Creating a simple churn prediction system is a good starting point for understanding your data and predicting customer behavior. This process involves using prepared data to create a computer program that can identify potential churners.

Key steps in building a basic churn prediction system include:

• Splitting data: Divide your dataset into two parts - one for teaching and one for testing
• Selecting important information: Choose the most relevant data points for prediction
• Choosing a simple method: Start with an easy-to-understand approach like simple probability calculations
• Teaching the system: Use the teaching data to help the system learn patterns of churn
• Testing predictions: Apply the trained system to the test data to see how well it predicts churn^[1]

For example, an e-commerce site might feed data on purchase frequency, time since last purchase, and customer service interactions into a statistical software like R or Python to create a program that predicts which customers are likely to stop buying.