Abstract

State-of-the-art CRMs currently are increasingly leveraging AI to predict the behavior of a customer and prescribe a "Next Best Action.". Yet most of these systems work as "black boxes," which erodes user trust and precludes effective human oversight. This work describes a new framework for an AI-powered CRM, embedding explainability, user override, and an auditable log directly into the User Experience. The proposal is a system that decomposes recommendations into clear components: Next Best Customer, Next Best Message, and Next Best Action. The research is based on the simulated dataset 'RetailInteract-484,' consisting of 484 unique instances of customers with rich transactional and behavioral data. Approach is to develop in Python an Explainable Hybrid Recommendation Engine, EHRE, integrated with a Feature Importance Module, FIM, furnishing transparent, human-readable explanations for every recommendation. A prototype for the front-end UX was created and tested within the simulated environment; the proposed human-in-the-loop approach not only increased user trust but also yielded improvement in simulated customer conversion rates over a fully automated system. The framework includes an auditable log to enable continuous learning, as well as compliance with regulations.

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