Journal of Electronic Commerce

Journal of Electronic Commerce

Real-Time Modeling of Customer Journey in Multichannel Platforms

Document Type : Original Article

Authors
Akari Tanaka 1
Kenji Sato 2
Yui Nakamura 3
1 Assistant Professor of Marketing Science, Graduate School of Economics, The University of Tokyo, Japan
2 Professor of Data Science and AI, Graduate School of Informatics, Kyoto University, Japan
3 Senior Researcher in Human-Computer Interaction, Faculty of Science and Technology, Keio University, Japan
joecm.3.2.15564.35125656565004
Abstract
The contemporary business landscape is characterized by complex, non-linear customer journeys across a multitude of online and offline channels. Traditional, retrospective approaches to customer journey analysis are inherently limited in capturing the dynamism, fluidity, and real-time shifts in consumer behavior, leading to missed opportunities for personalized engagement and proactive intervention. This study addresses this critical gap by proposing and conceptually validating a novel Real-Time Customer Journey Modeling Framework designed to understand, predict, and optimize customer interactions across multichannel platforms instantaneously.
Adopting a Design Science Research (DSR) methodology, the research involved the iterative conceptualization, design, and demonstration of a multi-layered analytical artifact. The framework's architecture comprises four integrated components: (1) a Dynamic Data Ingestion Layer for seamless, low-latency capture of heterogeneous event streams from diverse touchpoints; (2) an Intelligent Real-Time Feature Engineering and Profile Enrichment Layer that transforms raw data into actionable features and dynamically updates a unified customer profile; (3) a Predictive and Prescriptive Analytics Engine utilizing advanced machine learning (e.g., LSTMs, Transformers) for real-time journey state recognition and intent prediction, alongside reinforcement learning for optimal next-best action recommendations; and (4) a Seamless Action Orchestration and Feedback Loop for automated, cross-channel intervention triggering and continuous model optimization.
Conceptual demonstrations through simulated scenarios provided strong evidence for the framework's capabilities, showcasing its real-time responsiveness (e.g., sub-second latency in triggering personalized actions) and its ability to maintain a unified, continuously evolving view of the customer journey. Evaluation of the artifact's design confirmed its technical feasibility, highlighting its scalability, robustness, and integration capabilities based on established big data and machine learning technologies. Furthermore, the framework's inherent design strongly suggests significant business utility, promising enhanced customer experience through proactive and personalized interactions, optimized marketing effectiveness through precise interventions, improved operational efficiency, and a sustained competitive advantage in the digital marketplace.
This research contributes theoretically to customer journey theory, digital transformation, and adaptive marketing by offering a concrete framework for real-time, prescriptive customer engagement. Practically, it provides a crucial blueprint for businesses to strategically invest in infrastructure and analytics, enabling them to truly master the complexities of multichannel customer interactions in the fast-paced digital era.
Keywords
Real-Time
Customer Journey
Multichannel Platforms
Predictive Analytics
Prescriptive Analytics
Machine Learning
Reinforcement Learning
Digital Transformation
Customer Experience
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