Customer Churn Analysis: EDA

Customer Churn Analysis: EDA

Part 1: Exploratory Data Analysis. Understanding customer churn is vital to the success of a company and a churn analysis is the first step to understanding the customer.

In todays commercial world competition is high and every customer is valuable. Understanding the customer is of utmost importance, including being able to understand the behavior patterns of that customer. Customer Churn is the rate at which a commercial (very prevalent in SaaS platforms) customer leaves the commercial business and takes their money elsewhere. Understanding customer churn is vital to the success of a company and a churn analysis is the first step to understanding the customer.

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I decided to perform a churn analysis from a Kaggle data set which gives the customer information data of a telecommunications company (Telcom) trying to better understand their customer churn likelihood. While we will eventually build a classification model to predict likelihood of customer churn, we must first take a deep dive into the Exploratory Data Analysis (EDA) process to get a better understanding of our data. Github Repository with code and notebooks can be found here.

The Data

As mentioned above, the data is sourced from Kaggle. In our dataset, we have 7043 rows (each representing a unique customer) with 21 columns: 19 features, 1 target feature (Churn). The data is composed of both numerical and categorical features, so we will need to address each of the datatypes respectively.

Target:

  • Churn — Whether the customer churned or not (Yes, No)

Numeric Features:

  • Tenure — Number of months the customer has been with the company
  • MonthlyCharges — The monthly amount charged to the customer
  • TotalCharges — The total amount charged to the customer

data analysis

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