Customer Churn Prediction

The Customer Churn Prediction interview tests your ability to build a data-driven solution for identifying customers likely to churn. This mock interview reflects real business problems where you must analyze data, design predictive models, and communicate insights that improve customer retention.

You’ll complete an end-to-end workflow — from data wrangling and SQL querying to exploratory data analysis (EDA), feature engineering, modeling, and result interpretation. The goal is to assess how effectively you combine technical skill with strategic thinking to drive data-informed decisions.

This interview challenges you to prepare and analyze customer data, identify behavioral and demographic churn signals, and apply models such as logistic regression, decision trees, or ensemble methods. You’ll also practice translating model findings into clear business recommendations, focusing on actions that enhance retention and revenue.

By completing this interview, you’ll gain practical experience in solving churn prediction problems and strengthen your end-to-end data science capabilities, preparing for analytical and ML interviews at leading companies.

Goals of the Interview:

1. Define the churn prediction problem and objectives.
2. Perform SQL-based data extraction and cleaning.
3. Conduct EDA to uncover churn patterns.
4. Build and evaluate predictive churn models.
5. Communicate insights for customer retention.