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

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

Juin Ghosh Sarkar, Tuhin Mukherjee, Isita Lahiri
Copyright: © 2020 |Volume: 10 |Issue: 4 |Pages: 13
ISSN: 2156-1753|EISSN: 2156-1745|EISBN13: 9781799807728|DOI: 10.4018/IJOM.2020100105
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MLA

Sarkar, Juin Ghosh, et al. "Deep Learning-Based Classification of Customers Towards Online Purchase Behaviour: A Recent Empirical Study for Home Appliances." IJOM vol.10, no.4 2020: pp.74-86. http://doi.org/10.4018/IJOM.2020100105

APA

Sarkar, J. G., Mukherjee, T., & Lahiri, I. (2020). Deep Learning-Based Classification of Customers Towards Online Purchase Behaviour: A Recent Empirical Study for Home Appliances. International Journal of Online Marketing (IJOM), 10(4), 74-86. http://doi.org/10.4018/IJOM.2020100105

Chicago

Sarkar, Juin Ghosh, Tuhin Mukherjee, and Isita Lahiri. "Deep Learning-Based Classification of Customers Towards Online Purchase Behaviour: A Recent Empirical Study for Home Appliances," International Journal of Online Marketing (IJOM) 10, no.4: 74-86. http://doi.org/10.4018/IJOM.2020100105

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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.

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