An Analysis of the Impact of a Marketing Communication Management Method on the Purchase Behavior of Durable Consumer Goods using Machine Learning

Abstract

Purchase behavior of the durable consumer goods sector is a dynamic interplay of intricate factors, requiring businesses to adopt innovative methodologies for accurate prediction. This study presents various methods for predicting customer purchasing behavior in the durable consumer goods industry by integrating cutting-edge machine learning algorithms and utilizing enormous datasets with demographic data along with past purchasing habits. A variety of models are explained in the literature section, such as ensemble approaches, decision trees, and neural networks. Machine learning algorithm is the main focus of this paper, which aims to improve forecasting model precision on the purchase behavior. By doing this, companies are able to foresee changes in customer preferences and act accordingly. Machine learning techniques may also be used to evaluate the effectiveness of rivals' marketing activities and uncover the expectations of their target audience. A kind of artificial intelligence called machine learning (ML) enables computers to analyze and understand data without explicit programming. Moreover, machine learning helps people solve difficulties effectively. The impact of children and teenagers on family decision-making has not been extensively studied. For every sub-decision area, the relative effect of the husband, wife, and each kid was measured independently. Serving as the voice of the customer is the ultimate purpose of consumer research, which has a broad reach and may give businesses relevant, trustworthy, legitimate, and up-to-date information on their target market. The goals of consumer market research are to recognize, comprehend, and evaluate consumers and their demands.