Measuring the impact of data mining on churn management

Miguel A. Lejeune

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Customer churn and segmentation Customer Service Quality and Loyalty Big Data and Business Intelligence

Abstract

Churn management is a fundamental concern for businesses and the emergence of the digital economy has made the problem even more acute. Companies’ initiatives to handle churn and customers’ profitability issues have been directed to more customer‐oriented strategies. In this paper, we present a customer relationship management framework based on the integration of the electronic channel. This framework is constituted of four tools that should provide an appropriate collection, treatment and analysis of data. From this perspective, we pay special attention to some of the latest data mining developments which, we believe, are destined to play a central role in churn management. Relying on sensitivity analysis, we propose an analysis framework able to prefigure the possible impact induced by the ongoing data mining enhancements on churn management and on the decision‐making process.

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References

1994Customer Loyalty: Toward an Integrated Conceptual Framework 2000Measuring the Customer Experience in Online Environments: A Structural Modeling Approach 1995Why Satisfied Customers Defect 1999Attribute-Level Performance, Satisfaction, and Behavioral Intentions over Time: A Consumption-System Approach 2000Customer Relationship Management (CRM) in financial services 2000Mastering Data Mining: The Art and Science of Customer Relationship Management 2000Expanding to the Internet: Pricing and Communications Strategies When Firms Compete on Multiple Channels

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