How to perform churn analysis efficiently: step by step guide

This post details how to perform churn analysis efficiently using a step-by-step guide. It covers data collection and preprocessing, including handling nulls and data type conversion for columns like 'TotalCharges', and encoding categorical features using LabelEncoder. It also discusses customer segmentation by cohort and the importance of tracking key churn metrics such as churn rate, revenue churn rate, customer retention rate by cohort, customer engagement metrics, and Net Promoter Score (NPS). The post highlights the use of Hex notebooks for this analysis, leveraging both SQL and Python, and mentions Hex AI for assistance.