The customer lifetime value forecasting model in the process of optimizing the bank's customer paths using Process Mining technology

The purpose of this article is to develop an approach to managing Customer Lifetime Value (CLTV) in banking through the analysis of customer journeys. The research method is based on the application of Process Mining technology, which enables the identification of bottlenecks in customer–bank interactions and the assessment of their impact on financial performance. An economic model for forecasting CLTV has been developed, comprising three key components: cost reduction, revenue growth, and a decrease in customer churn probability. The results of an experiment using the customer journey of consumer loan origination demonstrated the significance of the proposed approach, confirming its effectiveness in improving the accuracy of CLTV estimation. The main conclusion of the study is that applying Process Mining technology to customer journeys not only enhances the quality of customer experience but also substantiates the direct impact of improvements on financial outcomes, specifically by reducing process maintenance costs.

For citation: Cherskikh D.A., Bogatyrev S.Yu. The customer lifetime value forecasting model in the process of optimizing the bank's customer paths using Process Mining technology // Uchenye zapiski Mezhdunarodnogo bankovskogo instituta [Proceedings of the International Banking Institute]. 2026. 1 (55). pp. 244-263 (in Russ.). EDN: ZENFSS.

Funding: This research received no external funding.