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CRM strategies for a small-sized online shopping mall based on association rules and sequential patterns

机译：基于关联规则和顺序模式的小型在线购物中心的CRM策略

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摘要

As dot-com bubble burst in 2002, an uncountable number of small-sized online shopping malls have emerged every day due to many good characteristics of online marketplace, including significantly reduced search costs and menu cost for products or services and easily accessing products or services in the world. However, all the online shopping malls have not continuously flourished. Many of them even vanished because of the lack of customer relationship management (CRM) strategies that fit them. The objective of this paper is to propose CRM strategies for small-sized online shopping mall based on association rules and sequential patterns obtained by analyzing the transaction data of the shop. We first defined the VIP customers in terms of recency, frequency and monetary (RFM) value. Then, we developed a model which classifies customers into VIP or non-VIP, using various data mining techniques such as decision tree, artificial neural network, logistic regression and bagging with each of these as a base clas sifier. Last, we identified association rules and sequential patterns from the transactions of VIPs, and then these rules and patterns were utilized to propose CRM strategies for the online shopping mall.
机译：随着2002年互联网泡沫的破灭，由于在线市场的许多良好特征，每天出现无数的小型在线购物中心，包括大大降低了产品或服务的搜索成本和菜单成本，以及易于访问的产品或服务在世界上。但是，所有的在线购物中心都没有持续发展。由于缺乏适合他们的客户关系管理（CRM）策略，其中许多甚至消失了。本文的目的是基于关联规则和通过分析商店交易数据而获得的顺序模式，为小型在线购物中心提出CRM策略。我们首先根据新近度，频率和货币（RFM）价值定义VIP客户。然后，我们开发了一个模型，该模型使用各种数据挖掘技术（例如决策树，人工神经网络，逻辑回归和装袋法）将客户分为VIP或非VIP，并以这些为基础。最后，我们从VIP的交易中识别了关联规则和顺序模式，然后利用这些规则和模式为在线购物中心提出CRM策略。

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