Data mining research for customer relationship management systems: a framework and analysis

TLDR

Data mining enables businesses to predict trends and behaviors, supporting proactive decisions, and CRM systems are adopted to leverage these predictions for strategic success. This paper focuses on areas needing further exploration and proposes a framework for analyzing data mining research in CRM systems. The study applies data mining techniques to customer data warehouses to uncover hidden patterns and identifies three key research areas: CRM implementation, evaluation criteria for data mining software and CRM systems, and data quality improvement methods. The authors show that discovered patterns enhance customer understanding and acquisition, and they propose an integrated model for evaluating and implementing CRM systems.

Abstract

Data mining is a new technology that helps businesses to predict future trends and behaviours, allowing them to make proactive, knowledge-driven decisions. When data mining tools and techniques are applied on the data warehouse based on customer records, they search for the hidden patterns and trends. These can be further used to improve customer understanding and acquisition. Customer Relationship Management (CRM) systems are adopted by the organisations in order to achieve success in the business and also to formulate business strategies, which can be formulated based on the predictions given by the data mining tools. Basically three major areas of data mining research are identified: implementation of CRM systems, evaluation criteria for data mining software and CRM systems and methods to improve data quality for data mining. The paper is concluded with a proposed integrated model for the CRM systems evaluation and implementation. This paper focuses on these areas, where there is need for more explorations, and will provide a framework for analysis of the data mining research for CRM systems.