A machine-learning approach for classifying Indian internet shoppers
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.
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Author's Biography
Ritanjali Majhi is an Associate Professor at the National Institute of Technology Karnataka School of Management. She is an expert in the fields of Big Data analysis, consumer decision making, time-series prediction, marketing analytics, artificial intelligence and machine-learning applications to management science. Dr Majhi’s research has been published in numerous international journals and been presented at various conferences.
Renu Prasad Sugasi is a Data Analytics and Business Consultant at Thorogood Associates. He has a B.tech degree in mechanical engineering from the National Institute of Technology Karnataka, and his professional interests include data science and analytics, deep learning and machine-learning applications.
Citation
Majhi, Ritanjali and Sugasi, Renu Prasad (2022, February 1). A machine-learning approach for classifying Indian internet shoppers. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 7, Issue 3. https://doi.org/10.69554/NQQL2875.
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