Forecasting Buying Intention Through Artificial Neural Network: An Algorithmic Solution on Direct-to-Consumer Brands
From: FIIB Business Review
An increasing number of firms including E-commerce
organizations have been adapting BDA in order to predict consumer behaviour
which will fructify to better mechanism to create, deliver and capture business
value, while strengthening IT infrastructure and delivering operational,
managerial, strategic and organizational benefits that can ultimately be
translated into a competitive advantage and better performance.
In other words, today’s business environment is
hypercompetitive and intricate with multiple economic factors which compete,
cooperate or even cooperate to control and analyse enormous volumes of data.
So far, most management scholars have researched
value creation by understanding the online buying behaviour of the consumers
through the deep study on their perception, attitudes, pricing decisions and
clustering based on their online buying behaviour. Accordingly, the extant
literature has focussed on several areas like online buying behaviour, service
quality of the online shopping portals, and digital marketing aspects of the
E-commerce organizations. However, very few of them have empirically addressed
the issue of segmenting the consumer on the basis of their attributes of online
buying behaviour through the Deep Learning methods and artificial neural
network (ANN) model. The top-performing firms use data more as compared to
the lowest-performing firms. This area is particularly important for direct-to-consumer
(DTC) brands operating in fast-changing and dynamic markets, as these firms
must apply the algorithm-based models of deep learning and ANN to maintain
their competitive edge.
Despite scattered anecdotal evidence about the
practical use of deep learning, big data analytics and the emerging literature
on big data, deep learning methods, it appears that there is a knowledge gap
spinning around how DTC brands are using deep learning methods to understand
their customers, which will further lead to an efficient business model.
The main objective of this study is to bridge the
aforementioned knowledge gap by extracting an ANN model based on the buying
attributes of the consumers of the DTC brands. As such, the study aims to
address the following research question ‘how a predictive model based on the
ANN can be extracted which will help to classify consumers based on their
buying attributes’. This question has been addressed by extracting an ANN
model. The classification of the consumers based on the ANN will help the DTC
brands to understand the buying attributes of consumers.