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.

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!!

Dataset History

A retail company, “ABC Private Limited” wants to understand the customer purchase behavior (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 (product ID 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.

The below-mentioned points are just given to get you started with the dataset; it is 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!!

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.

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.

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.

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!!

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.

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.

Black Friday Deals Srl recorded an import turnover of USD 74,818.19 million and an export turnover of USD 0 between December 2024 and November 2025. Explore detailed trade value insights, supply chain analytics, HS code-wise data, shipment history, partner countries, customs trade values, top import and export commodities with pricing, buyers, suppliers, ports, and key competitors in Costa Rica.

In 2022, Black Friday sales in the Middle East and North Africa reached ** billion U.S. dollars. This figure was a ** percent increase over the previous year, and it is forecast to grow by another ***** percent in 2023, reaching ** billion U.S. dollars.

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.

In 2025, online retail sales during Black Friday and Cyber Monday were forecast to reach *** billion euros in Italy. This represented a ***** percent increase from the previous year.

Almost **** of consumers in the United Kingdom intended to search for deals directly on the retailers websites during November's promotional period in 2025. Emails from retailers and discount websites were also popular methods to look for deals, with ** and ** percent of consumers using these channels, respectively.

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 2024, with a forecast for 2025, broken down by online and offline spending. Consumers are expected to spend a total of over ************ British pounds over the course of the Black Friday weekend in 2025, over ************ of which will be spent online.

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.”

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.

During 2022's Black Friday season retailers across Brazil sold approximately 5.19 million book copies. The revenue generated through these sales reached 213.8 million Brazilian reals, however, both these figures represented a decrease compared to the same period in 2021.

A survey probing Black Friday shopping intentions and opinions of U.S. consumers found that Gen Z was the generation that regretted their past Black Friday purchases the most, with ** percent of respondents. Generally, respondents across all age groups were found to regret having made purchases during Black Friday sales, with the exception of baby boomers.

As Black Friday is becoming one of the most popular and anticipated shopping events globally, online sales transactions made on this day are seeing surges across European countries as well. In 2019, Black Friday, which fell on November 29, online retail transactions saw the greatest growth in the Nordic countries of Denmark and Norway, with *** and *** percent change compared to the previous month of the same year, respectively. Cyber Monday was the less popular shopping occasion of the two. In the United Kingdom, online retail transactions grew by *** percent on Cyber Monday in 2019.