Black Friday Data Analysis

Is your Black Friday wishlist ready?

Black Friday is a shopper’s delight. It is a day full of special shopping deals and big discounts and is considered the beginning of the holiday shopping season. On the flipside, businesses look at this day as an opportunity to win new customers, boost sales, and increase profits. The name ‘Black Friday’ is a reference to the black ink which was used to record profits!(Unfortunately it's often the time of year when e-commerce fraud also peaks.)

To make the most of the Black Friday sale, business owners can look at past data. That way, they can decide which products to offer at high discounts, which demographics to market to, and more.

In this blog post, we will use Gigasheet for exploring a Black Friday dataset, and answer some questions. Let's get started!

About the Dataset

We are using the dataset ‘BlackFriday.csv’ from Kaggle.

Explore this data in Gigasheet ↗️No login required

This CSV file contains 550,000 records about Black Friday purchases in a retail store. The dataset has the following attributes:

●        User_ID : A unique value identifying each buyer

●        Product_ID: A unique value identifying each product

●        Gender : The gender of the buyer (M or F)

●        Age: The buyer's age

●        Occupation: The buyer's occupation, specified as a numeric value

●       City_Category: The city category in which the purchase was made

●        Stay_In_Current_City_Years: The number of years that a buyer has lived in their city

●        Marital_Status: The marital status of the buyer. 0 denotes single, 1 denotes married.

●       Product_Category_1: The main category of the product, specified as a number.

●        Product_Category_2: The first subcategory of the product

●        Product_Category_3: The second subcategory of the product

●        Purchase: The amount spent by a user for a purchase, in dollars

Data Exploration With Gigasheet

Gigasheet is the perfect tool to make sense of massive datasets like these. It is a free, no-code data exploration tool that can open large CSV files with ease. Gigasheet is more than an online CSV viewer, it's really a spreadsheet backed by a big data analytics platform.

So, if you aren’t a coder, don’t worry. You can still enjoy exploring data, with just a few clicks.  

  • First, create a free Gigasheet account.
  • Next, click on 'New'.
  • Then, navigate to your file location on your device, or a cloud drive.
  • That's it. Gigasheet will open your dataset for you, no matter the size.

Let us now explore the dataset and try to answer some burning questions about Black Friday.

A. Who Spends More: Men or Women?

Hold your guesses. Let use see what the dataset has to say. We will use Gigasheet’s Group feature to answer this question.

Here, we are grouping the dataset by gender. Next, we are using the ‘Sum’ aggregate function on the column ‘Purchases’ to calculate the total amount spent by each of these groups.

Adding a Group By clause on the column Gender
The results of grouping by gender

As you can see, there are 414,259 rows for group M and 135,809 for group F.  And so, the total amount spent by men is much higher for this dataset.

Let us now visualize this data with a pie chart. To create a chart, select the rows you want to display, right click, and select 'Chart Range.' Then choose the type of chart you want.

Creating a pie chart with grouped data
Result of the pie chart

B. Who Are The Top 10 Buyers?

Let us once again use Gigasheet’s group feature and group the records by User_Id. We will also use the 'Sum' aggregate function like the previous example to calculate the sum of purchases made by each buyer, and create a bar chart.

Grouping by user id and calculating the sum of purchases
bar graph depicting user ids and purchases

C. Which Were The Highest-Sold Products?

To find out the products which were the most sold, let us group by the column ‘Product Id’.

To count the number of sales, we can use the ‘Row Count’ function for the User Id column for each group.

Grouping by product id
Number of products sold

D. How Do Married People Shop?

Time to use a filter! In this dataset, the column Marital_Status indicates a user’s marital status, with 0 being single and 1 being married. So, let us apply a the following filter:

Filtering the dataset by marital status
Results of the filter

Suppose a business owner wants to know how different age groups of married people behave. So, we will once again use the Group feature, and group the filtered row set by Age.  

Grouping the filtered dataset by age
Bar graph depicting age groups and purchases

As evident from the chart, people between the ages of 26 and 35 are the top buyers among married people.

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