FST Graduate Research Topic

Research Topic: Intelligent Customer Relationship Management (CRM) System
Suggested By: Harris Wang
Last Update: September 28th, 2025

Constrained Object Hierarchies (COH) provides a neuroscience-inspired theoretical framework for modeling Artificial General Intelligence (AGI). As a flexible and robust foundation, COH supports the representation of general intelligent systems across diverse domains. Formally, a COH is defined as a 9-tuple: O = (C, A, M, N, E, I, T, G, D) Where: C: Components – sub-objects organized hierarchically A: Attributes – state variables describing object properties M: Methods – executable actions associated with components N: Neural Components – adaptive models for learning and inference E: Embedding Neural Component – semantic representation of the COH I: Identity Constraints – rules ensuring object integrity T: Trigger Constraints – event-condition-action rules for dynamic behavior G: Goal Constraints – optimization objectives guiding intelligent behavior D: Constraint Daemons – real-time monitors enforcing system constraints Based on the COH framework, the modelling process and result of Intelligent Customer Relationship Management (CRM) System are as follows: Description and Significance: A modern CRM system uses AI to analyze customer interactions, predict churn, recommend next-best actions, and personalize marketing. It represents the evolution of customer management from reactive tracking to proactive, intelligent engagement and is central to revenue growth and customer retention strategies. Analysis and Requirements: The system must unify customer data from multiple touchpoints, segment customers dynamically, predict customer lifecycle events (like churn or purchase readiness), and automate personalized engagement workflows while maintaining compliance with privacy regulations. Design of the COH Solution: The CRM is designed as a hierarchical system where high-level customer strategy constraints govern the behavior of sub-components responsible for data management, analytical processing, and customer engagement execution. COH Modelling Solution: C: {CustomerDataLake, SegmentationEngine, PredictiveAnalyticsModule, AutomatedWorkflowOrchestrator} A: {CustomerLifetimeValue, ChurnProbability, EngagementScore, SegmentAssignment, CampaignPerformance} M: {IngestCustomerData(), CalculatePropensityScores(), AssignToSegment(), TriggerPersonalizedCampaign()} N: Machine learning models for predicting churn, next-best-product, and customer lifetime value E: "Maximizing customer lifetime value through personalized, predictive engagement" I: DataPrivacyRules (e.g., GDPR) must not be violated; CustomerConsent must be respected T: ON (ChurnProbability > Threshold) IF (CustomerIsHighValue) THEN (TriggerRetentionWorkflow) G: Maximize Customer Lifetime Value; Minimize Customer Churn Rate D: Real-time monitors for data quality and compliance with privacy constraints The COH model effectively separates the intelligent, adaptive core (the neural components N) from the critical, non-negotiable business rules (I constraints like GDPR compliance). The trigger constraints (T) encode the business logic that turns customer insights into actionable interventions, making the system's "intelligence" explicit, auditable, and aligned with business objectives. You may choose this topic and pursue one of the following routes: 📝 Essay Survey existing methodologies for modeling, designing, and implementing similar intelligent systems. Include: Literature synthesis COH framework analysis Critical evaluation of competing approaches 🛠️ Project Design and implement the system using the COH model. Include: System architecture and design Development and testing Demonstration of core functionality 🎓 Thesis Explore COH’s theoretical foundations in depth. Include: Proposed enhancements or extensions Implementation and evaluation Development of an advanced intelligent system grounded in COH principles