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 (MCKV Institute of Engineering, India), Tuhin Mukherjee (Department of Business Administration, University of Kalyani, India) and Isita Lahiri (University of Kalyani, India)
Copyright: © 2020 |Pages: 13
DOI: 10.4018/IJOM.2020100105
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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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The internet revolution during the last two decades totally changed the shopping outlook of the consumers to buy goods and services which has abruptly evolved into a global phenomenon as per Saha (2015). To keep up with the emerging trend, the consumer’s behaviour towards the purchase of Home Appliances has undergone through a sea change according to Lahiri and Ghosh (2018). In this evolutionary phase, various companies have started using the Internet with the aim of curtailing marketing costs, thereby reducing the price of their products and services in order to have an edge in this highly competitive market according to Gupta and Jain (2017). Now a days, the online sellers use the Internet to convey, communicate and disseminate information, to sell the product, to take feedback and also to conduct satisfactory surveys with customers. The customers on the other hand uses the Internet not only to buy the product online, but also to compare prices, product features and after sale service facilities they would receive if they purchase the product from a particular store as observed by Kinker and Shukla (2016). Since, this is a win-win situation for both the ends that is the sellers and the buyers, so many experts are optimistic about the prospect of online marketing.

This global revolution of online marketing is also reflected in case of online purchase of Home Appliances. Lahiri and Ghosh (2018) stated Home Appliances are actually the electrical or mechanical acoustics which are mainly used to perform the household functions like cooling, cooking, heating or cleaning. Home Appliances are broadly classified into Major Appliances (White Goods), Small Appliances and Consumer Electronics (Brown Goods).

In India also, the internet and mobile penetration has created an immense effect over the last two decades in the field of e-commerce.

This paper mainly highlights to classify the customers into two disjoint classes- the customers who are more likely to purchase home appliances through internet and vice versa using Neural Network Model based on five demographic profiles namely- age, gender, place of residence, occupation and income. To conduct the research work, Kolkata and outskirts is chosen since Kolkata is a metropolitan city with numerous uninterrupted facilities like transport facility, potential market, huge number of shopping malls to confuse the potential customers, etc. But in spite of all these facilities why the consumers of Kolkata and outskirts are getting addicted to online shopping was the main reason to zero down on the abovementioned area to conduct research. Awan and Abbas (2015) stated these five demographic factors namely- age, gender, place of residence, occupation and income make a huge impact on the impulsive buying behaviour of the consumers, as a result of which these five factors are chosen as the basis of classification of the online consumers of Home Appliances.

A neural network is actually a series of algorithms that attempts to recognise elementary relationships in a set of data through a process that impersonates the human brain as observed by Binshan and Bruwer (1995). Above all this process can adapt to changes in the input as a result of which the network can generate the best possible outcome without redesigning the output criteria.

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