Customer behavior modeling and classification are well-studied areas for applications in retail. Past studies implemented the purchase behavior modeling based on the physical behavior of a subject. In this research, we apply the recency, frequency, and monetary (RFM) model and data modeling techniques to detect behavior patterns for a customer. Each transaction attributed to a customer is part of one's behavior, and an instance of the feature vector, it is modeled on a set of transactions to constitute repurchase behavior. The proposed scheme is validated by simulating a publicly accessible real-world data set with a need-tailored multi-layer perceptron (MLP) and also support vector machine (SVM) and decision tree classification (DTC) methods. The experiments yield a high customer classification rate of more than 97% for the different numbers of the customers. Empirical analysis shows that eight transactions are sufficient to classify a customer with high accuracy.

客户行为建模和分类是零售应用中经过深入研究的领域。过去的研究基于对象的身体行为来实施购买行为模型。在这项研究中，我们应用新近度，频率和货币（RFM）模型以及数据建模技术来检测客户的行为模式。归因于客户的每笔交易都是一个人的行为的一部分，而特征向量的一个实例则以一组交易为模型，以构成回购行为。通过使用需要量身定制的多层感知器（MLP）以及支持向量机（SVM）和决策树分类（DTC）的方法来模拟可公开访问的现实世界数据集，从而验证了所提出的方案。实验产生了超过97的高客户分类率％代表不同数量的客户。实证分析表明，八笔交易足以对客户进行高精度分类。