Smart grocery shopping suggestions by AI are likely to be used by 35.2 percent of survey respondents from the U.S., according to their own estimations. Another 23.3 percent of respondents said they are very likely to take such suggestions by AI.

The primary grocery shopper in U.S. households made an average of *** shopping trips per week in 2025. The importance of fresh products in consumers’ diets, along with buying habits, keep shoppers returning to stores to restock their shelves and fridges. Despite the increasing competition from online grocery retailers, grocery shopping in a physical store is still important to Americans. Online influences Within the U.S., consumers have a number of choices when it comes to how they choose to do their weekly or bi-weekly food shop. Whilst most Americans still choose to visit brick-and-mortar stores, online grocery shopping is becoming undoubtedly more popular. To keep up with such trends, some grocery stores offer click and collect or grocery delivery service. This reduces the number of in-store visits consumers make when buying groceries. Generational preferences Online shopping comes as second nature to younger consumers. Therefore, it is no surprise that Millennials account for the highest spending share of digital grocery orders and delivery services in the United States.. Generation X are not far behind Millennials when it comes to share of spending. This is an important demographic for online grocery retailers to target, as Generation X are the generation spending the most on average on groceries per month.

Graph and download economic data for Retail Sales: Supermarkets and Other Grocery (Except Convenience) Stores (MRTSSM44511USN) from Jan 2001 to Jul 2025 about groceries, retail trade, sales, retail, and USA.

According to a survey carried out in March 2025 in the United States, some ** percent of respondents were shopping for groceries once a week. Another ** percent were doing so two to three times a week, while only **** percent shopped for groceries more often than that.

Data Driven Detroit created the data by selecting locations from NETS and ESRI business data with proper NAICS codes, then adding and deleting though local knowledge and confirmation with Google Streetview. These locations are Grocery stores which primarily sell food and don't include convenience stores. Visual confirmation cues included the existence of the word "grocery" in the name, or the presence of shopping carts.

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.

Market Size statistics on the Supermarkets & Grocery Stores industry in the US

Context

A supermarket is a self-service shop offering a wide variety of food, beverages and household products, organized into sections. This kind of store is larger and has a wider selection than earlier grocery stores, but is smaller and more limited in the range of merchandise than a hypermarket or big-box market. In everyday U.S. usage, however, "grocery store" is synonymous with supermarket, and is not used to refer to other types of stores that sell groceries.

Content

In the dataset, You'll get data of different stores of a supermarket company as per their store IDs which for ease has been converted to positive integers.

Store ID: (Index) ID of the particular store.

Store_Area: Physical Area of the store in yard square.

Items_Available: Number of different items available in the corresponding store.

Daily_Customer_Count: Number of customers who visited to stores on an average over month.

Store_Sales: Sales in (US $) that stores made.

Acknowledgement

The data is obtained from the project from the University after seeking proper permission.

Inspiration

Analyzing the performances of stores in the past on basis of which will try to rectify defects as well as to leverage the positives. Who doesn't want to increase their profits right?

A listing of all retail food stores which are licensed by the Department of Agriculture and Markets.

In a 2023 survey of U.S. shoppers, 68 percent of respondents who identified themselves as primary shoppers in their household were women, while 56 percent were men. For those who identified themselves as self shoppers, that is, people who live alone and are responsible for their own spending, 15 percent were men and 13 percent were women.

View monthly updates and historical trends for US Grocery Store Sales. from United States. Source: Census Bureau. Track economic data with YCharts analyti…

Introduction Unlock powerful insights with the Target Groceries Data dataset, featuring an extensive collection of over thousands of grocery products sold at Target stores across the USA. This dataset, available in CSV format, is ideal for analysts, researchers, and marketers who want to explore product details, pricing trends, and consumer preferences in one of the biggest retail chains in the United States.

With detailed information on groceries from various categories, including fresh produce, packaged foods, snacks, beverages, and more, this dataset empowers you to perform in-depth market analysis, track product performance, and understand consumer shopping behaviors.

Key Features of the Target Groceries Data Dataset

The Target Groceries Data dataset provides a wide range of attributes that allow you to analyze grocery products across multiple dimensions. Key data points include:

• Title: Name of the grocery product.
• URL: Direct link to the product page on the Target website.
• Brand: The brand associated with the product.
• Main Image: Primary image of the product for easy visual identification.
• SKU: Unique product identifier for inventory management.
• Description: A detailed description of the product, including key features and uses.
• Raw Description: The unprocessed, full product description as listed on the Target website.
• GTIN13: Global Trade Item Number (GTIN13), a unique identifier for the product.
• Currency: Currency used for the product price (usually USD for the USA).
• Price: The listed price of the product.
• Availability: Whether the product is in stock or out of stock.
• Available Delivery Method: Options available for delivery (e.g., standard shipping, same-day delivery).
• Available Branch: Locations where the product is available for pickup.
• Primary Category: The main category the product falls under (e.g., Grocery, Household).
• Sub Category 1, 2, 3: Additional category breakdowns for better classification (e.g., Snacks, Beverages, Organic).
• Images: Additional product images.
• Raw Specifications: The full list of product specifications in raw text format.
• Specifications: Structured version of the product specifications for easy analysis.
• Highlights: Key selling points or product features.
• Raw Highlights: Raw text from product highlights.
• Uniq ID: Unique identifier for each product record in the dataset.
• Scraped At: Timestamp for when the data was scraped, ensuring data freshness.

Why Use the Target Groceries Data?

1. Comprehensive Market Research: Dive deep into the grocery market by analyzing product prices, availability, and consumer favorites across multiple categories.
2. Pricing Strategy Insights: Leverage data on prices, discounts, and availability to assess competitive pricing strategies and optimize your own.
3. Consumer Behavior Analysis: With product descriptions, highlights, and specifications, understand what influences consumer purchasing decisions.
4. Product Performance Tracking: Analyze how products in various categories are performing, identify popular brands, and track trends over time using scraped_at timestamps.
5. Optimized Inventory Management: Utilize SKU, availability, and delivery method data for efficient inventory tracking and management.

How to Access the Dataset The Target Groceries Data is available in CSV format, making it easy to import into any data analysis tool for in-depth analysis. Whether you're a business strategist, data scientist, or researcher, this dataset offers everything you need to gain valuable insights into the grocery retail market.

Conclusion Enhance your analysis of the grocery sector with the Target Groceries Data. With detailed attributes such as product titles, pricing, availability, and specifications, this dataset provides everything you need to make informed, data-driven decisions in the competitive grocery market.

Locations of Grocery Stores, which are deemed essential following hurricanes or other disaster scenarios.This dataset is fed from the revenue department with weekly updates.

This dataset contains measures of the number and density of grocery stores – including supermarkets, specialty food stores, and warehouse clubs – per United States census tract from 2003 through 2017. These types of businesses represent places where neighborhood residents can obtain fresh and healthy foods.

Graph and download economic data for Producer Price Index by Industry: Supermarkets and Other Grocery Stores: Supermarket and Other Grocery Store Services (PCU4451104451103) from Dec 1999 to Sep 2025 about groceries, services, PPI, industry, inflation, price index, indexes, price, and USA.

These datasets provide a comprehensive and detailed view of the sales and financial performance of the grocery store, including information about sales by city, region, and customer, as well as overall sales, profits, and trends over time. This information can be used to make data-driven decisions about inventory management, crew staffing, and marketing strategy, in order to improve sales and profits. Credit to the original owner of this dataset

Contains a list of grocery stores which was used by the city to calculate the estimates of Chicagoans living in food deserts in 2013. Data in this file can be cross-referenced with the city's business license data (http://bit.ly/sMFZdN).

The dataset contains aggregated weekly sales and product-level information for 200 grocery products sold in 10 stores over a period of 52 weeks (a full year). The dataset captures product sales, promotions, store traffic, and other related metrics, along with information on the complementary, substitute, and unrelated product sales.

The dataset is structured in the following columns: 1) Product_ID: Description: Unique identifier for each product in the store's inventory. Type: Integer (ranging from 101 to 300 for a total of 200 products).

2) Product_Category: Description: The product's category in the store. Type: Categorical variable with categories like: Beverages, Dairy, Snacks, Vegetables, Packaged Food, Frozen Foods, Cereals, Breads, Confectionery, Household

3) Week: Description: The week number in the year, ranging from 1 to 52 (52 weeks in total for the year).

4) Promotion_Flag: Description: Indicates whether the product was on promotion for that week. 0: No promotion 1: Promotion (with price discount)

5) Price: Description: The regular price of the product (before any discount). Type: Float (between 1.5 and 6.5)

6) Promoted_Price: Description: The discounted price of the product when it's on promotion. Type: Float (lower than regular price if the product is promoted).

7) Total_Sales_Volume: Description: The total number of units of the product sold during the week. Type: Integer (ranging from 1,000 to 6,000 units per week).

8) Total_Sales_Revenue: Description: The total revenue generated from the sales of the product during the week. Type: Float (calculated as Total Sales Volume × Promoted Price).

9) Store_Traffic: Description: The total number of customers visiting the store during the given week. Type: Integer (between 12,000 and 17,000 visits per week). Note: This value is consistent across all products for the week, as store traffic is not product-specific but depends on the store's overall footfall.

10) Promoted_Complementary_Sales: Description: The sales volume of complementary products during the week for products on promotion. Type: Float (calculated based on the relationship between the promoted product and its complementary items). Note: Complementary items are products that are typically bought together with the promoted product.

11) Non_Promoted_Complementary_Sales: Description: The sales volume of complementary products during the week for products that are not on promotion. Type: Float

12) Promoted_Substitute_Sales: Description: The sales volume of substitute products during the week for products on promotion. Type: Float (substitute items are products that could serve as alternatives to the promoted product).

13) Non_Promoted_Substitute_Sales: Description: The sales volume of substitute products during the week for products that are not on promotion. Type: Float

14) Promoted_Unrelated_Sales: Description: The sales volume of unrelated products during the week for products on promotion. Type: Float (unrelated items are products that are not closely linked to the promoted product).

15) Non_Promoted_Unrelated_Sales: Description: The sales volume of unrelated products during the week for products that are not on promotion. Type: Float

16) Store_Profit: Description: The profit generated by the store during the week. Type: Integer (between 4,000 and 6,000 for each week, consistent across products in the same week).

17) Weekday_Indicator: Description: Indicates whether the sales occurred during a Weekday or Weekend (based on the week’s sequence of days). Type: Categorical, with two possible values: Weekday: Monday through Friday Weekend: Saturday and Sunday Note: The distribution of weekdays and weekends is consistent across all products in a given week, with 5 weekdays and 2 weekend days.

Key Insights & Assumptions: 1) Weekday/Weekend Traffic & Sales: Weekday sales are typically higher than weekend sales, though weekends tend to see a spike in store traffic due to increased customer visits. This is reflected in the Weekday_Indicator column.

2) Promotion Effects: Products on promotion tend to have a lower price and higher sales volume, contributing to changes in retailer profit and possibly store traffic. Sales of complementary products also tend to increase when the promoted product is in high demand, while substitute products often experience a decline during promotion periods.

3) Product Relationships: Complementary products generally see an increase in sales when the promoted product is sold. Conversely, substitute products might experience a dip in sales, especially when a cheaper version or the promoted product is available.

4) Store Traffic & Profit: The store traffic and store profit are constants for each week, but the product-specific sales (both promoted and non-promoted) affect retailer profits based on the prices, volumes, and promotions applied.

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Graph and download economic data for All Employees: Retail Trade: Grocery Stores in New Orleans-Metairie, LA (MSA) (SMU22353804244510001A) from 1990 to 2024 about groceries, New Orleans, LA, retail trade, sales, retail, employment, and USA.

According to a survey carried out between July 2024 and June 2025 in the United States, some ** percent of Generation Z respondents shopped at Walmart on a regular basis for groceries. Another ** percent did so at Target.