An approach for clustering consumers by their top-box and top-choice responses

IF 1.6 3区 农林科学 Q3 FOOD SCIENCE & TECHNOLOGY
John C. Castura, Michael Meyners, Terhi Pohjanheimo, Paula Varela, Tormod Næs
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引用次数: 0

Abstract

Cluster analysis is often used to group consumers based on their hedonic responses to products. We give a motivating example in which conventional cluster analyses converge on a solution where consumers do not agree on which products they like. We show why this occurs. We state a goal: to group together consumers who have a shared opinion of which products are delightful and which products are not delightful, apart from consumers who have a different opinion. To meet this goal, we code consumers' hedonic responses in ways inspired by top-k box analysis, then cluster consumers using b-cluster analysis. For comparison, we cluster consumers using two conventional methods. We interpret each cluster by focusing on which product(s) the cluster accepts and whether a large proportion of cluster members are aligned in accepting these products. Solutions from b-cluster analysis based on top-k box-inspired codings met our goal better than conventional approaches, indicating that these methods deserve further study.

Practical Applications

Cluster analysis outcomes are profoundly shaped by a researcher's decisions related to response coding and clustering algorithm. This paper highlights the importance of determining the goal of the cluster analysis first, then selecting a response coding and clustering algorithm to best meet this goal. Our stated goal is one that is frequently of interest in sensory evaluation but is not well met by conventional clustering approaches. The novel approaches that we give in this paper meet the goal and are available using software that is freely available in the public domain.

一种根据消费者的首选和首选反应对其进行聚类的方法
聚类分析通常用于根据消费者对产品的享乐反应对其进行分组。我们给出了一个激励的例子,在这个例子中,传统的聚类分析收敛于消费者不同意他们喜欢哪种产品的解决方案。我们将说明为什么会发生这种情况。我们提出了一个目标:将那些对哪些产品令人愉快和哪些产品不令人愉快有共同看法的消费者分组,而不是有不同意见的消费者。为了实现这一目标,我们以top-k盒分析的方式对消费者的享乐反应进行编码,然后使用b-聚类分析对消费者进行聚类。为了比较,我们使用两种传统方法对消费者进行聚类。我们通过关注集群接受哪些产品以及是否大部分集群成员在接受这些产品方面保持一致来解释每个集群。基于top-k盒启发编码的b-聚类分析解决方案比传统方法更符合我们的目标,表明这些方法值得进一步研究。研究人员对响应编码和聚类算法的决策影响着聚类分析的结果。本文强调了首先确定聚类分析目标的重要性,然后选择最能满足这一目标的响应编码和聚类算法。我们声明的目标是一个经常感兴趣的感官评估,但不能很好地满足传统的聚类方法。我们在本文中给出的新方法满足了目标,并且可以使用公共领域中免费提供的软件。
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来源期刊
Journal of Sensory Studies
Journal of Sensory Studies 工程技术-食品科技
CiteScore
3.80
自引率
20.00%
发文量
71
审稿时长
18-36 weeks
期刊介绍: The Journal of Sensory Studies publishes original research and review articles, as well as expository and tutorial papers focusing on observational and experimental studies that lead to development and application of sensory and consumer (including behavior) methods to products such as food and beverage, medical, agricultural, biological, pharmaceutical, cosmetics, or other materials; information such as marketing and consumer information; or improvement of services based on sensory methods. All papers should show some advancement of sensory science in terms of methods. The journal does NOT publish papers that focus primarily on the application of standard sensory techniques to experimental variations in products unless the authors can show a unique application of sensory in an unusual way or in a new product category where sensory methods usually have not been applied.
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