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Developing customer attrition management system: discovering action rules for making recommendations to retain customers

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Abstract

Customer churn, a major concern for most of the companies, leads to higher customer acquisition cost, lower volume of service consumption and reduced product purchase. Thus, it is critical for companies to take effective strategies to reduce customer outflow. In this paper, we aim to discover high quality action rules and provide valid and trustworthy recommendations to improve customer churn rate. We propose a Semantic-aided Customer Attrition Management System (SaCAMS), in which we use reducts for feature engineering, apply hierarchical clustering to build the semantic similarity relationship among clients, run action rule mining to discover the actionable patterns, and extract meta-actions to get the final recommendations. The experimental results show that SaCAMS can discover high quality action rules. Moreover, based on the improved action rules, SaCAMS can extract effective meta-actions to generate recommendations. Last but not least, SaCAMS utilizes meta-node to provide decision-makers with valid and trustworthy strategies, which are quantified by effectiveness scores.

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Authors and Affiliations

  1. Computer Science Department, University of North Carolina, Charlotte, 9201 University City Blvd, Charlotte, 28223, NC, USA

    Yuehua Duan & Zbigniew W. Ras

  2. Institute of Computer Science, Polish-Japanese Academy of Information Technology, Koszykowa 86, Warszawa, 02-008, Poland

    Zbigniew W. Ras

Authors
  1. Yuehua Duan
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  2. Zbigniew W. Ras
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Correspondence to Yuehua Duan.

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Zbigniew W. Ras contributed equally to this work.

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Duan, Y., Ras, Z.W. Developing customer attrition management system: discovering action rules for making recommendations to retain customers. Appl Intell 53, 10485–10499 (2023). https://doi.org/10.1007/s10489-022-03614-0

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