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Deep Learning-Based Classification of Customers Towards Online Purchase Behaviour: A Recent Empirical Study for Home Appliances

Author

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  • Juin Ghosh Sarkar

    (MCKV Institute of Engineering, India)

  • Tuhin Mukherjee

    (Department of Business Administration, University of Kalyani, India)

  • Isita Lahiri

    (University of Kalyani, India)

Abstract

Online shopping is the new trend and is quickly becoming an integral part of our lifestyle. Due to the internet revolution and massive e-commerce usage by traders, online shopping has seen mammoth growth in recent years. In today's intensely competitive and dynamic environment with technological innovation in every sphere, knowing the consumer mind is the most daunting task for the success of any business. In this backdrop, the researchers have developed a neural network model. They have also made an attempt to classify the customers into two disjoint classes that are interested and uninterested online customers regarding purchase of home appliances through internet in and around Kolkata based on five demographic attributes, namely age, gender, place of residence, occupation, and income. The paper also focuses to optimise the parameters of the proposed neural network and test the efficiency of the constructed model and compare the result by reviewing the existing literatures on the related topic.

Suggested Citation

  • Juin Ghosh Sarkar & Tuhin Mukherjee & Isita Lahiri, 2020. "Deep Learning-Based Classification of Customers Towards Online Purchase Behaviour: A Recent Empirical Study for Home Appliances," International Journal of Online Marketing (IJOM), IGI Global, vol. 10(4), pages 74-86, October.
    • Handle: RePEc:igg:jom000:v:10:y:2020:i:4:p:74-86
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