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Consumer segmentation in multi-attribute product evaluation by means of non-negatively constrained CLV3W

机译：通过非负约束的CLV3W进行多属性产品评估的消费者分割

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摘要

In consumer studies, segmentation has been widely applied to identify consumer subsets on the basis of their preference for a set of products. From the last decade onwards, a more comprehensive evaluation of product performance has led to take into account various information such as consumer emotion assessment or hedonic measures on several aspects, like taste, visual and flavor. This multi-attribute evaluation of products naturally yields a three-way (products by consumers by attributes) data structure. In order to identify segments of consumers on the basis of such three-way data, the Three-Way Cluster analysis around Latent Variables (CLV3W) approach (Wilderjans & Cariou, 2016) is considered. This method groups the consumers into clusters and estimates for each cluster an associated latent product variable and attribute weights, along with a set of consumer loadings, which may be used for the purpose of cluster-specific product characterization. As consumers who rate the products along the attributes in an opposite way (i.e., raters' disagreement) should not be in the same cluster, in this paper, we propose to add a non-negativity constraint on the consumer loadings and to integrate this constraint within the versatile CLV3W approach. This non-negatively constrained criterion implies that the latent variable for each cluster is determined such that consumers within each cluster are as much related - in terms of a positive covariance - as possible with this latent product component. This approach is applied to a consumer emotion ratings dataset related to coffee aromas. (C) 2017 Elsevier Ltd. All rights reserved.
机译：在消费者研究中，分割已被广泛应用于识别消费者子集，以偏好对一套产品进行偏好。从最后十年开始，对产品绩效的更全面的评估导致了在几个方面的消费者情感评估或蜂窝织措施等各种信息，如味道，视觉和味道。该产品的这种多属性评估自然地产生三通（通过属性的消费者的产品）数据结构。为了基于这种三元数据识别消费者的细分，考虑了潜在变量（CLV3W）方法（Wilderjans＆amp; Cariou，2016）周围的三元集群分析。该方法将消费者分组到每个集群的集群和估计，每个群集相关的潜在产品变量和属性权重以及一组消费负载，可以用于特定于集群特定的产品特征。作为以相反的方式沿着属性评价产品的消费者（即，评估者的分歧）不应在同一集群中，在本文中，我们建议为消费负荷增加非负面影响并集成这一限制在多功能CLV3W方法中。该非负限制标准意味着确定每个群集的潜变量，使得每个群集内的消费者与正协方差的消费者与该潜在产品组件尽可能多地相关。这种方法适用于与咖啡香气相关的消费者情感评级数据集。 （c）2017 Elsevier Ltd.保留所有权利。

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