Predictive Analytics for Customer Behavior

Albérico Travassos Rosário · Rui Manuel Nunes Cruz · Luís Bettencourt Moniz · Joaquim A. Casaca

7 citations0/0 files storedAdvances in marketing, customer relationship management, and e-services book series

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Customer churn and segmentation Big Data and Business Intelligence AI in Service Interactions

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.

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2009Software survey: VOSviewer, a computer program for bibliometric mapping 2019Conducting systematic literature reviews and bibliometric analyses 2019A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector 2016Understanding Thematic Analysis and its Pitfall 2023How has data-driven marketing evolved: Challenges and opportunities with emerging technologies 2020Churn Prediction in Telecommunication using Logistic Regression and Logit Boost 2018A big data analytics model for customer churn prediction in the retiree segment 2020Dynamic behavior based churn prediction in mobile telecom

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