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Customer Visit Prediction Using Purchase Behavior and Tendency

机译：客户访问预测使用购买行为和趋势

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摘要

Over the last few decades, consumers' preferences and lifestyles have changed significantly. In such market, the number of products and services aims to mass marketing have become less effective. Therefore, personalization tailored to individual characteristics, certain segments, and one-to-one marketing focusing on individuals are becoming important. Therefore, customer relationship management, retaining existing customers and costs for acquiring new customers are important for both of academic and business field. However, for that purpose, it is essential to grasp customer behavior in more detail and use it for analysis. Therefore, in this study, we estimate the potential clusters of customers and customers' purchase behavior. Concretely, we use pLSA and XGBoost which become popular machine learning methodologies. In this study, we set the number of store visits per month for objective variable and show a prediction model of it. Then we compare the model that incorporates the result of predicting the probability of the latent cluster as an explanatory variable with the model that incorporates a general explanatory variable.
机译：在过去的几十年里，消费者的偏好和生活方式发生了重大变化。在此类市场中，产品和服务的数量旨在大规模营销变得不那么有效。因此，针对各个特征，某些部分和专注于个人的一对一营销量身定制的个性化正在变得重要。因此，客户关系管理，保留现有客户和收购新客户的费用对于学术和商业领域都很重要。但是，为此目的，必须更详细地掌握客户行为并使用它进行分析。因此，在这项研究中，我们估计了客户和客户购买行为的潜在集群。具体地，我们使用PLSA和XGBoost成为流行的机器学习方法。在本研究中，我们为目标变量设置每月的商店访问数，并显示其预测模型。然后，我们将包含预测潜在群集的概率的模型进行比较，作为包含一般解释性变量的模型的解释性变量。

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