Deep Learning-Based Classification of Customers Towards Online Purchase Behaviour

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.

Can Perceived Ease of Use Improve M-Commerce Adoption?

“…Nowadays, online retailers are using the Internet to convey, communicate, and disseminate information, sell the product, receive feedback, and conduct satisfactory customer surveys. On the other hand, customers used the Internet to purchase the product online and compare prices, product features, and after-sales service facilities that they would receive if they purchased the product from a particular store (Sarkar, Mukherjee & Lahiri, 2020).…”