In 2025, it was estimated about 95 percent of people in Germany had heard of Black Friday, while around 48 percent had purchased discounted products. This statistic shows the number of people in Germany who had heard of and used Black Friday.

Roughly ************* U.S. consumers surveyed in a recent study said they might go shop in-store for the Black Friday sales in 2024. This made it the most popular response. About the same percent of respondents said that they will definitely not do their Black Friday sales shopping in store.

Comprehensive dataset tracking mobile device share of Black Friday ecommerce sales from 2020 to 2024, including conversion rates, traffic percentages, year-over-year growth, and demographic breakdowns by generation. Data sourced from Adobe Analytics, Salesforce, and Digital Commerce 360.

Comprehensive dataset tracking U.S. online Black Friday sales from 2020-2024 with projections through 2026, including year-over-year growth rates, mobile commerce share, BNPL transaction volumes, and compound annual growth rate analysis. Data sourced from Adobe Analytics, National Retail Federation, and industry research.

The goal of this dataset is to perform a data analysis project to investigate customer purchase behaviour during Black Friday at Walmart, specifically focusing on understanding if there are differences in spending habits between male and female customers. The analysis aims to provide insights to assist Walmart's management team in making informed business decisions.

Nationally representative survey of UK adults (18+) on planned 2025 Black Friday/Cyber Monday participation and spend, with breakdowns by generation, region, and purchase categories.

In late 2025, about *** in ten consumers in the United States said they liked deals with discounts on everything (clearly displayed in percentages) the most when shopping during Black Friday, Cyber Monday, or Singles' Day. About ******* of those who intended to shop during these events liked deals that offer cash back or gift cards.

Dataset Description

The dataset contains sales transactions captured at various retail stores across the United States during the Black Friday shopping event. It includes a comprehensive set of features that provide insights into customer demographics, product categories, and sales patterns. The dataset is designed to help retailers and e-commerce businesses optimize their sales strategies and maximize profits during this critical shopping period.

Key Features

1. User Information: Includes user demographics such as age, gender, marital status, and occupation.
2. Product Information: Covers product categories, subcategories, and specific product details.
3. Purchase Information: Contains the amount spent by each user for each purchase, in U.S. dollars.
4. Geographical Information: Includes city categories and the number of years the buyer has lived in their city.

Dataset Size

The dataset consists of approximately 550,000 records, providing a robust and representative sample of Black Friday sales data.

Purpose

The primary goal of this dataset is to help retailers and e-commerce businesses predict sales and optimize their pricing strategies to maximize profits during the Black Friday shopping event. This dataset can be used to develop machine learning models that can accurately forecast sales and identify trends in customer behavior.

Potential Use Cases

• Sales Forecasting: Use machine learning algorithms to predict sales based on historical data and optimize pricing strategies.
• Customer Segmentation: Identify and analyze customer demographics and purchasing patterns to tailor marketing campaigns and promotions.
• Product Recommendations: Develop personalized product recommendations based on customer preferences and purchase history.
• Store Optimization: Analyze sales data to optimize store layouts, product placement, and inventory management.

In the United States, only around ***** in ** people found Black Friday sales to be good value. An additional ** percent of consumers said while they offered good value, it was not always worth the hassle of shopping on that day. The 2023 survey also revealed that some consumers (** percent of respondents) believed retailers inflated the prices on the run-up to Black Friday and discounted them on the day of the event.

This statistic shows the year-over-year growth in online and offline sales over the course of the Black Friday weekend (from Friday to Cyber Monday) in the United Kingdom (UK) from 2019 to 2024, with a forecast for 2025. In 2024, online sales grew by *** percent and are forecast to see a bigger increase in 2025. Offline sales, on hte other hand, are forecast to increase by nearly *** percent.

Comprehensive dataset tracking Black Friday online order volumes, revenue, mobile vs desktop sales share, product category performance, and year-over-year growth metrics from 2020-2024, compiled from Adobe Analytics, Salesforce Commerce Cloud, and National Retail Federation sources covering over 1 trillion retail site visits.

The Black Friday Sales dataset is a comprehensive collection of sales transaction data from a major retail store during a Black Friday event. This dataset includes over 550,000 observations and 12 key variables, offering valuable insights into customer purchasing behavior during one of the biggest shopping days of the year.

-> Key Features: - User ID: Unique ID for each customer. - Product ID: Unique ID for each product. - Gender: Gender of the customer, either male or female. - Age: The age group of the customer, represented in categories (e.g., 18-25, 26-35, etc.). - Occupation: Occupation category code of the customer. - City_Category: The category of the city where the customer resides, classified as A, B, or C. - Stay_In_Current_City_Years: Number of years the customer has lived in the current city. - Marital_Status: Indicates whether the customer is married (1) or not (0). - Product_Category 1, 2, 3: Product categories associated with the purchased item. - Purchase: The amount spent by the customer on the product.

This dataset can be utilized for analyzing patterns in consumer behavior, demographic-based purchasing tendencies, and predicting future sales trends. It's widely used in data science projects for regression, classification, and recommendation systems, making it ideal for feature engineering, model building, and data visualization.

Comprehensive dataset covering Black Friday retailer participation rates, consumer shopping behavior, sales volumes, and global trends across retail categories for 2020-2024.

Historical Black Friday revenue data showing 11-year trends in U.S. online and retail sales with year-over-year growth analysis and market insights.

Comprehensive dataset tracking Black Friday online sales percentages, growth rates, and market trends from 2019 to 2024, including mobile commerce adoption and payment method statistics.

In Canada, a combined share of 83 percent of consumers intended to either definitely or probably shop during November shopping events such as Singles Day, Black Friday, and Cyber Monday in 2024. This share was also strong in Spain, as well as the United States and Poland.

The Black Friday sale, which originated in Western countries like the United States and Canada, has also spread to other parts of the world. The Black Friday sale market is estimated to be around USD 278,645 million in 2024. With increased exposure due to social media, the industry is set to follow an upward trend in the coming future. By the end of 2034, this valuation is very likely to surpass USD 1,158,302.7 million, exhibiting a CAGR of 15.3%.

Attributes	Description
Estimated Global Black Friday Sale Market Size (2024E)	USD 278,645 million
Projected Global Black Friday Sale Market Value (2034F)	USD 1,158,302.7 million
Value-based CAGR (2024 to 2034)	15.3%

Category-wise Insights

Segment	Consumer Electronics (Product Type)
Value Share (2024)	24.7%

Segment	Men (End User)
Value Share (2024)	36.6%

Country-wise Insights

Countries	CAGR 2024 to 2034
Australia	14%
China	11.6%
India	9.5%
United States	8.5%
Germany	7.9%

Dataset

This dataset was created by zhaopeng

Released under MIT

Contents

Retail sales on Black Friday 2022, more specifically ***********, increased by ** percent across all categories in the United States. For apparel products, the sales increase is expected to reach ** percent.

This Black Friday dataset offers a deep dive into the world of consumer shopping habits during the biggest retail event of the year. It contains thousands of records capturing customer demographics, product preferences, and purchase behavior, all designed to help data enthusiasts explore real-world patterns. Whether you're building a machine learning model, analyzing customer segments, or visualizing trends in spending, this dataset provides a rich and versatile playground. Perfect for regression, classification, and recommendation system projects, it simulates the high-stakes world of retail with clean, structured data that's ready to explore.