Framework for Predicting Customer Channel Preference using Machine Learning

Understanding customer preferences across communication channels is critical for optimizing marketing strategies in the automotive industry. This paper introduces a machine learning framework that predicts customer engagement rates for various channels, including email, SMS, and social media. By linking historical engagement data with future behaviors using observation and performance windows, the framework enables precise channel assignment based on predictive scores. Applications in vehicle purchase campaigns and after-sales promotions highlight the framework's potential to improve marketing efficiency and customer satisfaction. Challenges like data sparsity and interpretability are discussed, along with proposed mitigation strategies.