Churn and Lifetime Value Case Study using SQL, Python and Tableau

In this segment, you will learn about two key metrics: 

1. Customer churn
2. Customer lifetime value (CLV)

In the forthcoming video, Favio will help you understand what customer churn is. Let us go ahead and watch the video.

In the next segment, Favio will show you how to install and work with SQLite.

So, as you saw in the previous video, in this case study, we will be using the tools:

1. SQLite,
2. Python, and
3. Tableau.

Like MySQL, SQLite is an open-source RDBMS based on the structured query language, or SQL.

So, in the video, you learned the concept of customer churn.

Customer churn can be defined formally as the percentage of customers who have discontinued their subscription to a service in a given period of time. It can be expressed as shown here

$\frac{\left(NumberofCustomerslost\right)}{TotalCustomersstartedwith}=ChurnRate$

Moving on, in the next video, you will learn about another key metric: customer lifetime value. Let’s watch the video.

So, in the video, you learned that customer lifetime value (CLV) is the net profit associated with a customer for a company over a fixed period of time. A customer with a high CLV is an indicator of recurring revenue, and companies are willing to take measures to retain such customers for as long as possible.

So, with that, you must have understood the key metrics that we will be analyzing throughout the case study. In the next video, Favio will help you understand the approach that we will be following to integrate Python, SQL, and Tableau in this case study.

So, in the video, Favio explained the approach that we will be following to carry out this case study. The detailed approach is given here.