Retention of latent segments in regression-based marketing models

Retention of latent segments in regression-based marketing models
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DOI:
10.1016/j.ijresmar.2003.04.001
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发表时间:
2003-12-01
影响因子:
7
通讯作者:
Currim, IS
Currim, IS
中科院分区:
管理学3区
文献类型:
--
作者:
Andrews, RL;Currim, IS

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细分市场的产品设计和营销组合决策关键取决于作为这些决策输入的营销模式的正确规范。根据真实世界的数据,市场中细分的真实数量是未知的。模拟研究的现有证据表明,用于确定市场细分数量的常用标准的准确性取决于使用环境,包括用于描述数据的分布类型,模型规格和市场特征。本研究通过模拟研究了七个片段保留标准与有限混合回归模型的正常数据的性能。这是市场研究中最重要的分析背景之一,因为回归模型被用于联合分析和市场反应分析,但在市场营销或统计文献中没有以前的研究探讨了混合回归模型的细分保留问题。该研究表明,一个标准,赤池的信息标准(AIC)与每参数惩罚因子3(AIC 3),显然是最好的标准,使用在各种各样的模型规格和数据配置,具有最高的成功率,并产生非常低的参数偏差。目前,这一标准很少,如果有的话,在营销文献中使用。(C)2003 Elsevier B.V.保留所有权利。
Product design and marketing mix decisions for segmented markets depend crucially on the correct specification of marketing models used as input to these decisions. With real-world data, the true number of segments in a market is unknown. Current evidence from simulation studies suggests that the accuracy of commonly used criteria for determining the number of segments in a market depends on the usage context, including the type of distribution being used to describe the data, the model specification, and the characteristics of the market. This study investigates via simulation the performance of seven segment retention criteria used with finite mixture regression models for normal data. This is one of the most important analysis contexts in marketing research since regression models are used, for example, in conjoint analysis and market response analysis, yet no previous study in either the marketing or statistics literatures explores the segment retention problem for mixture regression models. The study shows that one criterion, Akaike's Information Criterion (AIC) with a per-parameter penalty factor of 3 (AIC3), is clearly the best criterion to use across a wide variety of model specifications and data configurations, having the highest success rate and producing very low parameter bias. Currently, this criterion is rarely, if ever, used in the marketing literature. (C) 2003 Elsevier B.V. All rights reserved.