A machine-learning approach for classifying Indian internet shoppers

Ritanjali Majhi and Renu Prasad Sugasi
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Ritanjali Majhi: School of Management, National Institute of Technology Karnataka, India
Renu Prasad Sugasi: Data Analytics and Business Consultant, Thorogood Associates, India

Applied Marketing Analytics: The Peer-Reviewed Journal, 2022, vol. 7, issue 3, 288-298

Abstract: This paper identifies the key factors that influence Indian consumers to shop online. The study uses data collected via questionnaire survey to segment consumers with shared behaviours into groups, with the results of this clustering used to train radial basis function neural networks, decision trees and random forest models. The performance of these classification models is then assessed and compared with the conventional statistical-based naïve Bayes method and logistic regression. The study finds that the random forest method provides the greatest accuracy for the segmentation of online consumers, followed by naïve Bayes and decision trees methods. The behavioural patterns identified in this study may be leveraged in real-world situations.

Keywords: classification; consumer behaviour; online shoppers; random forest; decision tree; RBFNN; logistic regression; naive Bayes model (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:aza:ama000:y:2022:v:7:i:3:p:288-298

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