Multi-Stage Data-Driven Framework for Customer Journey Optimization and Operational Resilience

Tzu-Chien Wang (), Ruey-Shan Guo, Chialin Chen and Chia-Kai Li
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Tzu-Chien Wang: Department of Computer Science and Information Management, Soochow University, No. 56, Sec. 1, Guiyang St., Zhongzheng Dist., Taipei City 100, Taiwan
Ruey-Shan Guo: Department of Business Administration, National Taiwan University, Taipei City 106, Taiwan
Chialin Chen: Department of Business Administration, National Taiwan University, Taipei City 106, Taiwan
Chia-Kai Li: Graduate Institute of Industrial Engineering, National Taiwan University, Taipei City 106, Taiwan

Mathematics, 2025, vol. 13, issue 7, 1-33

Abstract: Optimizing customer journeys is a critical challenge in e-commerce and financial services, attracting attention from marketing, operations research, and business analytics. Traditional customer analytics models, such as rule-based segmentation and regression models, rely heavily on structured transactional data, limiting their ability to capture latent behavioral patterns and adapt to multi-channel dynamics. These models often struggle to integrate unstructured data sources, failing to provide adaptive, personalized insights. To address these limitations, this study proposes a multi-stage data-driven framework integrating latent Dirichlet allocation (LDA) for behavioral insights, deep learning for predictive modeling, and heuristic algorithms for adaptive decision-making. Empirical validation using Taiwanese financial institution data shows a 15% improvement in predictive accuracy compared to traditional machine-learning models, significantly enhancing customer lifetime value (CLV) predictions and multi-channel resource allocation. This research highlights the practical value of integrating structured and unstructured data for improving customer analytics. Our framework leverages LDA to extract behavioral patterns from customer interactions, enriching predictive models and enhancing real-time decision-making in financial services. Robustness checks confirm the scalability and adaptability of this approach, offering a data-driven strategy for long-term value optimization in dynamic digital ecosystems.

Keywords: data-driven predictive models; customer journey optimization; multi-channel marketing; deep learning; heuristic optimization; knowledge systems (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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