Retail sales data for Great Britain in value and volume terms, seasonally and non-seasonally adjusted.

As the new coronavirus strain Sars-Cov-2 (Covid-19) is spreading across the world at an alarming pace, the consumer market is seeing disruptions as manufacturing and production sectors slow down, particularly in countries where the disease hit the hardest. According to a study conducted with UK retailers in the food, fashion, and health and beauty categories, retailers are positive that the Covid-19 will have a negative impact on their sales. While ** percent thought the impact would be significant, a great share of respondents thought the outbreak would have a slightly negative impact on their sales, if the virus persists.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

Retail footfall is one of the casualties of the growing e-commerce industry and the toll on the UK high street is visible more and more frequently. As shown in this statistic, the declining visitor numbers to retail and shopping centers help understand the apprehension around traditional retail. Throughout 2018, footfall persistently decreased and the percentage change confimed this even during the holiday season.

Store closures in retail centers

One of the negative outcomes of the falling visitor numbers to retail centers is store closures. In 2017, high streets and shopping centers together were the leading locations which experienced a drop in store numbers across Great Britain.

Is there hope for offline retail?
In the past few years, growth in the retail industry happened more online than in traditional retail formats. Yet forecasts anticipate growth for offline retail shopping locations, whereas a standstill is in sight for online sales.

Due to the coronavirus (Covid-19) crisis and social distancing measures the UK government took, retail footfall data following March 2020 saw an unprecedented fall. In the United Kingdom (UK), visitor numbers to retail locations were generally in decline, but for retail parks the decline was less dramatic. Over the period displayed here, footfall has slightly recovered, with positive year-on-year change in shopper numbers across retail parks occuring in August, September, and October 2024. In the most recent period, footfall in retail parks also reflected a slight increase of *** percent compared to the previous year.

Reporting periods for the Retail Sales Index in Great Britain.

Descriptions and categories of the Internet Sales Index and their percentage of all retailing for Great Britain.

The extent to which individual businesses in Great Britain experienced actual changes in their sales.

Retail Sales in the United States increased 0.50 percent in July 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.

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!

This statistic displays the share of multichannel retailers that utilize geolocation functionality on mobile in the United Kingdom (UK) from 2016 to 2018. In 2018, 62 percent of UK retailers had geolocation functionality on their mobile websites. In 2016, less than half of the UK retailers geolocated their mobile users.

Retail sales Y-on-Y in France, July, 2025 The most recent value is 3.22 percent as of July 2025, a decline compared to the previous value of 3.83 percent. Historically, the average for France from January 2000 to July 2025 is 2.74 percent. The minimum of -30.91 percent was recorded in April 2020, while the maximum of 43.96 percent was reached in April 2021. | TheGlobalEconomy.com

Retail sales Y-on-Y in Vietnam, May, 2025 The most recent value is 6.98 percent as of May 2025, an increase compared to the previous value of 5.65 percent. Historically, the average for Vietnam from January 2005 to May 2025 is 8.02 percent. The minimum of -33.7 percent was recorded in August 2021, while the maximum of 50.2 percent was reached in August 2022. | TheGlobalEconomy.com

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

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The online retail market share in the US is expected to increase to USD 460.13 billion from 2021 to 2026, and the market’s growth momentum will accelerate at a CAGR of 11.64%.

The report extensively covers online retail market in the US segmentation by the following:

Product - Apparel, footwear, and accessories, consumer electronics and electricals, food and grocery, home furniture and furnishing, and others
Device - Smartphones and tablets and PCs

The US online retail market report offers information on several market vendors, including Amazon.com Inc., Apple Inc., Best Buy Co. Inc., Costco Wholesale Corp., eBay Inc., Kroger Co., Target Corp., The Home Depot Inc., Walmart Inc., and Wayfair Inc. among others.

This online retail market in the US research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches.

What will the Online Retail Market Size in the US be During the Forecast Period?

Download the Free Report Sample to Unlock the Online Retail Market Size in the US for the Forecast Period and Other Important Statistics

Online Retail Market in the US: Key Drivers, Trends, and Challenges

The growing seasonal and holiday sales is notably driving the online retail market growth in the US, although factors such as transportation and logistics may impede the market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the online retail industry in the US. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key US Online Retail Market Driver

The growing seasonal and holiday sales is one of the key drivers supporting the US online retail market growth. For instance, from November 1 to December 24, e-commerce sales in the US increased by 11% in 2021, when compared to a massive 47.2% growth in the holiday season of 2020. E-commerce sales made up 20.9 % of total retail sales in the holiday season of 2021, slightly higher than 20.6 percent in 2020. Thanksgiving, Black Friday, and Cyber Monday are the days that see a high amount of online shopping. Apparel, footwear and accessories, consumer electronics, computer hardware, and toys are the largest gaining product categories during the holiday season. Consumers in the US spent $204.5 billion online in November and December 2021, up 8.6% over the same period in 2020. Such exciting sales and offers are driving the market growth.

Key US Online Retail Market Trend

Omni-channel retailing is one of the key US online retail market trends fueling the market growth. It is rapidly becoming the norm for many retailers in the US. It offers consumers the option to shop online and pick up the merchandise from the store nearest to their location on the same day. Retailers are observing a high web influence on their in-store sales. For instance, Best Buy is integrating its offline and online stores to boost revenues. As a part of its omnichannel strategy, the retailer is utilizing physical stores as distribution centers for online purchases. According to Best Buy, 40% of its online shoppers prefer picking up their purchases from physical stores. Best Buy also challenges online and discount retailers with its match-to-price strategy, claiming to offer gadgets at or below the price offered by competitors. Such strategies are expected to boost market growth during the forecast period.

Key US Online Retail Market Challenge

Transportation and logistics are some of the factors hindering the US online retail market growth. Product procurement or sourcing, shipment of ordered items, and delivery to customers are the three major processes where the intervention of transportation and logistics come into the picture. All these processes require a high investment of both time and money, which challenges the efficiency and effectiveness of retailers and their costing strategies. The higher cost incurred from transportation and logistics reduces the margin of retailers, and most of the time, retailers are unable to break even. Between rising fuel prices, driver shortages, as well as a governmental and societal push for increased digitization and sustainability, transport and logistics will continue to be under a lot of pressure. Such factors will negatively impact the market growth during the forecast period.

This online retail market in the US analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2022-2026.

Who are the Major Online Retail Market Vendors in the US?

The report analyzes the market’s competitive landscape and offers information on sever

(CDID: IDIG) Month - Retail Sales Index time series A first estimate of retail sales in value and volume terms for Great Britain, seasonally and non-seasonally adjusted.

Analysis of ‘USA Monthly Retail Sales’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/landlord/usa-monthly-retail-trade on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Introduction

The dataset contains the Monthly sales for retail trade and food services in USA, adjusted and unadjusted for seasonal variations for various categories. These categories shows various kind of Business categories operating in USA. These categories are based on North American Industry Classification System (NAICS).

Dataset Description

• The dataset contains the estimates of Monthly Retail and Food Services Sales by Kind of Business from the year 1992 - 2020. These estimates are shown in millions of dollars and are based on data from the Monthly Retail Trade Survey, Annual Retail Trade Survey, * Service Annual Survey, and administrative records.
• Their are another to files that contain the monthly data for the code NAICS code 44X72: Retail Trade and Food Services: U.S. Total for both Seasonally Adjusted Sales and non Seasonally Adjusted Sales in Millions of Dollars from 1992 to 2020.
• An helper file for NAICS code for retail and food industry is also provided for reference

Acknowledgements

The Dataset was published on U.S. Census Bureau website (https://www.census.gov)

--- Original source retains full ownership of the source dataset ---

Retail sales Y-on-Y in Canada, June, 2025 The most recent value is 6.61 percent as of June 2025, an increase compared to the previous value of 4.96 percent. Historically, the average for Canada from January 1992 to June 2025 is 4.54 percent. The minimum of -30.26 percent was recorded in April 2020, while the maximum of 55.47 percent was reached in April 2021. | TheGlobalEconomy.com

Norway Retail Sales Index: Vol: sa: Specialised Store: ON: Textiles data was reported at 145.800 2000=100 in Dec 2008. This records an increase from the previous number of 144.000 2000=100 for Nov 2008. Norway Retail Sales Index: Vol: sa: Specialised Store: ON: Textiles data is updated monthly, averaging 75.000 2000=100 from Jan 1979 (Median) to Dec 2008, with 360 observations. The data reached an all-time high of 154.700 2000=100 in Nov 2007 and a record low of 53.400 2000=100 in Dec 1988. Norway Retail Sales Index: Vol: sa: Specialised Store: ON: Textiles data remains active status in CEIC and is reported by Statistics Norway. The data is categorized under Global Database’s Norway – Table NO.H008: Retail Sales Index: Volume: 2000=100: Seasonally Adjusted.

Retail sales Y-on-Y in Japan, July, 2025 The most recent value is 0.3 percent as of July 2025, a decline compared to the previous value of 1.9 percent. Historically, the average for Japan from January 2003 to July 2025 is 0.93 percent. The minimum of -13.9 percent was recorded in April 2020, while the maximum of 12 percent was reached in April 2021. | TheGlobalEconomy.com

Counts of supermarkets and counts grouped by size band and type of retailer, across various geographical areas. Geographies include Local Authority Districts (LAD) in the UK, Middle Layer Super Output Area (MSOA) in England and Wales, Intermediate Zones in Scotland, and Super Data Zones in Northern Ireland. Counts of supermarkets per 10,000 people are also provided for LAD level.