Implementing churn prediction models involves several steps:
Data Collection: Gather data from various sources such as email analytics, CRM systems, and customer behavior records. Data Preprocessing: Clean and normalize the data to make it suitable for analysis. Feature Engineering: Identify and create relevant features that will be used in the prediction model. Model Selection: Choose an appropriate machine learning model based on the nature of the data and the problem. Model Training: Train the model using historical data to learn patterns and trends. Model Evaluation: Test the model on a separate dataset to evaluate its performance and accuracy. Deployment: Deploy the model in a live environment to start making predictions.