Recommender Systems for Personalized Offers

Abstract

The development of information and communication technologies has forced companies to keep pace with technological innovations. This is referred to as digitalization, which leads to significant changes in companies' processes. It reshapes business environments, reconfigures business models and strategies, and enhances overall business performance and competitiveness. Compared to small and medium-sized enterprises, large companies tend to adopt digitization and digital transformation more quickly. However, their long-term survival in the market requires that small and medium-sized enterprises engage in digitization and digital transformation processes, as well. Digitalization is strongly linked to user experience, while personalization is the key to improved experience and satisfaction. It also influences sales and improves competitive position, while targeted advertising contributes to the optimization of the company's budget through more effective marketing campaigns that maximize return on investment. Concerning said, one of the challenges that small and medium-sized companies face is related to the application of information and communication technologies in the personalization of the product/service offer and targeted advertisement. As a limiting factor, the authors recognize insufficient knowledge and understanding of the possibilities and limitations of digital technologies, particularly artificial intelligence (AI), for improving the personalization of the offer and targeting customers by their preferences, habits, and aspirations. AI-driven product recommendation systems are key to improving customer experience and sales growth. To generate personalized recommendations that will align with individual preferences or behavior, recommendation systems utilize advanced machine learning algorithms and natural language processing techniques and process large amounts of customer and product-related data, both structured and unstructured. The paper aims to contribute to understanding the possibilities and benefits of applying AI-based recommendation systems in customer offer personalization through a review of current literature, with a closer look at the types of recommender systems and the possibilities and limitations of their functioning or implementation.