Artificial neural network to diagnose the consumer behaviour towards non-fuel products and services at filling stations
by Gautam Srivastava; Surajit Bag
International Journal of Technology Marketing (IJTMKT), Vol. 15, No. 2/3, 2021

Abstract: The petroleum companies are transforming their business model from fuel retailing to non-fuel retailing to increase their revenues. However, predicting the complex buying behaviour of the consumer is a major challenge faced by petroleum retailers. Therefore, there is a need for research to develop models, which can predict consumer behaviour pertaining to non-fuel retailing at filling stations. The study intended to predict the buying behaviour of the consumers of non-fuel products and services at filling stations. The study proposed a predictor model by using an artificial neural network (ANN). The literature reveals that an artificial neural network is a better predictor than traditional predictors, such as logistic regression and discriminant analysis. This research can help develop the supervised machine learning model and further classify the consumers visiting the filling stations. This model can analyse consumer behaviour with an automation system, which reduces the cost of marketing with more accurate results. This paper extends the applications of ANN in the domain of marketing and the precise analysis of the consumer behaviour.

Online publication date: Fri, 15-Oct-2021

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