Empirical study on understanding online buying behaviour through machine learning algorithms

Article type: Research Article

Authors: Mukherjee, Sayantan^{a; *} | Jason, A. Prince^a | Selvakumar, Angeline^b

Affiliations: [a] V.I.T. Bhopal University, Bhopal, India | [b] College of The Rockies, Canada

Correspondence: [*] Corresponding author: Sayantan Mukherjee, V.I.T. Bhopal University, Bhopal, India. Tel.: +91 8961984401; E-mail: sayantan.mukherjee2020@vitbhopal.ac.in.

Abstract: The research study tries to understand teenagers’ online engagement and the behavioral transformation in buying stuff online. The study also tries to ideate the stability of spike in online buying (if any) and its sustainability. Statistical tools like the K-S test, M.L.R. test, Pearson Correlation has been used to justify the study and the usage of machine learning algorithms to construct a predictive model of behaviour and its efficiency. The study will help online retailers understand their sales figures’ stability. It will allow them to strategize their marketing functionalities to make the space more attractive even after the world comes out of the pandemic. The increasing usage of intelligent android devices and relatively cheap data has surged the penetration of online engagements among all the age group peoples. The youngsters are engaging in online stuff hence bringing down a considerable transformation in buying behaviour, pattern, and a collective change in marketers’ approach to strategizing according to the ever-evolving market forces.

Keywords: Consumer data analysis, COVID-19, online buying behaviour, machine learning algorithm, teenager consumer behaviour

DOI: 10.3233/MAS-220008

Journal: Model Assisted Statistics and Applications, vol. 17, no. 1, pp. 59-68, 2022