Enhancing Targeted Marketing Strategies: Interpretable Uplift Modeling to Identify Key Client Segments

Abstract

Abstract Uplift modeling stands at the forefront of machine learning innovation, serving as a pivotal tool for quantifying the causal effect of marketing strategies on consumer decisions. This paper explores the intricacies of uplift modeling, examining its theoretical framework and the practical application of its principal methodologies: the one-model, two-model, and class transformation techniques. Through an empirical study of a Portuguese bank's direct phone marketing campaigns, we aim to discern the predictive capability of uplift modeling in determining client engagement with term deposit subscriptions. We present a detailed comparative analysis of the uplift modeling techniques, scrutinizing their effectiveness and limitations within the context of our dataset. The findings reveal that the class transformation approach, specifically using CatBoost, significantly outperforms its counterparts, providing a marked increase in predictive accuracy and customer conversion rates. This insight emphasizes the potential of uplift modeling to identify key customer segments for targeting, enhancing the precision and ROI of marketing initiatives. Our study contributes to the expanding literature on causal inference and targeted marketing, providing a pathway for businesses to fine-tune their marketing strategies. The application of the uplift models detailed in this paper transcends the scope of banking, offering a versatile framework for various industries to deploy data-informed decisions, thereby fostering revenue growth and optimizing marketing expenditures.