Transforming Consumer Behavior to New Paradigms Through Deep Learning Applications

Abstract

Direct-to-consumer (DTC) businesses are gaining popularity as a way to reach a larger number of customers and better suit their needs. Vertical brands are distinguished by their metamorphosis, in which they offer their products straight from the manufacturer to consumers without the use of distribution intermediaries, like in traditional business models. They're obliterating themselves on virtual platforms and undermining their old linear sales processes in the process. In the current scenario, the ability of connectionist models to explain consumer behavior, with a focus on the feed-forward neural network model, should be emphasized, and the possibility of expanding the implications of ANN (Artificial Neural Network) for predicting buying behavior for DTC (direct-to-consumer) brands should be explored. To forecast consumer loyalty as a critical feature of consumer behavior, a variety of neural network models of various complexity are constructed. When compared to the more standard logistic regression approach, neural networks outperform logistic regression in predicting customer loyalty. Utilitarian and Informational Reinforcement factors, both independently determined, are shown to contribute significantly to the explanation of consumer choice. The potential of connectionist models for predicting and explaining consumer behavior is discussed, and future research directions are proposed for investigating the predictive and explanatory capacity of connectionist models, such as neural network models, and their integration into consumer behavior analysis within the theoretical framework.