Enhanced feature mining and classifier models to predict customer churn for an E-retailer

K. B. Subramanya, Arun Kumar Somani
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引用次数: 12

Abstract

Customer Churn, an event indicating a customer abandoning an established relation with a business is an important problem researched well, both for academic and commercial interest. Through this work, we propose an improved churn prediction model that emphasizes on an effective data collection pipeline through varied channels capturing explicit and implicit customer footprints. The goal of this paper is to demonstrate the improvement in classifier efficiency using an extended feature set and feature selection algorithms. Prominent features playing a vital role in customer churn are also ranked. The contributions through this paper can be broadly categorized into 3 folds: First, we discuss how popular data mining tools in Hadoop stack help extract several implicit customer interaction metrics including Sales and Clickstream logs generated as a result of customer interaction. Second, through Feature Engineering techniques we verify that some of the new features we propose have a definite impact on customer churn. Finally, we demonstrate how Regularized Logistic Regression, SVM and Gradient Boost Random Forests are the best performing models for predicting customer churn verified through comprehensive cross-validation techniques.
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增强的特征挖掘和分类器模型预测电子零售商的客户流失
客户流失是一个表明客户放弃与企业建立关系的事件,是一个重要的问题,研究得很好,无论是学术还是商业利益。通过这项工作,我们提出了一种改进的客户流失预测模型,该模型强调通过各种渠道捕获显式和隐式客户足迹的有效数据收集管道。本文的目标是证明使用扩展的特征集和特征选择算法来提高分类器的效率。在客户流失中发挥重要作用的突出特征也被排名。本文的贡献可以大致分为三部分:首先,我们讨论了Hadoop堆栈中流行的数据挖掘工具如何帮助提取几个隐含的客户交互指标,包括由客户交互产生的销售和点击流日志。其次,通过特征工程技术,我们验证了我们提出的一些新特性对客户流失有明确的影响。最后,我们展示了正则化逻辑回归、支持向量机和梯度提升随机森林是如何通过全面的交叉验证技术来预测客户流失的最佳模型。
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