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<p>Customer Relationship Management (CRM) systems are at the heart of how modern businesses connect with their customers. But despite all the data available, most CRM systems still rely on rigid segmentation rules and look backward rather than forward. In this work, we introduce a new hybrid approach to CRM personalization—one that brings together the best of rule-based business logic and machine learning (ML) predictions. The goal: deliver smarter, more personalized Next Best Action (NBA) recommendations that adapt to each customer’s context. Our framework is built on four core layers: collecting and engineering useful data, validating decisions with clear business rules, generating predictions with multiple ML models, and orchestrating final decisions that balance statistical insights with real-world business needs. Using real CRM data from a large, multi-property service company, we show how this system can predict things like customer engagement, estimate value, and group similar behaviors—then turn those insights into concrete tasks for relationship managers. In testing, our hybrid approach beat both the traditional rule-only system on accuracy (AUC-ROC 0.82 vs. 0.65) and the ML-only configuration (AUC-ROC 0.80), demonstrating that the hybrid adds value beyond either component alone. The system acted much faster (cutting signal-to-action time by 75%) and improved customer engagement by 14% over a matched control group receiving conventional rule-based recommendations, all while ensuring every recommendation complies with business eligibility rules, contact frequency limits, and capacity constraints before reaching frontline staff.</p>

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A framework for hybrid CRM personalization: combining rule-based logic with machine learning predictions

  • 期刊论文

  • 发表日期：

    2026年5月6日

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  • Krishna Chaithanya Vuppala,
  • Nithin Maruthi Prasad

摘要

Customer Relationship Management (CRM) systems are at the heart of how modern businesses connect with their customers. But despite all the data available, most CRM systems still rely on rigid segmentation rules and look backward rather than forward. In this work, we introduce a new hybrid approach to CRM personalization—one that brings together the best of rule-based business logic and machine learning (ML) predictions. The goal: deliver smarter, more personalized Next Best Action (NBA) recommendations that adapt to each customer’s context. Our framework is built on four core layers: collecting and engineering useful data, validating decisions with clear business rules, generating predictions with multiple ML models, and orchestrating final decisions that balance statistical insights with real-world business needs. Using real CRM data from a large, multi-property service company, we show how this system can predict things like customer engagement, estimate value, and group similar behaviors—then turn those insights into concrete tasks for relationship managers. In testing, our hybrid approach beat both the traditional rule-only system on accuracy (AUC-ROC 0.82 vs. 0.65) and the ML-only configuration (AUC-ROC 0.80), demonstrating that the hybrid adds value beyond either component alone. The system acted much faster (cutting signal-to-action time by 75%) and improved customer engagement by 14% over a matched control group receiving conventional rule-based recommendations, all while ensuring every recommendation complies with business eligibility rules, contact frequency limits, and capacity constraints before reaching frontline staff.