Several algorithms can be used for building predictive models in email marketing. The choice depends on the specific use case and the nature of the data:
- Logistic Regression: Useful for binary outcomes like predicting whether a customer will open an email or not. - Decision Trees: Good for understanding the decision-making process of different customer segments. - Random Forest: An ensemble method that improves the accuracy and robustness of predictions. - Neural Networks: Effective for complex relationships but require a larger dataset and computational power.