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Advances in Predictive Customer Journey Analytics for Cross-Sell and Upsell in Financial Services

Chifum Ann Ukadike, Adaobi Vivian Ibeh

Abstract

Predictive customer journey analytics has become central to how financial institutions identify, time, and personalize cross-sell and upsell opportunities. This paper synthesizes advances through 2023 in the modeling techniques, data architectures, and organizational practices that enable institutions to anticipate customer needs across multichannel journeys. It frames the customer journey as a sequence of observable touchpoints and latent intentions, and reviews how propensity modeling, sequence-aware machine learning, uplift modeling, and next-best-action systems have matured from static segment-level scoring toward dynamic, individualized recommendation. The analysis organizes the field around four advances: the shift from outcome prediction to causal and uplift estimation; the adoption of sequence and representation learning for journey data; the move toward real-time and event-driven scoring architectures; and the integration of explainability and fairness controls demanded by external review. Drawing on literature from marketing science, machine learning, and financial services, the discussion identifies recurring tensions between predictive accuracy and interpretability, between personalization and privacy, and between short- term conversion and long-term customer value. It argues that the most defensible deployments treat journey analytics as a governed decision system rather than a single model, embedding measurement, monitoring, and accountability throughout. The paper concludes with a consolidated framework and a research agenda emphasizing causal rigor, longitudinal value measurement, and regulatory alignment. The synthesis is intended to support both researchers and practitioners seeking a rigorous, verifiable account of the state of the field.

Keywords

customer journey analyticspredictive modelingcross-sellupsellfinancial servicesuplift modelingnext-best-actionpersonalization

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