Forecasting Buying Intention Through Artificial Neural Network: An Algorithmic Solution on Direct-to-Consumer Brands

Abstract

The direct-to-consumer (DTC) brands are emerging to reach more number of consumers with more potential to meet their expectations. They are characterized through their metamorphosis as the vertical brands sell their products from the manufacturer to consumers directly without any interruptions from distribution channels as in traditional mode of doing business. They are annihilating themselves in the virtual platforms and later disrupting their existing linear sales models. This empirical investigation is targeted to construct an algorithmic model through a deep learning process which has been instrumental to predict the purchase decision. This investigation has churned a predictive model that is based on the attributes of the buying behaviour of the consumers. The attributes of online buying behaviour like safety of transaction, availability of innovative products and quality of products have been considered to build a predictive model through artificial neural network (ANN). The accuracy of training and testing data are closer, which infers about the consistency and validity of the predictive model. There are several consequences arising from the predictive model obtained that can be seeded from customer-centred marketing and further stemmed from the framing of business strategy, gaining insights into market architecture and choice of customer