable insight can help many supply chain practitioners to correctly manage their inventory levels.

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We will study the sales data of one of the largest retailers in the world. Let's figure out what factors influence its revenue. Can factors such as air temperature and fuel cost influence the success of a huge company along with the purchasing power index and seasonal discounts? And how does machine learning minimize costs and increase economic impact?

The data contains the following columns:

• Store: Store number
• Date: Sales week start date
• Weekly_Sales: Sales
• Holiday_Flag: Mark on the presence or absence of a holiday
• Temperature: Air temperature in the region
• Fuel_Price: Fuel cost in the region
• CPI: Consumer price index
• Unemployment: Unemployment rate _

In 2024, ** percent of Walmart's retail sales came from U.S. operations. In comparison, all of The Kroger Company's retail sales were generated within the United States. Walmart was the leading retailer based on U.S. retail sales.

Columns: • Order Date: Consists the date on which a specific order was created. • Customer Name: Consists the full name of the customer who created an order. • State: Name of the state the customer belonged to. • Category: Which category did the ordered product belonged to. • Sub – Category: Name of the sub – category of the ordered product. • Product Name: Name of the ordered product. • Sales: The price at which the product was sold. • Quantity: Number of quantities the customer ordered of that product. • Profit: How much did the firm made in that transaction.

Graph and download economic data for Advance Retail Sales: Nonstore Retailers (RSNSR) from Jan 1992 to Sep 2025 about retail trade, sales, retail, and USA.

Small specialty retail stores are influenced by broad macroeconomic variables rather than product-specific trends. Still, individual segments do respond to specific shifts in consumer preferences. In recent years, rising per capita disposable income has sustained demand throughout the retail sector. A recovery from the pandemic boosted consumer spending and encouraged consumers to return to brick-and-mortar stores. Specialty retailers were relatively unaffected by pandemic declines as high-income consumers and tobacco users, two significant markets for the industry, continued to spend. Competition from online and big-box retailers has risen, putting downward pressure on profit. More stores are expanding their online platforms to boost consumer reach and provide additional revenue streams. Rising operational costs have contributed to a slight dip in profit. Revenue for small specialty retailers is expected to swell at a CAGR of 4.0% to $68.4 billion through the end of 2025, including a hike of 2.0% in 2025 alone. Despite intensifying competition from discount department stores and online retailers, specialty retail stores have relied on serving a particular niche to remain successful. Big-box stores offer a one-stop shopping experience with lower prices for similar products. External competition has driven underperforming retailers to exit the industry, leaving nonemployers and small retail stores with low barriers to entry. Still, revenue gains have prompted the emergence of many new specialty retailers seeking to capitalize on the trend of shopping locally and broader sustainability trends. Small retailers have maintained a strong customer base by offering a unique in-store experience and high-quality products. Moving forward, small specialty retailers will continue expanding, albeit slower than in the previous five-year period. A gain in consumer spending and consumer confidence compounded by growing environmental awareness will support specialty retail store sales. Ongoing competition from large-scale retailers and declining smoking rates will mitigate specialty retailers' expansion. More consumers view consumer products, particularly luxury and nostalgic items, as sound investment options. Stores can benefit from this trend by stocking high-end goods that appeal to these consumers, focusing on popular brands. Revenue is expected to expand at a CAGR of 1.4% to $73.3 billion through the end of 2030.

Data Set Description This dataset simulates a retail environment with a million rows and 100+ columns, covering customer information, transactional data, product details, promotional information, and customer behavior metrics. It includes data for predicting total sales (regression) and customer churn (classification).

Detailed Column Descriptions Customer Information:

customer_id: Unique identifier for each customer. age: Age of the customer. gender: Gender of the customer (e.g., Male, Female, Other). income_bracket: Income bracket of the customer (e.g., Low, Medium, High). loyalty_program: Whether the customer is part of a loyalty program (Yes/No). membership_years: Number of years the customer has been a member. churned: Whether the customer has churned (Yes/No) - Target for classification. marital_status: Marital status of the customer. number_of_children: Number of children the customer has. education_level: Education level of the customer (e.g., High School, Bachelor's, Master's). occupation: Occupation of the customer. Transactional Data:

transaction_id: Unique identifier for each transaction. transaction_date: Date of the transaction. product_id: Unique identifier for each product. product_category: Category of the product (e.g., Electronics, Clothing, Groceries). quantity: Quantity of the product purchased. unit_price: Price per unit of the product. discount_applied: Discount applied on the transaction. payment_method: Payment method used (e.g., Credit Card, Debit Card, Cash). store_location: Location of the store where the purchase was made. Customer Behavior Metrics:

avg_purchase_value: Average value of purchases made by the customer. purchase_frequency: Frequency of purchases (e.g., Daily, Weekly, Monthly, Yearly). last_purchase_date: Date of the last purchase made by the customer. avg_discount_used: Average discount percentage used by the customer. preferred_store: Store location most frequently visited by the customer. online_purchases: Number of online purchases made by the customer. in_store_purchases: Number of in-store purchases made by the customer. avg_items_per_transaction: Average number of items per transaction. avg_transaction_value: Average value per transaction. total_returned_items: Total number of items returned by the customer. total_returned_value: Total value of returned items. Sales Data:

total_sales: Total sales amount for each customer over the last year - Target for regression. total_transactions: Total number of transactions made by each customer. total_items_purchased: Total number of items purchased by each customer. total_discounts_received: Total discounts received by each customer. avg_spent_per_category: Average amount spent per product category. max_single_purchase_value: Maximum value of a single purchase. min_single_purchase_value: Minimum value of a single purchase. Product Information:

product_name: Name of the product. product_brand: Brand of the product. product_rating: Customer rating of the product. product_review_count: Number of reviews for the product. product_stock: Stock availability of the product. product_return_rate: Rate at which the product is returned. product_size: Size of the product (if applicable). product_weight: Weight of the product (if applicable). product_color: Color of the product (if applicable). product_material: Material of the product (if applicable). product_manufacture_date: Manufacture date of the product. product_expiry_date: Expiry date of the product (if applicable). product_shelf_life: Shelf life of the product (if applicable). Promotional Data:

promotion_id: Unique identifier for each promotion. promotion_type: Type of promotion (e.g., Buy One Get One Free, 20% Off). promotion_start_date: Start date of the promotion. promotion_end_date: End date of the promotion. promotion_effectiveness: Effectiveness of the promotion (e.g., High, Medium, Low). promotion_channel: Channel through which the promotion was advertised (e.g., Online, In-store, Social Media). promotion_target_audience: Target audience for the promotion (e.g., New Customers, Returning Customers). Geographical Data:

customer_zip_code: Zip code of the customer's residence. customer_city: City of the customer's residence. customer_state: State of the customer's residence. store_zip_code: Zip code of the store. store_city: City where the store is located. store_state: State where the store is located. distance_to_store: Distance from the customer's residence to the store. Seasonal and Temporal Data:

holiday_season: Whether the transaction occurred during a holiday season (Yes/No). season: Season of the year (e.g., Winter, Spring, Summer, Fall). weekend: Whether the transaction occurred on a weekend (Yes/No). Customer Interaction Data:

customer_support_calls: Number of calls made to customer support. email_subscription...

Korea Retail Sales: Major Department Stores: Purchase Amount per Customer data was reported at 5.800 % in Sep 2018. This records an increase from the previous number of 1.600 % for Aug 2018. Korea Retail Sales: Major Department Stores: Purchase Amount per Customer data is updated monthly, averaging 0.900 % from Feb 2003 (Median) to Sep 2018, with 186 observations. The data reached an all-time high of 15.300 % in Jan 2006 and a record low of -8.900 % in Aug 2015. Korea Retail Sales: Major Department Stores: Purchase Amount per Customer data remains active status in CEIC and is reported by Ministry of Trade, Industry and Energy. The data is categorized under Global Database’s Korea – Table KR.H019: Retail Sales: YoY%.

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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.

Korea Retail Sales: Major Department Stores: Men's Apparel data was reported at 10.100 % in Sep 2018. This records an increase from the previous number of 1.200 % for Aug 2018. Korea Retail Sales: Major Department Stores: Men's Apparel data is updated monthly, averaging -0.400 % from Feb 2003 (Median) to Sep 2018, with 188 observations. The data reached an all-time high of 19.900 % in Dec 2005 and a record low of -22.300 % in Mar 2004. Korea Retail Sales: Major Department Stores: Men's Apparel data remains active status in CEIC and is reported by Ministry of Trade, Industry and Energy. The data is categorized under Global Database’s South Korea – Table KR.H019: Retail Sales: YoY%.

The dataset is about a retail sales dataset containing information about store sales for various products over time.

The specific variables include: Store: Unique identifier for the store location Date: Calendar date of the sales data Product: Name of the product being sold Weekly Sales: Total number of units sold for the product in a week Inventory Level: Number of units of the product currently in stock at the store Temperature: Average temperature for the week at the store location Past Promotion of Product (in lac): Total value (in lakhs) of any past promotions for the product during the week (1 lac = 100,000) Demand Forecast: Predicted number of units to be sold for the product in the next week (provided for baseline model comparison)

This dataset can be used for various analytical purposes related to retail sales and inventory management, including:

Demand forecasting: By analyzing historical sales data, temperature, past promotions, and other relevant factors, you can build models to predict future demand for products. This information can be used to optimize inventory levels and prevent stock outs or overstocking. Promotion analysis: You can compare sales data during promotional periods with non-promotional periods to assess the effectiveness of different promotions and identify products that respond well to promotions. Product analysis: By analyzing sales data across different stores and time periods, you can identify which products are most popular and in which locations. This information can be used to inform product placement, marketing strategies, and assortment planning. Store performance analysis: You can compare sales performance across different stores to identify top-performing stores and understand factors contributing to their success. This information can be used to identify areas for improvement in underperforming stores.

By utilizing this dataset for these analytical purposes, retail organizations can gain valuable insights into their sales patterns, customer behavior, and inventory management practices. This information can be used to make data-driven decisions that improve sales performance, profitability, and customer satisfaction.

Overview with Chart & Report: Large Retailers Sales y/y reflect changes in the total value of goods sold in large stores, supermarkets and hypermarkets in Japan, in the given month compared to the same month of the previous

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

Dataset Description

This dataset contains sales information from four stores of one of the retailers over 25 months. Participants are expected to use these files to develop models that can predict customer demand. Additionally, the dataset includes a holdout sample with sales data for a 1-month period for which forecasts should be provided.

sales.csv - Purpose: This file contains aggregated store sales for specific dates. - Columns: - date: Sales date - item_id: A unique identifier for each product - quantity: Total quantity of product sold per day - price_base: Average sales price per day - sum_total: Total daily sales amount - store_id: Store number

online.csv - Purpose: This file contains aggregated online sales by store for specific dates. - Columns: - date: Sales date - item_id: A unique identifier for each product - quantity: Total quantity of product sold per day (online) - price_base: Average sales price per day - sum_total: Total daily sales amount - store_id: Store number

markdowns.csv - Purpose: This file provides data on products sold at markdown prices in each store. - Columns: - date: Date of markdown - item_id: A unique identifier for each product - normal_price: Regular price - price: Price during markdown - quantity: Quantity sold at markdown - store_id: Store number

price_history.csv - Purpose: This file contains price changes data in each store. - Columns: - date: Date of price change - item_id: A unique identifier for each product - price: Item new price - code: Price change code - store_id: Store number

discounts_history.csv - Purpose: Contains historical promo data for each specific store. - Columns: - date: Date - item_id: A unique identifier for each product - sale_price_before_promo: Price before promo period started - sale_price_time_promo: Price during the promo period - promo_type_code: Promo code type - doc_id: Promo document number - number_disc_day: Sequential day number of the current promo period - store_id: Store number

actual_matrix.csv - Purpose: Contains the list of products available in stores. - Columns: - item_id: A unique identifier for each product - date: Date of last product appearance in the current matrix - store_id: Store number

catalog.csv - Purpose: Product catalog with characteristics. - Columns: - item_id: A unique identifier for each product - dept_name: Product department (hierarchy level) - class_name: Product class (hierarchy level) - subclass_name: Product subclass (hierarchy level) - item_type: Product type - weight_volume: Volumetric weight - weight_netto: Net weight - fatness: Fat content

stores.csv - Purpose: Contains stores info data. - Columns: - store_id: Store number - division: Store division - format: Store format - city: Location - area: Store sales area

Retail Sales in the United States increased 4.30 percent in September of 2025 over the same month in the previous year. This dataset provides - United States Retail Sales YoY - actual values, historical data, forecast, chart, statistics, economic calendar and news.