忠诚计划会员是否会导致重复购买:基于客户特征和机器学习的人口统计分析

Reeti Agarwal
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摘要

背景:目前的研究是为了找出各种利益的顾客认为,由于是百货公司的忠诚计划的成员积累。本研究从印度百货商店的角度扩展了Yi和Jeon(2003)的研究成果,并吸收了MimouniChaabane和Volle(2010)所倡导的多样化优势框架。目的:目前研究的主要目的是根据他们对忠诚度计划利益的看法,找出不同的客户群体,并确定他们的人口统计资料。材料和方法:在印度客户中,对观察到的忠诚计划利益的量表进行了验证。根据顾客对忠诚度计划的好处的看法,他们被分成了不同的组。此外,分类和回归树(C&RT)是一种机器学习技术,用于发现人口统计学特征是否以及如何影响客户在拥有忠诚计划会员的商店重复购买的倾向。结果与结论:确定了“前景”、“不确定”和“可疑”三个集群,并列举了不同集群的概况,以增强对不同集群的了解,从而使企业的目标定位工作更加有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Does Loyalty Program Membership Lead to Repeat Purchase: A Demographic Analysis with Customer Profiling and Machine Learning
Background: The current study was undertaken to find out the various benefits customers perceive as accruing from being members of loyalty programs of departmental stores. The study outspreads Yi and Jeon’s (2003) work assimilated along with the diverse advantages framework advocated by MimouniChaabane and Volle (2010) from the perspective of departmental stores in India. Objective: The primary aim of the current study is to find out different groups of customers based on their perception of loyalty program benefits and to identify their demographic profile. Material and methods: The scale relating to observed loyalty program benefits was authenticated in respect of Indian customers. The customers were divided into clusters based on their opinion of the benefits of loyalty programs. Further, Classification and Regression Tree (C&RT), a Machine Learning technique, was applied to find out if and how demographic characteristics have an impact on a customer’s inclination to go for a repeat purchase in a store whose loyalty program membership the customer has. Results and conclusion: Three clusters namely ‘Prospects, ‘Uncertain’ and ‘Suspects’ were identified and the profile of different clusters were enumerated to enhance the understanding, which can be utilized for making the targeting efforts of companies more effective.
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