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.

Unlock the secrets of the booming Black Friday market! Discover key trends, CAGR projections, and regional insights from our comprehensive analysis. Learn how top retailers like Amazon and Walmart are capitalizing on this massive shopping event. Explore the future of Black Friday sales and plan your strategy for 2025 and beyond.

Compared to sales that took place in the preceding eight weeks, retail sales during Black Friday grew by varying degrees in the United Kingdom in 2020. On Black Friday, the highest increase was observed in the apparel and accessories category, with a sales increase of *** percent. Cyber Monday, which immediately follows holiday season’s most anticipated retail event, led to increased activity as well.

UK: where the biggest Black Friday happens In Europe, Black Friday stimulates the highest sales in the United Kingdom. In 2020, the total value of sales during Black Friday and Cyber Monday amounted to *** billion British pounds. Online purchases made up the majority of the projected value of Black Friday and Cyber Weekend sales in the UK.

Black Friday 2021: what are UK consumers buying?
In 2021, consumers in the older age brackets were preparing to allocate higher budgets for their Black Friday purchases, over *** British pounds each. In comparison, Gen Z and millennials were not planning to splash their money on Black Friday purchases. London was projected to be the region where consumers were intending to spend the highest amount during this retail event.

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.

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.

In 2020, Black Friday e-commerce in Brazil accounted for approximately *** percent of the South American country’s annual online sales revenue. A year earlier, the share nearly reached *** percent. According to another source, Brazil’s online shopping revenue increased by nearly ** percent between 2019 and 2020. This suggests that mobility restrictions – in response to the COVID-19 pandemic – led online consumers to shop more during the entire year of 2020 and focus less on Black Friday alone.

A nonstop e-commerce favorite Black Friday’s online shopping revenue in Brazil has consistently increased at least since 2013. In 2020, it surpassed four billion Brazilian reals, growing by more than ** percent compared with the previous year. This made Black Friday the country’s second-biggest online shopping holiday in terms of revenue that year, just behind the Lovers’ Day season.

New trends and old challenges The outbreak of novel coronavirus also took meals, food, and medicines to the top five most popular mobile shopping categories in Brazil in 2020, along with clothing and electronics. This last segment's success, at times, takes its toll. A few days before Black Friday, some e-tailers rose prices of consumer electronics and announced discounts on them when the date came. In response, Brazilian shoppers created an informal slogan for Black Friday: “everything for half of the doubled price.”

Comprehensive dataset of Black Friday 2024 spending patterns including average spend per shopper ($104), channel-specific spending (online $124, in-store $82), generational spending differences, payment method analysis, and five-year historical trends from 2020-2024 covering 152 million U.S. shoppers.

While in 2022, consumers in the United States spent on average ****** U.S. dollars during the Black Friday period, this figure is set to rise in 2023, with consumers saying they will likely spend more.

This statistic shows the value of spending over the course of the Black Friday weekend (from Friday to Cyber Monday) in the United Kingdom (UK) from 2016 to 2023, with a forecast for 2024, broken down by online and offline spending. Consumers are expected to spend a total of over nine billion British pounds over the course of the Black Friday weekend in 2024, nearly five billion of which will be spent in stores.

Black Friday spending was forecast to be the higher in the United Kingdom (UK) than any other European nation in 2020, although total figures are expected crash in 2020 due to the impact of coronavirus restrictions and lockdown measures in place in many European countries. Following the UK, the second biggest Black Friday spender was Germany, where Black Friday weekend and Cyber Monday sales value reached *** billion in 2018 and *** billion British pounds in 2019. In 2020, Germany is predicted to spend around * percent less during Black Friday weekend.

The most popular retail event of the holiday season

Black Friday might be an American shopping phenomenon by origin, but European consumers have adopted it with similar degrees of zealotry. Across the EMEA region, Black Friday stood out among other special retail events during the holiday season in 2018. The number of daily retail transactions that took place on this day put Black Friday ahead of more traditional holiday season discount opportunities such as Christmas and Boxing Day sales. Perhaps not surprisingly, the most purchased products on Black Friday were in the tech category.

Millions of Black Friday transactions on Amazon

Black Friday is simultaneously a challenge and an opportunity for retailers, be it brick-and-mortar or online. One retailer that springs to mind immediately on this day is Amazon. In the UK, the total number of transactions made during 2018 Black Friday on Amazon’s UK platform saw a ** percent increase from about two weeks before. On Black Friday’s twin event, Cyber Monday, the total number of transactions was just shy of **** million.

Expected to reach ** billion U.S. dollars, Cyber Monday is the shopping day with the highest e-commerce sales revenue in the United States in 2023. Black Friday ranks second, with over **** billion dollars in online revenue according to the latest forecasts.

Dataset History

A retail company “ABC Private Limited” wants to understand the customer purchase behaviour (specifically, purchase amount) against various products of different categories. They have shared purchase summaries of various customers for selected high-volume products from last month. The data set also contains customer demographics (age, gender, marital status, city type, stay in the current city), product details (productid and product category) and Total purchase amount from last month.

Now, they want to build a model to predict the purchase amount of customers against various products which will help them to create a personalized offer for customers against different products.

Tasks to perform

The purchase column is the Target Variable, perform Univariate Analysis and Bivariate Analysis w.r.t the Purchase.

Masked in the column description means already converted from categorical value to numerical column.

Below mentioned points are just given to get you started with the dataset, not mandatory to follow the same sequence.

DATA PREPROCESSING

• Check the basic statistics of the dataset

• Check for missing values in the data

• Check for unique values in data

• Perform EDA

• Purchase Distribution

• Check for outliers

• Analysis by Gender, Marital Status, occupation, occupation vs purchase, purchase by city, purchase by age group, etc

• Drop unnecessary fields

• Convert categorical data into integer using map function (e.g 'Gender' column)

• Missing value treatment

• Rename columns

• Fill nan values

• map range variables into integers (e.g 'Age' column)

Data Visualisation

• visualize individual column
• Age vs Purchased
• Occupation vs Purchased
• Productcategory1 vs Purchased
• Productcategory2 vs Purchased
• Productcategory3 vs Purchased
• City category pie chart
• check for more possible plots

All the Best!!

The Black Friday in the UK – 2019 report forms part of GlobalData’s Retail Occasions series, and offers a comprehensive insight into the consumer dynamics and spending habits of UK consumers for Black Friday The report analyses the market, the major players, the main trends, and consumer attitudes. Read More

In recent years, online spending via desktop devices during Cyber Monday has skyrocketed in the United States, reaching approximately *********** U.S. dollars in 2020. This figure represents an increase of nearly ** percent over the previous year. In its 2020 version, Black Friday recorded a desktop spend of about *** billion dollars, whereas Thanksgiving expenditure amounted to some *** billion dollars.

According to a survey, over ** percent of consumers in Spain planned to spend at least *** euros during Black Friday in 2025. More than a third of Spanish shoppers planned to spend between *** and *** euros in the mega sale event in 2025.

Walmart Sales Forecast

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Problem:

There are many seasons that sales are significantly higher or lower than averages. If the company does not know about these seasons, it can lose too much money. Predicting future sales is one of the most crucial plans for a company. Sales forecasting gives an idea to the company for arranging stocks, calculating revenue, and deciding to make a new investment. Another advantage of knowing future sales is that achieving predetermined targets from the beginning of the seasons can have a positive effect on stock prices and investors' perceptions. Also, not reaching the projected target could significantly damage stock prices, conversely. And, it will be a big problem especially for Walmart as a big company.

Aim:

My aim in this project is to build a model which predicts sales of the stores. With this model, Walmart authorities can decide their future plans which is very important for arranging stocks, calculating revenue and deciding to make new investment or not.

Solution:

With the accurate prediction company can;

• Determine seasonal demands and take action for this
• Protect from money loss because achieving sales targets can have a positive effect on stock prices and investors' perceptions
• Forecast revenue easily and accurately
• Manage inventories
• Do more effective campaigns

Plan:

1. Understanding, Cleaning and Exploring Data

2. Preparing Data to Modeling

3. Random Forest Regressor

4. ARIMA/ExponentialSmooting/ARCH Models

Metric:

The metric of the competition is weighted mean absolute error (WMAE). Weight of the error changes when it is holiday.

Understanding, Cleaning and Exploring Data: The first challange of this data is that there are too much seasonal effects on sales. Some departments have higher sales in some seasons but on average the best departments are different. To analyze these effects, data divided weeks of the year and also holiday dates categorized.

Preparing Data to Modeling: Boolean and string features encoded and whole columns encoded.

Random Forest Regressor: Feature selection was done according to feature importance and as a best result 1801 error found.

ARIMA/ExponentialSmooting/ARCH Models: Second challange in this data is that it is not stationary. To make data more stationary taking difference,log and shift techniques applied. The least error was found with ExponentialSmooting as 821.

Findings:

• Although some departments has higher sales, on average others can be best. It shows us, some departments has effect on sales on some seasons like Thanksgiving.
• It is same for stores, means that some areas has higher seasonal sales.
• Stores has 3 types as A, B and C according to their sizes. Almost half of the stores are bigger than 150000 and categorized as A. According to type, sales of the stores are changing.
• As expected, holiday average sales are higher than normal dates.
• Top 4 sales belongs to Christmas, Thankgiving and Black Friday times. Interestingly, 22th week of the year is the 5th best sales. It is end of May and the time when schools are closed.
• Christmas holiday introduces as the last days of the year. But people generally shop at 51th week. So, when we look at the total sales of holidays, Thankgiving has higher sales between them which was assigned by Walmart. But, when we look at the data we can understand it is not a good idea to assign Christmas sales in data to last days of the year. It must assign 51th week.
• January sales are significantly less than other months. This is the result of November and December high sales. After two high sales month, people prefer to pay less on January.
• CPI, temperature, unemployment rate and fuel price have no pattern on weekly sales.

More detailed finding can be found in notebooks with explorations.

Future Improvements:

• Data will be made more stationary with different techniques.

• More detailed feature engineering and feature selection will be done.

• More data can be found to observe holiday effects on sales and different holidays will be added like Easter, Halloween and Come Back to School times.

• Markdown effects on model will be improved according to department sales.

• Different models can be build for special stores or departments.

• Market basket analysis can be done to find higher demand items of departments.

Please feel free look at EDA on Tableau.

And you can find CRISP-DM on my Github.

This statistic shows the results of a survey conducted in the United States in October 2017. U.S. consumers were asked how their total spending would most likely compare with the previous year's Black Friday expenditure. During the survey, ** percent of the respondents living in suburban communities said that they will spend more on Black Friday 2017.

E‑Commerce Peak Planning Services Market Outlook

According to our latest research, the global E-Commerce Peak Planning Services market size reached USD 7.8 billion in 2024, with a robust year-on-year growth driven by the surge in online retail activity and the increasing complexity of managing peak sales periods. The market is expected to expand at a CAGR of 13.2% from 2025 to 2033, reaching an estimated USD 23.4 billion by 2033. This growth is fueled by the rapid digital transformation across industries, the rising adoption of omnichannel retail strategies, and the critical need for seamless customer experiences during high-traffic events such as Black Friday, Cyber Monday, and festive sales. As per our latest research, businesses are increasingly recognizing the necessity of specialized solutions to optimize operations, manage logistics, and enhance marketing effectiveness during peak e-commerce periods.

The primary growth driver for the E-Commerce Peak Planning Services market is the exponential rise in online shopping, especially during seasonal sales and promotional events. Retailers and brands are witnessing unprecedented spikes in order volumes, necessitating advanced planning and execution capabilities to handle the surge efficiently. The proliferation of mobile commerce, expanding internet penetration, and the growing comfort of consumers with digital transactions have all contributed to this trend. Furthermore, the competitive landscape in e-commerce is intensifying, pushing businesses to seek expert consulting, strategy development, and technology implementation services to outperform rivals during peak periods. These services are crucial for ensuring inventory readiness, optimizing website performance, and delivering exceptional customer service, which collectively drive higher conversion rates and customer loyalty.

Another significant factor propelling market growth is the integration of advanced technologies such as artificial intelligence, machine learning, and predictive analytics into peak planning services. These technologies enable e-commerce enterprises to forecast demand more accurately, personalize marketing campaigns, and automate critical processes, thereby reducing operational bottlenecks and enhancing scalability. The adoption of cloud-based solutions further supports real-time data analysis, agility, and collaboration among different stakeholders, making it easier to respond to rapidly changing market dynamics. As retailers increasingly rely on data-driven insights to make informed decisions, the demand for comprehensive peak planning services that encompass technology implementation, logistics optimization, and promotional strategies continues to rise.

The ongoing shift towards omnichannel retailing also plays a pivotal role in accelerating the growth of the E-Commerce Peak Planning Services market. Consumers now expect a seamless shopping experience across various touchpoints, including online stores, mobile apps, and physical outlets. This requires meticulous coordination of inventory, logistics, and customer engagement strategies, particularly during peak periods. Service providers are responding by offering integrated solutions that align digital and physical operations, enhance last-mile delivery efficiency, and ensure consistent brand messaging. The growing adoption of these services by both established retailers and emerging direct-to-consumer brands highlights the market’s expanding scope and the critical importance of peak planning in achieving business objectives.

From a regional perspective, North America remains the largest market for E-Commerce Peak Planning Services, owing to its mature e-commerce ecosystem, high consumer spending, and early adoption of advanced technologies. However, Asia Pacific is emerging as the fastest-growing region, driven by the rapid digitization of retail in countries such as China, India, and Southeast Asian nations. The region’s large and increasingly affluent population, coupled with the proliferation of online marketplaces and mobile commerce, is creating substantial opportunities for service providers. Europe also presents significant growth potential, particularly in markets like the United Kingdom, Germany, and France, where e-commerce penetration is high and retailers are investing heavily in digital transformation initiatives. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth as internet access improves and e-commer

In 2025, holiday retail sales in the United States were forecast to reach over************* U.S. dollars. This figure was given as a conservative estimate; retail sales over the holiday season were projected to be between ****** billion U.S. dollars and *******billion U.S. dollars in 2025. Holiday retail sales have risen substantially since the turn of the century, reaching approximately 416 billion U.S. dollars in 2002. Holiday retail sales are a fraction of total retail sales in the United States, which were around ************** U.S. dollars in 2023. Holiday season e-commerce is also on the rise, with increasing numbers of retailers and consumers going digital. What makes up the winter holiday season in the United States? The winter holiday season includes shopping occasions such as Thanksgiving weekend - which is made up of Black Friday, Small Business Saturday, and Cyber Monday, Super Saturday – the last Saturday before Christmas, and Christmas itself. Thanksgiving weekend is a very popular time for consumers to partake in holiday shopping. In 2022, over *** million U.S. consumers shopped on Black Friday. Leading companies in U.S. retail The domestic retail market in the United States is very competitive, with many companies recording substantial retail sales. Walmart, a retail chain offering low prices and a wide selection of products, is the leading retailer in the United States. Amazon, The Kroger Co., Costco, and Target are a selection of other leading U.S. retailers.