Enhanced Churn Prediction Using Stacked Heuristic Incorporated Ensemble Model

Sivasankar Karuppaiah, N. Gopalan
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引用次数: 2

Abstract

In a rapidly growing industry like telecommunications, customer churn prediction is a crucial challenge affecting the sustainability of the business as a whole. The fact that retaining a customer is more profitable than acquiring new customers is important to predict potential churners and present them with offers to prevent them from churning. This work presents a stacked CLV-based heuristic incorporated ensemble (SCHIE) to enable identification of potential churners so as to provide them with offers that can eventually aid in retaining them. The proposed model is composed of two levels of prediction followed by a recommendation to reduce customer churn. The first level involves identifying effective models to predict potential churners. This is followed by result segregation, CLV-based prediction, and user shortlisting for offers. Experimental results indicate high efficiencies in predicting potential churners and non-churners. The proposed model is found to reduce the overall loss by up to 50% in comparison to state-of-the-art models.
基于堆叠启发式集成模型的客户流失预测
在像电信这样快速发展的行业中,客户流失预测是影响整个业务可持续性的关键挑战。留住老客户比获得新客户更有利可图,这一点对于预测潜在的流失客户并向他们提供优惠以防止他们流失非常重要。这项工作提出了一个堆叠的基于clv的启发式集成(SCHIE),以识别潜在的流失,从而为他们提供最终有助于留住他们的报价。提出的模型由两个层次的预测组成,然后是减少客户流失的建议。第一个层次包括识别有效的模型来预测潜在的流失。接下来是结果分离、基于clv的预测和用户候选列表。实验结果表明,预测潜在流失和非流失的效率很高。研究发现,与最先进的模型相比,拟议的模型可将总损失减少多达50%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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