Enhancing Targeting in CRM Campaigns Through Explainable AI

Abstract

Modern customer relationship management (CRM) solutions are vital to firms because they streamline the administration of customer interactions, sales processes, and marketing initiatives. To fully exploit the potential of massive volumes of customer data, these platforms need help from AI techniques to quickly evaluate and extract useful insights, personalize customer experiences, and optimize decision-making to improve business outcomes. This study delves into the use of explainable AI methods like SHAP, LIME, and ELI5 to analyze CRM campaign outcomes. The purpose of this research is to discover essential traits that serve as indications for successful targeting by analyzing a dataset that captures the results of customers’ interactions with campaign content as responder or non-responder. Using these methods improves interpretability and closes the gap between AI-driven decision-making and human understanding. The findings add to the field by offering clear rationales for consumer actions, which in turn helps companies fine-tune their targeting tactics and boost the efficiency of their campaigns. This study emphasizes the value of AI systems being transparent and interpretable in order to promote trust and enable data-driven decision-making in CRM contexts. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.