Personalized Email Marketing: A Machine Learning Approach for Higher Engagement and Conversion Rates

This research analyzes machine learning to aid in increase of purchase rates of product-promo-code pairings in digital marketing. We evaluate the effectiveness of these pairings using four machine learning models: These include Decision Trees, Naive Bayes, Latent Dirichlet Allocation (LDA), and Light Gradient Boosting Machine (Light GBM). After the comparison of classification report and kappa statistics, Light GBM was identified as a model that yields the highest accuracy rate of 86% and the kappa score of 0. 71. Thus, this research investigates the impact of machine learning in addressing marketing optimization problems and enhancing the conversion. Altogether, the findings confirmed the relevance of data analysis for increasing the efficiency of marketing strategies and offered practical recommendations for companies interested in improving the purchase rates with proper combinations of products and codes.