Summary on Analyzing Churn and LTV Using SQL and Python

In this session, we performed an analysis of churn and LTV on SQL and Python.

The approach that we followed for the analysis is described here:

1. We began by creating the churn database using all the csv files that were available to us.
2. Next, Favio created an ERD using dbdiagram.io and then checked whether the database satisfied the properties of an RDBMS by validating the normalization rules.
3. Then we opened the SQL browser and performed some data exploration and analysis on the churn data.
4. Further, we moved to Python, where we removed missing values and joined all the tables to create a single table with all the data that we have with us.
5. After generating the combined dataframe, we found that there was a significant difference in the LTVs of the top 20% and bottom 80% customers.
6. Finally, we performed a complex analysis to check which factors influence the LTVs of the customers and what are the conjectures that we can draw for pulling people into the high-LTV group.