A big data clustering algorithm for mitigating the risk of customer churn

1. 2020 – 2021
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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.