MLS-C01 Question 12
Select 3You are building a machine learning model to predict customer churn for a subscription-based e-commerce platform. The model needs to analyze user behavior and content preferences to predict when customers are likely to cancel their subscriptions. Which data sources should you prioritize for this use case?
- A
User activity logs, such as time spent on the platform and purchase history
- B
Weather data for the user’s location
- C
Customer feedback or support ticket data
- D
Demographic information such as age, gender, and location
- E
Stock market indices and financial data
Show answer and explanation
Correct answers: A, C, D
Explanation
To predict customer churn, it is crucial to analyze data sources that directly reflect user behavior, satisfaction, and demographic patterns. User activity logs provide insights into engagement, customer feedback captures dissatisfaction, and demographic information helps in identifying trends. Weather data and stock market indices are not relevant to this specific use case.
- A. Correct.
User activity logs provide critical insights into user behavior, such as engagement patterns and purchase habits, which are directly relevant to predicting churn.
- B. Incorrect.
Weather data is not typically relevant to a customer churn prediction model in an e-commerce context unless the platform has a direct dependency on weather conditions, which is not indicated here.
- C. Correct.
Customer feedback or support ticket data can reveal dissatisfaction or frequent issues that may lead to churn, making it a valuable data source.
- D. Correct.
Demographic information helps in segmenting customers and identifying patterns in churn rates across different groups, making it a useful data source.
- E. Incorrect.
Stock market indices and financial data are not directly relevant to predicting customer churn for a subscription-based e-commerce platform.