Customer Churn Prediction

Background:

• A Marketing Analytics team at company XYZ wants to ensure successful and profit-making rollout for their streaming platform in a new geographical region
• The specs shared with us on the available subscription plan for customers are 30/90 days of free trial, following which they will need to pay based on their plan of selection
• The team has the task of coming out with effective metrics to ensure the customers signing up, stay with the company beyond the free trial, thereby generating revenue
• In such a scenario, understanding and tracking Customer Churn Rate as a key metric could help us be proactive in our engagement with our customers and potentially save a churn before it could even happen
• In the code notebook provided below, steps towards detailed Exploratory Data Analysis (EDA) and Machine Learning Modeling to predict churn has been shown in Python
• While the presentation deck covers storyline, presentation of insights to action and how the predictions of churn from the machine learning model can translate to profit in marketing revenue!

Resources:

• Link to presentation (case study)
• Link to code notebook
• Link to Kaggle dataset