Customer Behavior Analysis

In this lesson, you will learn about customer behavior and what analysis can we draw from its data.

What is customer behavior?

The decisions and instincts that make a customer buy a certain product or service can be described as customer behavior.

With the advent of targeted marketing, traditional marketing techniques are getting obsolete with every new day. The rise of digital marketing, where every customer is shown advertisements particular to their interests and habits, has taken over the world.

This insight into customer’s interests and habits is obtained through an extensive customer behavior analysis approach. We will try to implement a very basic level of this approach that will include finding the products that are selling more and at which time of the day. Then we will group the customers according to their buying habits.

Why is it important?

Do you know that the average attention span of a person is at an all-time low? This means that an average advertiser or salesperson has only seven seconds to grasp a customer’s attention before they move to another product as there are so many options available for them to choose from.

A customer will only be interested in your product if they somehow get convinced that it aligns with their interests and habits.

The dataset

The dataset used in this project was made by collecting information from an e-commerce store with products in multiple categories. The data is only for the months of October and November for the year 2019. The dataset description can be found here.

The October dataset can be downloaded from here and the November dataset can be downloaded from here. As the datasets are available on Kaggle, it might require you to log in before the datasets can be downloaded.


We will try to provide promising results for the following queries:

  1. Do users prefer the products of a specific brand?

  2. What is the user’s activity(view, cart, buy) throughout the day?

  3. Items from which brands and categories are most preferred by users?

  4. Can we effectively conduct targeted marketing?

In the next lesson, we explore the data along with some other analysis.

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