Improving B2B customer churn through action rule mining

Abstract

Business-to-business (B2B) firms must maintain robust customer bases to ensure recurring revenue. To do so effectively, they should engage in churn prediction. Proactively identifying potential churners and taking proactive retention measures help companies safeguard their revenue streams and build strong, long-lasting relationships with customers, which enhances their sustainability and competitive performance in dynamic, competitive markets. Yet, extant B2B customer churn models often fail to offer truly practical or actionable decision support, such that marketers must rely on their intuition and exert additional effort to define appropriate preventive retention measures. To address this research gap between research models and actionable insights, the current study proposes B2B-ARM, a B2B actionable rule model (ARM), that offers clear action paths for proactive retention management. A real-life case study of a European B2B software company with 6275 contracts provides benchmark evidence that B2B-ARM can detect churn equally well as popular existing prediction models (i.e., decision tree, logistic regression, and naïve Bayes). Furthermore, B2B-ARM provides actionable recommendations, as well as direct remedies to prevent churn, such that marketers save both time and resources. Overall, B2B-ARM is a reliable, efficient, and practical tool for mitigating B2B churn and improving customer retention.