Decoding the DNA of Customer Relationships: the Role of Marketing Diagnostics in the Digital Age

Abstract

As businesses increasingly recognize the importance of effective customer relationship management, building a rich database and managing it sustainably becomes a crucial factor for success. Digital transformation has altered consumer behavior, necessitating the transformation of strategic frameworks for managing customer relationships and precise management of customer data. To implement effective marketing approaches, it is necessary to conduct a CRM system diagnostics that identifies critical customer interaction areas through adequate metrics. This paper examines the main aspects of marketing diagnostics and their contribution to customer relationship management. The research focuses on the nature and development of three main diagnostic tools as fundamental to the diagnostic process in customer relationship management: the RFM-method, the customer lifetime value (CLTV) and the Customer churn prediction model. The possibilities of using machine learning and artificial intelligence in marketing diagnostics and their impact on customer relationship management are discussed. The purpose of the present study is to contribute to a better understanding of the importance of marketing diagnostics for customer relationship management and to highlight the importance of the effective application of diagnostic tools in organizations.