Predictive Analytics for Customer Behavior

Abstract

Predictive analytics have become indispensable tools for implementing data-driven strategies and decisions. These innovations are used to predict future activities, thereby enabling businesses to plan accordingly. The main purpose of this study is, through a systematic bibliometric literature review, to understand how businesses apply predictive models to predicting consumer behavior. The review includes 74 articles published in the Scopus® database, presenting up-to-date knowledge on the topic. The bibliographic search included peer-reviewed articles published up to 2024. The research findings identified three major ways predictive analytics helps forecast customer behavior. These include predicting their purchasing patterns, churn, and engagement. While predictive analytics for customer behavior offers significant advantages, it also poses challenges related to data privacy, ethical considerations, and the need for sophisticated analytics skills.