The advent of intelligent platforms has transformed commerce, yet existing customer journey modeling remains inadequate for understanding the self-directed customer navigating complex multichannel journeys. These customers actively orchestrate their paths across diverse touchpoints, displaying non-linear behaviors and dynamic shifts in intent that traditional, often retrospective and linear, models fail to capture. This deficiency leads to fragmented customer experiences, suboptimal marketing investments, and missed opportunities for meaningful engagement. This study addresses this critical gap by employing a Design Science Research (DSR) paradigm to develop a novel, comprehensive conceptual framework for modeling the multichannel journey of self-directed customers within intelligent platforms.
The developed artifact is a multi-layered framework designed to handle the velocity, volume, and variety of modern customer data. Its components include: a Data Ingestion and Unification Layer for real-time, comprehensive data stitching; a Dynamic Feature Engineering Layer for extracting both explicit and implicit signals of customer intent; a sophisticated Journey Modeling and Intent Inference Layer leveraging advanced sequential deep learning (LSTMs, Transformers) and graph neural networks (GNNs) to capture non-linearity and infer real-time intent; and a Prescriptive Recommendation and Orchestration Layer utilizing Reinforcement Learning (RL) and Contextual Bandits to generate optimal, non-intrusive "next-best actions" across channels. An overarching Continuous Learning and Feedback Layer ensures the framework's adaptability.
Conceptual scenario simulations demonstrating high-involvement consumer purchases and complex B2B solutions affirmed the framework's utility in overcoming linearity bias, interpreting subtle behavioral signals, and providing real-time, context-aware guidance that respects customer agency. Evaluation criteria confirmed its completeness, internal consistency, strong theoretical grounding, and conceptual feasibility. This research significantly advances customer journey theory by providing a prescriptive model for understanding autonomous customer navigation and enriches the application of AI/ML in customer relationship management. It offers a critical blueprint for businesses to enhance customer experience, optimize strategic interventions, and gain competitive advantage by truly understanding and supporting the self-directed customer in intelligent platform environments.