Prediction & Forecasting for Churn Prediction & Prevention

1. Data Collection: Gather historical customer data from various sources such as CRM systems, transaction databases, and customer support logs. 2. Data Cleaning: Clean the data to remove duplicates, fill in missing values, and standardize formats. 3. Feature Engineering: Identify and create relevant features that may influence churn, such as customer demographics, usage patterns, and engagement metrics. 4. Model Selection: Choose appropriate predictive modeling techniques (e.g., logistic regression, decision trees, random forests, or neural networks). 5. Model Training: Split the dataset into training and testing sets, and train the model using the training data. 6. Model Evaluation: Assess the model's performance using metrics such as accuracy, precision, recall, and F1 score on the testing set. 7. Prediction: Use the trained model to predict churn probabilities for existing customers. 8. Actionable Insights: Generate reports and dashboards to visualize churn predictions and identify at-risk customers. 9. Strategy Development: Develop targeted retention strategies based on insights from the predictions. 10. Monitoring: Continuously monitor model performance and customer feedback to refine the model and strategies over time.