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出 处:《管理工程学报》2005年第1期104-107,共4页Journal of Industrial Engineering and Engineering Management
基 金:国家自然科学基金资助项目(50174021)。
摘 要:顾客购买行为是市场营销研究的重要内容之一。目前已有一些文献通过引入几个属性变量,构建起描述顾客购买行为或预测购买概率的随机模型。本文在前人研究的基础上,首先分析了对顾客购买行为影响较大的顾客购买决策、前后两次购买间隔时间、顾客重购行为和顾客逃逸等因素,而后将这些因素综合考虑,给出预测日用消费品重购概率的一种方法,并用实际数据对预测方法进行检验,预测的结果与实际数据非常接近。本方法的特点一是预测精度较高,二是具有一般性,适用于一般的日用消费品。Customer purchase behavior is a very fundamental part in marketing research.Some literatures in this field have emerged by incorporating several characteristic variables to set up mathematical models, which can describe customer purchase behavior or predict customer repurchase probability.On the basic of these researches,firstly, this paper analyze the four elements of customer in-store decision, customer interpurchase time, repurchase behavior and customer departure; and then integrates all these factors to provide a methodology that can predict customer repurchase probability.Meanwhile, the methodology employed is illustrated by taking the customer purchase data for salad oil as an example, and the results are relatively precise to actual selling data.Relative preciseness and more generality are two main advantages of the methodology provided.
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