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Auditable and Overridable Next‑Best Recommendations for Enterprise Customer Relationship Management
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 67 |
| Year of Publication: 2025 |
| Authors: Sitaram Srivatsavai |
10.5120/ijca2025926118
|
Sitaram Srivatsavai . Auditable and Overridable Next‑Best Recommendations for Enterprise Customer Relationship Management. International Journal of Computer Applications. 187, 67 ( Dec 2025), 1-7. DOI=10.5120/ijca2025926118
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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