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Loyal customer discriminant model using customer features on EC site

机译:使用EC网站上的客户功能进行忠诚的客户区分模型

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In this study, we try to measure customer loyalty attract high attention from the viewpoint of customer relationship management (CRM). From this scape there are a few studies to measure the loyalty of the customers. However, these studies have not considered the potential customer features. In this study, we focus on EC site, then we additionaly consider the behavior out of the site as well as the behavior in the site for a customer. By doing so, we caught a wide variety of customer behavior by adding the behavior data out of the site and could identify a loyal customer more appropriately. In this study, we propose a model which is able to classify a loyal customer to discover a customer who has high loyalty to the EC site. For modeling, we used the behavior data out of the EC site in addition to RFM (Recency, Frequency, Monetary) gotten from purchasing data. In addition, based on the result of the precedent study, we adopted logistic regression to analyze the purchasing history of the loyal customer identified in the discriminant model. We try to grasp the purchasing tendency of a customer having high loyalty by analyzing the purchasing data of the loyal customer. In this study, We classified loyal customer into 3 classes Customer-A, Customer-B or Customer-C. We compared the precision by the RFM model and the our RFMO model. As a result, RFMO model can more classify loyal customer and the other customer with high precision compared with RFMmodel about Customer-A and Customer-B. In addition, it was shown that purchasing tendencies is difference at each loyal customers.
机译:在本研究中,我们尝试从客户关系管理(CRM)的角度衡量吸引客户关注的忠诚度。从这个角度来看,有一些研究可以衡量客户的忠诚度。但是,这些研究没有考虑潜在的客户功能。在本研究中,我们将重点放在EC站点上,然后再考虑站点外的行为以及客户在站点内的行为。通过这样做,我们通过将行为数据添加到站点之外来捕获了各种各样的客户行为,并且可以更适当地确定忠实的客户。在这项研究中,我们提出了一个模型,该模型能够对忠实的客户进行分类,以发现对EC网站具有较高忠诚度的客户。对于建模,除了从购买数据中获得的RFM(新近度,频率,货币)外,我们还使用了EC站点之外的行为数据。另外,根据先例研究的结果,我们采用逻辑回归分析了判别模型中确定的忠诚客户的购买历史。我们通过分析忠诚客户的购买数据来尝试掌握具有高度忠诚度的客户的购买倾向。在这项研究中,我们将忠实的客户分为3类:客户A,客户B或客户C。我们通过RFM模型和我们的RFMO模型比较了精度。结果,与关于客户A和客户B的RFM模型相比,RFMO模型可以更精确地对忠诚客户和其他客户进行分类。另外,还显示出购买倾向在每个忠实顾客上是不同的。

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