A Big Data Clustering Algorithm for Mitigating the Risk of Customer Churn

Abstract

As market competition intensifies, customer churn management is increasingly becoming an important means of competitive advantage for companies. However, when dealing with big data in the industry, existing churn prediction models cannot work very well. In addition, decision makers are always faced with imprecise operations management. In response to these difficulties, a new clustering algorithm called semantic-driven subtractive clustering method (SDSCM) is proposed. Experimental results indicate that SDSCM has stronger clustering semantic strength than subtractive clustering method (SCM) and fuzzy c-means (FCM). Then, a parallel SDSCM algorithm is implemented through a Hadoop MapReduce framework. In the case study, the proposed parallel SDSCM algorithm enjoys a fast running speed when compared with the other methods. Furthermore, we provide some marketing strategies in accordance with the clustering results and a simplified marketing activity is simulated to ensure profit maximization.