客户重复购买的组合预测方法

doi:10.3969/j.issn.1006-2475.2015.05.014

计算机与现代化

客户重复购买的组合预测方法

(江苏科技大学经济管理学院，江苏镇江 212003)

收稿日期:2015-01-21 出版日期:2015-05-18 发布日期:2015-05-18
作者简介:舒方(1990-)，女，河南固始人，江苏科技大学经济管理学院硕士研究生，研究方向：客户关系管理; 马少辉(1972-)，男，副教授，博士，研究方向：客户关系管理，决策支持系统。
基金资助:
国家自然科学基金资助项目(71171100)

A Composition Forecasting Approach of Customer Repeat Purchasing

(School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang 212003, China)

Received:2015-01-21 Online:2015-05-18 Published:2015-05-18

摘要/Abstract

摘要： SMC模型是由Schmittlein 等人提出，用于描述非契约客户关系情景下客户的重复购买行为。该模型假设客户重复购买行为服从泊松过程，客户一旦流失则不会被赢回。HIPP模型是由马少辉等人提出，该模型假设客户不会永久流失，而是在活跃和不活跃的状态之间转换。然而在现实中，永久流失和暂时流失的客户都是可能存在的。因此，SMC模型可能会低估重复购买的概率，而HIPP模型可能会高估重复购买的概率。本文提出一种客户重复购买的组合预测方法SMC模型和HIPP模型分别对客户重复购买进行预测，通过遗传算法寻找这2个模型的最优组合权值。通过实证分析，验证了组合预测方法的优越性。。该方法利用

关键词: SMC模型, HIPP模型, 组合预测方法, 遗传算法

Abstract: SMC model was proposed by Schmittlein et al., which is very popular in customer base analyses under non-contractual settings. The SMC model assumes that customers’ purchasing follow Poisson processes until they defect. The HIPP model was proposed by Ma et al., which assumes that customers do not defect, but instead they switch constantly between active and inactive state. In reality however, both permanent and temporary defecting may exist. Therefore, SMC model may underestimate customer’s probability of re-purchasing, while HIPP model may overestimate that. Based on two models, in this paper, we propose a composition forecasting approach which combines the forecasts from both SMC and HIPP. The optimal weights of compositing are calculated by a genetic algorithm. Empirical studies show the superiority of the proposed composition approach.

Key words: SMC model, HIPP model, composition forecasting, genetic algorithm

中图分类号:

TP391

舒方，马少辉. 客户重复购买的组合预测方法[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2015.05.014.

SHU Fang, MA Shao-hui. A Composition Forecasting Approach of Customer Repeat Purchasing[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2015.05.014.

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客户重复购买的组合预测方法

A Composition Forecasting Approach of Customer Repeat Purchasing

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