随机RFM模型及其在零售顾客价值识别中的应用  

Stochastic RFM Model and Its Application in Customer Values Analysis

作　　者：马宝龙[1] 李飞[2] 王高[3] 李纯青[4] 

机构地区：[1]北京理工大学管理与经济管理学院,北京100081 [2]清华大学经济管理学院,北京100084 [3]中欧国际工商学院,上海201206 [4]西安工业大学经济管理学院,陕西西安710032

出　　处：《管理工程学报》2011年第1期102-108,共7页Journal of Industrial Engineering and Engineering Management

基　　金：国家自然科学基金项目(7100210270872087);教育部人文社会科学研究青年基金项目(09YJC630136)

摘　　要：对顾客进行价值识别是以顾客价值为导向进行差异化营销管理的基础和前提。本文通过建立随机RFM模型,提供了一种对未来顾客价值进行识别的模型方法,并利用一家购物中心的顾客交易数据对该方法进行了实证分析。结果表明随机RFM模型不仅是进行顾客价值识别与分析的有效工具,也是研究分析顾客购买行为的有效模型方法。该模型方法对于零售企业加强顾客价值分析,提高基于顾客价值的管理水平具有积极的实践意义。Customer relationship management programs have the merits of targeting valuable customers and distinguishing between valuable customers and non-valuable customers.The RFM（Recency-Frequency-Monetary） model is the most common method used in CRM programs.RFM often asserts that customers with higher shopping frequency and larger shopping volume are the most valuable customers.However,the traditional application of RFM ignores the nature of customer heterogeneity.A deeper understanding of customer heterogeneity can lead to more accurate predictions of customer demands.Customer heterogeneity is a valuable source of ideas for creative marketing strategies.One effective way of overcoming current RFM problems is to model buyer behaviors based on the stochastic method.The goal of this paper is to propose a stochastic method to estimate customer values and conduct empirical validation.This research consists of three steps.Step 1 is to propose the underlying probability distribution of customers＇ purchasing behaviors and construct a stochastic method to model customers＇ purchasing behavior.Step 2 is to use the stochastic method to model customer R（Recency）,F（Frequency） and M（Monetary） behaviors,predict customer future responses and estimate customer value.Step 3 is to validate the proposed model using empirical data collected from a panel of customers in a shopping center in Beijing.Data collected are split into two sets： 6 months of training data and 6 months of testing data.A cluster analysis is conducted to cluster the participants into four homogeneous groups,followed by customer＇s value analysis.Analysis results are then compared between the model＇s predicted value and the actual value.Several conclusions are made in this study based on the analysis results.The proposed model outperforms the traditional methods and achieves a very accurate estimation of customers＇ values.The modeling method has great value to retailers in enhancing customer value analysis and improving customer relationship m

关 键 词：随机模型 RFM模型 顾客价值 

分 类 号：F830[经济管理—金融学]