Análisis de clasificación por tipo de cliente de la empresa Labelprintsa S.A. en base a un modelo de clusterización por Machine Learning.

Abstract

Technology and innovation are fundamental for the growth and competitiveness of companies today, where process automation through data analysis is a key tool to improve efficiency and the quality of service offered to customers. However, the management and understanding of artificial intelligence concepts, such as machine learning, remains a challenge due to the complexity of these technologies and practical factors such as costs, personnel, training, and business stance. Despite these challenges, it is essential for companies to adapt to the digitization of processes to compete in global markets. Customer classification and segmentation are fundamental techniques for companies, as they allow them to adapt marketing strategies and campaigns in a more personalized way to different types of target customers, and to establish efficient strategies to achieve a competitive advantage in the market. This research focuses on the development of a customer classification model using Machine Learning (ML) and clustering for the company Labelprintsa. The objective is to establish personalized strategies for different customer groups due to the increasing demand for printing methods and the growth of the company. The application of ML is justified due to the growth of the company and the need to handle a large amount of data to apply customer retention or acquisition strategies. The proposed algorithm is k means neighbours, which is expected to achieve a minimum error in the prediction of customer group classification and a possible increase in market share.