The growth of supermarkets in most populated cities are increasing and market competitions are also high. The dataset is one of the historical sales of supermarket company which has recorded in 3 different branches for 3 months data. Predictive data analytics methods are easy to apply with this dataset.

A structured dataset of over three months of sales transactions from three supermarket branches. It includes attributes such as invoice ID, branch, city, customer type, gender, product line, unit price, quantity, total, tax, payment method, and gross income. Designed for predictive analytics, sales forecasting, and customer behavior analysis.

In 2024, U.S. supermarket and other grocery store sales amounted to about ***** billion U.S. dollars. This is more than double the sales amounts that were generated back in 1992. Supermarkets and grocery stores in the U.S. WalMart stores dominated sales of the leading grocery retailers in 2023, generating close to *** billion U.S. dollars. Kroger and Ahold Delhaize each reached sales numbers of about *** and ** billion U.S. dollars, respectively, within the same period. In 2018, there were over 38,000 supermarket stores in the United States. Over ** percent of this total were supermarket chains. Supermarket formats In the United States, there are various types of supermarkets consumers can visit, such as natural/gourmet food supermarkets, warehouse grocery stores, and military commissaries. The most common type of supermarket in the country is one of the conventional kind. In 2018, there were just over ****** conventional supermarkets in the United States. The second most common type is the supercenter for groceries and mass merchandise, of which there were about ***** that year.

Dataset

This dataset was created by Mark Medhat

Released under Apache 2.0

Contents

Graph and download economic data for Retail Sales: Grocery Stores (MRTSMPCSM4451USN) from Feb 1992 to Jul 2025 about groceries, retail trade, sales, retail, and USA.

This statistic shows the supermarket sales in the United States in 2018, by product category. In that year, about ***** billion U.S. dollars were generated by non-food grocery sales in U.S. supermarkets.

This dataset provides a detailed information of a superstore’s operations, encompassing transactional data, customer segmentation, and return trends. It has been structured into three sheets—Orders, People, and Returns—offering a holistic view of retail dynamics. The inspiration behind this dataset lies in its potential to unlock actionable business insights, from sales analysis and customer behavior trends to return rate predictions and product performance evaluation. Designed to spark creativity and enable practical applications, this dataset is perfect for analysts, students, and data enthusiasts looking to dive into retail analytics and beyond.

This statistic shows supermarket sales in the United States in 2020, by department. From January 1 to July 12, 2020 edibles grocery sales in supermarkets totaled ***** billion U.S. dollars.

For the 52 weeks ended on February 21, 2021, produce accounted for approximately ** percent of the U.S. supermarket sales of the perimeter food categories. Total category sales amounted to approximately ***** billion U.S. dollars that period.

Grocery Sales Database - Data Card

Overview

The Grocery Sales Database is a structured relational dataset designed for analyzing sales transactions, customer demographics, product details, employee records, and geographical information across multiple cities and countries. This dataset is ideal for data analysts, data scientists, and machine learning practitioners looking to explore sales trends, customer behaviors, and business insights.

Database Schema

The dataset consists of seven interconnected tables:

File Name	Description
categories.csv	Defines the categories of the products.
cities.csv	Contains city-level geographic data.
countries.csv	Stores country-related metadata.
customers.csv	Contains information about the customers who make purchases.
employees.csv	Stores details of employees handling sales transactions.
products.csv	Stores details about the products being sold.
sales.csv	Contains transactional data for each sale.

Table Descriptions

1. categories

Key	Column Name	Data Type	Description
PK	CategoryID	INT	Unique identifier for each product category.
	CategoryName	VARCHAR(45)	Name of the product category.

2. cities

Key	Column Name	Data Type	Description
PK	CityID	INT	Unique identifier for each city.
	CityName	VARCHAR(45)	Name of the city.
	Zipcode	DECIMAL(5,0)	Population of the city.
FK	CountryID	INT	Reference to the corresponding country.

3. countries

Key	Column Name	Data Type	Description
PK	CountryID	INT	Unique identifier for each country.
	CountryName	VARCHAR(45)	Name of the country.
	CountryCode	VARCHAR(2)	Two-letter country code.

4. customers

Key	Column Name	Data Type	Description
PK	CustomerID	INT	Unique identifier for each customer.
	FirstName	VARCHAR(45)	First name of the customer.
	MiddleInitial	VARCHAR(1)	Middle initial of the customer.
	LastName	VARCHAR(45)	Last name of the customer.
FK	cityID	INT	City of the customer.
	Address	VARCHAR(90)	Residential address of the customer.

5. employees

Key	Column Name	Data Type	Description
PK	EmployeeID	INT	Unique identifier for each employee.
	FirstName	VARCHAR(45)	First name of the employee.
	MiddleInitial	VARCHAR(1)	Middle initial of the employee.
	LastName	VARCHAR(45)	Last name of the employee.
	BirthDate	DATE	Date of birth of the employee.
	Gender	VARCHAR(10)	Gender of the employee.
FK	CityID	INT	unique identifier for city
	HireDate	DATE	Date when the employee was hired.

6. products

Key	Column Name	Data Type	Description
PK	ProductID	INT	Unique identifier for each product.
	ProductName	VARCHAR(45)	Name of the product.
	Price	DECIMAL(4,0)	Price per unit of the product.
	CategoryID	INT	unique category identifier
	Class ...

Retail Sales - Table 620-67011 : Value of Retail Sales in Supermarkets by Broad Product Category

Monthly sales of supermarkets and other grocery (except convenience) stores in Canada hit a high of **** billion Canadian dollars in March 2020, an increase of over two billion compared to the previous month. The spread of coronavirus in early March 2020 and consequent panic buying can explain this jump in sales. In 2025, grocery store sales exceeded **** billion Canadian dollars every month.

Dataset

This dataset was created by Sriram

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Sabhireddy

Contents

Dataset

This dataset was created by Raymond Eli

Contents

Chongqing: Chain: Supermarket: Sales data was reported at 4.885 RMB bn in 2019. This records an increase from the previous number of 4.621 RMB bn for 2018. Chongqing: Chain: Supermarket: Sales data is updated yearly, averaging 4.885 RMB bn from Dec 2005 (Median) to 2019, with 15 observations. The data reached an all-time high of 11.511 RMB bn in 2011 and a record low of 1.113 RMB bn in 2005. Chongqing: Chain: Supermarket: Sales data remains active status in CEIC and is reported by Ministry of Commerce, China General Chamber of Commerce. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.CRAD: Supermarket: Chongqing.

Supermarkets and grocery stores have significantly transformed in recent years, driven by technological advancements and shifting consumer preferences. E-commerce has become a cornerstone of the industry, with over 70.0% of grocery retailers integrating online ordering and fulfillment into their operations in 2025. This shift has been fueled by consumer demand for convenience and efficient shopping experiences, prompting retailers to invest heavily in curbside pickup and home delivery services. Major players like Kroger have leveraged these innovations to maintain a competitive edge, while third-party delivery platforms like Instacart have enabled smaller grocers to compete with larger chains. The adoption of "dark stores" and AI-driven technologies has further optimized operations but heightened competition has limited revenue expansion. Over the past five years, revenue has been slipping at a CAGR of 0.1%, reversing course in 2025 to climb 1.1%, reaching $883.1 million. Over the past five years, the industry has faced rising labor costs and competition from discount grocers and private-label products. Automation has played a crucial role in managing these pressures, with more than 50.0% of transactions in major chains processed through self-checkout systems in 2025. Despite these advancements, wages have continued to rise, accounting for an estimated 10.7% of revenue. This has led retailers to focus on strategic pricing and the promotion of high-margin private-label products to sustain profit. The proliferation of discount grocers like Aldi and Lidl has intensified competition, forcing traditional supermarkets to innovate and adapt to retain market share. Looking ahead, supermarkets and grocery stores are likely to endure steady but marginal revenue growth over the next five years, influenced by economic and demographic factors. Increases in per capita disposable income and consumer spending suggest a stable economic environment that could bolster sales of premium and specialty grocery items. However, declines in the agricultural price index may pressure revenue growth, as lower prices could reduce sales value. Urban population growth will continue to drive demand for grocery products, encouraging retailers to adopt urban-centric strategies. Upcoming FDA regulations on product labeling and ongoing geopolitical tensions will present challenges and opportunities for the industry. Retailers that can navigate these complexities and align with evolving consumer preferences, such as the rise of functional foods and the "quiet luxury" trend, will be well-positioned to thrive in a rapidly changing market landscape. Revenue is anticipated to expand marginally over the next five years at a CAGR of less than 0.1%, totaling $883.3 million in 2030.

Chile Supermarket Sales: Metropolitan Santiago data was reported at 372,564.577 CLP mn in Apr 2019. This records a decrease from the previous number of 410,215.729 CLP mn for Mar 2019. Chile Supermarket Sales: Metropolitan Santiago data is updated monthly, averaging 344,268.949 CLP mn from Jan 2014 (Median) to Apr 2019, with 64 observations. The data reached an all-time high of 472,334.618 CLP mn in Dec 2018 and a record low of 244,037.946 CLP mn in Feb 2014. Chile Supermarket Sales: Metropolitan Santiago data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Chile – Table CL.H011: Supermarket Sales.

This graph presents supermarket sales in the United States from 2013 to 2018, by format. In 2018, U.S. supermarkets offering natural and/or gourmet foods generated about ***** billion U.S. dollars in sales. Total U.S. supermarket sales amounted to about ***** billion U.S. dollars that year.

Japan Supermarket Sales: Food: Fresh data was reported at 309,505,780.000 JPY th in Sep 2018. This records a decrease from the previous number of 316,878,840.000 JPY th for Aug 2018. Japan Supermarket Sales: Food: Fresh data is updated monthly, averaging 279,748,080.000 JPY th from Apr 2010 (Median) to Sep 2018, with 102 observations. The data reached an all-time high of 365,965,870.000 JPY th in Dec 2017 and a record low of 238,319,170.000 JPY th in Apr 2010. Japan Supermarket Sales: Food: Fresh data remains active status in CEIC and is reported by Japan Supermarket Association. The data is categorized under Global Database’s Japan – Table JP.H013: Supermarket: Sales.