Graph Machine Learning to Map and Predict Customer Journeys and Churn

Understanding customer behavior and predicting customer churn remain vital and central strategic objectives for modern digital organizations. Customer interactions are becoming multi-touch, they are cross-channel, and dynamically interconnected. The traditional linear modeling approach does not meet these demands. The traditional model does not capture the complexity that is vital in digital customer journeys. These challenges should be addressed, and graph machine learning (GraphML) and graph neural networks (GNNs) have become effective frameworks for addressing these challenges. The frameworks are capable of modeling the behavior of the customers as a network of entities, transitions, and interactions. The paper explores how graph machine learning can be applied to map the journeys of the customer and predict churn with higher accuracy, as well as deeper behavioral insight than conventional models. This paper will draw insights from peer-reviewed sources. It develops a conceptual, training graph model to detect churn signals, a multi-layered framework for constructing customer journey graphs, and operationalizing predictions into retention strategies that are actionable. The findings depict that graph-based modeling captures network dependencies. It captures cross-channel transitions and the behavior patterns that traditional machine learning does not incorporate. The findings indicate that GraphML offers a better method for describing customers as interconnected nodes within a relational structure. It enables more accurate churn prediction and a comprehensive mapping of the real-world customer journeys. The paper will conclude with a managerial implication, suggestions for future research, and limitations.