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.
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