Socio-Transactional Impact of Recency, Frequency, and Monetary Features oN Customers’ Behaviour in Telecoms’ Churn Prediction

Abstract

Due to the increasing competitiveness in telecom’s market, it has now become more necessary for operators to start building personal relationship with customers for targeted retention strategies. Achieving this goal requires the development of an effective churn prediction model that will solve the problem of churn misclassification, which is persistent in current churn prediction models. With several existing segment-oriented churn prediction models failing to harness the power of associative networking provided by telecoms users, churn prediction accuracy remains unguaranteed while targeted decision support is not enhanced. Here, the research introduced the Customer’s Influence Degree (I) to the existing Recency, Frequency, and Monetary (RFM) values as an additional predictive factor, towards determining the churn class of a customer. The essence is to utilise the socio-transactional affinities of customers’ direct dependent to targeted communication nodes through customers RFM analysis to determine the dominance of a customer in the community. The newly introduced predictive factor helped to minimise churn misclassification rate through appropriate reclassification of customers who were wrongly classified as churner or non-churner when using the existing RFM churn scores only.