Leveraging neural networks technique for predicting impulsive buying: an empirical study in India
by Sanjeev Prashar; Chandan Parsad; Tata Sai Vijay
International Journal of Manufacturing Technology and Management (IJMTM), Vol. 31, No. 6, 2017

Abstract: It is a common belief that product assessments and buying decisions are made inside retail outlets, which are influenced by various antecedent factors related to retail outlets as well as individual personality traits. To attract the attention of purchasers, retailers invest huge amount on promotions and ambient factors. However, retailers must focus on something more substantive that match shopper personality. If managers are aware of their consumers' personality traits, they can extend substantive benefits that enhance shopper experience. This research identifies significant variables that influence impulse buying. These variables will help the retailers in stimulating shoppers' unplanned buying. This would help them reduce the risk of either stockpile or stock out conditions. Despite the development of various forecasting techniques, predicting impulse buying has remained under-explored. This paper addresses this gap by using neural network model to predict such purchasing behaviour. The findings of the study offer a number of implications for retailers.

Online publication date: Fri, 05-Jan-2018

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