Enhancing Targeting in CRM Campaigns through Explainable AI

This research article investigates the application of explainable AI (XAI) methods in customer relationship management (CRM) campaigns. The study focuses on three specific XAI techniques: SHAP, LIME, and ELI5, to analyze the outcomes of CRM campaigns by examining a dataset that records customer interactions with campaign content. The primary objective is to identify key features that influence customer responses, thereby improving targeting strategies. The research emphasizes the importance of interpretability in AI systems, arguing that transparent AI can enhance trust and facilitate data-driven decision-making in CRM contexts. By providing clear explanations for customer behavior, organizations can refine their marketing tactics and increase campaign effectiveness. The findings contribute to the understanding of customer engagement and offer a structured approach to leveraging AI in CRM, highlighting the necessity for AI systems to be both effective and comprehensible to users. This study ultimately aims to bridge the gap between AI-driven insights and human understanding in marketing.