Hybrid model for profit-driven churn prediction based on cost minimization and return maximization

Abstract

Customer churn prediction is widely used to detect potential churners, which stimulates customer retention, and decrease churn loss. Most customer churn prediction models evaluate classifiers with a profit maximization indicator, which ignores the complex relationship between the cost and return of customer churn prediction. To fill this gap, a hybrid profit-driven churn prediction model is proposed that considers both return and cost. To evaluate category and continuous variables in the sample, synthetic minority over-sampling technique-nominal continuous is used to predict churners and non-churners. The feature selection based on a modified multi-objective atomic orbital search and extreme learning machine is used to obtain suitable variables for churn prediction with maximum return and minimum cost. Moreover, the modified multi-objective atomic orbital search optimizes the initial weights and thresholds of extreme learning machine to maximize profit from churn prediction. Based on two real-life datasets from bank and telecommunication service providers, experimental results show that the proposed hybrid model can achieve high-quality forecasting performance with higher profit, which can provide reliable references for operators and decision-makers.