Customer retention management: A complementary use of data mining and soft systems methodology

Abstract

BACKGROUND: Customer retention and management of customer churn are deemed as among the most significant issues for businesses. Given the fact that customer churn is not typically predictable easily, identifying and analyzing customer churn is necessary for businesses. OBJECTIVE: Therefore, the current research was conducted to employ a complementary approach to identify the reasons influencing customer churn. METHODS: To do so, initially, customers’ data were clustered by recruiting the K-means method. Each cluster represented customers who held similar values and the probability of churn behavior. In the next step, stakeholder groups are identified based on the K-means algorithm. Then, Soft Systems Methodology (SSM) was employed to encapsulate each of the identified interested groups’ world-view to better understand logical reasons for churned customers. Purposeful activity modeling (human activity system) was adopted for each interested group utilizing SSM techniques. RESULTS: Using SSM techniques, purposeful activity modeling (human activity system) for each interested group adopted. Utilizing human activity systems for structuring debate sessions about change actions, short-term and long-term plans have been proposed to maintain and improve customer retention programs. CONCLUSIONS: SSM can be considered as an overarching approach that can afford a better understanding of the processes derived from data mining.