Advanced data mining techniques for customer behavior analysis and segmentation in retail banking
Posted: May 29, 2024
Abstract
The retail banking sector faces unprecedented challenges in understanding and responding to rapidly evolving customer behaviors in an increasingly digital financial landscape. Traditional customer segmentation approaches, predominantly based on demographic characteristics and basic transactional patterns, have proven inadequate for capturing the complex, multi-faceted nature of modern banking relationships. This research addresses these limitations through the development of an innovative data mining framework that integrates quantum-inspired clustering principles with advanced temporal pattern analysis. The primary research questions guiding this investigation are: How can quantum computing principles be adapted to enhance customer segmentation in retail banking? What novel customer archetypes emerge when analyzing banking behaviors through multi-dimensional, temporal lenses? To what extent do overlapping segment memberships reflect the true complexity of customer banking relationships?
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