This study examines the evolution of customer segmentation from rule-based systems to multimodal AI architectures, emphasizing the necessity for transparent and context-aware CRM strategies. Despite the widespread adoption of RFM models, organizations often struggle to synthesize transactional metrics with multifaceted qualitative feedback, creating a numerical paradox that this research addresses by capturing the authentic voice of the customer within high-dimensional analytical spaces. This paper presents the Contextual-RFM (C-RFM) framework, an innovative multimodal architecture aimed at bridging the representational divergence between quantitative metrics and qualitative feedback.
The research operationalizes a comprehensive five-phase pipeline to ensure methodological transparency and replicability: (1) establishment of a statistical baseline, (2) narrative synthesis for tabular serialization, (3) multimodal vectorization utilizing advanced embeddings, (4) semantic analysis through Latent Dirichlet Allocation (LDA) and (5) implementation of the C-RFM Expert chatbot. This entire workflow is implemented using KNIME Analytics and Langflow, providing a robust and replicable process for both scholars and practitioners. A key distinction of the framework is its early-fusion approach. Unlike traditional late-fusion models that integrate data streams only at the final analysis stage, early-fusion retains critical cross-modal correlations during the initial processing phase, effectively addressing the semantic opacity common in traditional numerical-only datasets.
Empirical findings indicate that the C-RFM framework adeptly organizes linguistic complexity, evidenced by a significant improvement in structural validity when transitioning from purely transactional environments to topic-validated contexts. Moreover, this study introduces the Discrepancy Detection Rate (DDR) as a novel performance metric to quantify the misalignment between transactional behaviour and qualitative feedback, thereby identifying critical churn risks that conventional quantitative analyses often overlook.
The primary contribution of this research lies in the synergy achieved between human expertise and Artificial Intelligence. By deploying dynamic persona chatbots grounded in Retrieval-Augmented Generation (RAG), the framework facilitates a shift from opaque analytical outputs to actionable, customized recovery strategies. This glass-box methodology transforms intricate numerical complexities into auditable business narratives, empowering decision-makers to transcend statistical constructs and foster a deeper, dialogue-based understanding of consumers. The C-RFM framework represents a significant advancement in human-centred AI, promoting strategic marketing intelligence that is not only transparent but also finely tuned to the authentic voices of real-world consumer archetypes, thereby paving the way for the next generation of customer-centric strategies.