Cluster Analysis in Marketing Research

2020 ◽  
pp. 1-29
Author(s):  
Thomas Reutterer ◽  
Daniel Dan
Keyword(s):  
Cluster Analysis ◽  
Marketing Research

A Survey of the Challenges and Pifalls of Cluster Analysis Application in Market Segmentation

2011 ◽  
Vol 53 (3) ◽  
pp. 391-414 ◽  
Author(s):  
Michael N. Tuma ◽  
Reinhold Decker ◽  
Sören W. Scholz
Keyword(s):  
Cluster Analysis ◽  
Best Practices ◽  
Market Segmentation ◽  
Marketing Research ◽  
Journal Articles ◽  
Research Setting ◽  
Empirical Survey ◽  
Critical Issues ◽  
Analysis Application

Market segmentation is a widely accepted concept in marketing research and planning. Although cluster analysis has been extensively applied to segment markets in the last 50 years, the ways in which the results were obtained have often been reported to be less than satisfactory by both practitioners (Yankelovich & Meer 2006) and academics (Dolnièar 2003). In order to provide guidance to those undertaking market segmentation, this study discusses the critical issues involved when using cluster analysis to segment markets, makes suggestions for best practices and potential improvements, and presents an empirical survey that seeks to provide an up-to-date assessment of cluster analysis application in market segmentation within a six-stage framework. Analyses of more than 200 journal articles published since 2000, in which cluster analysis was empirically used in a marketing research setting, indicate that many critical issues are still ignored rather than addressed adequately.

Cluster Analysis in Marketing Research: Review and Suggestions for Application

1983 ◽  
Vol 20 (2) ◽  
pp. 134-148 ◽  
Author(s):  
Girish Punj ◽  
David W. Stewart
Keyword(s):  
Cluster Analysis ◽  
Empirical Work ◽  
Marketing Research ◽  
Minimum Variance ◽  
Alternative Methods ◽  
Research Review ◽  
Preliminary Identification ◽  
Cluster Analytic ◽  
Partitioning Procedure ◽  
Iterative Partitioning

Applications of cluster analysis to marketing problems are reviewed. Alternative methods of cluster analysis are presented and evaluated in terms of recent empirical work on their performance characteristics. A two-stage cluster analysis methodology is recommended: preliminary identification of clusters via Ward's minimum variance method or simple average linkage, followed by cluster refinement by an iterative partitioning procedure. Issues and problems related to the use and validation of cluster analytic methods are discussed.

Cluster Analysis in Marketing Research

2021 ◽  
pp. 221-249
Author(s):  
Thomas Reutterer ◽  
Daniel Dan
Keyword(s):  
Cluster Analysis ◽  
Marketing Research

A Note on the Reliability of Certain Clustering Algorithms

1983 ◽  
Vol 20 (1) ◽  
pp. 99-102 ◽  
Author(s):  
G. Ray Funkhouser
Keyword(s):  
Cluster Analysis ◽  
Market Segmentation ◽  
Clustering Algorithms ◽  
Marketing Research ◽  
Cluster Membership ◽  
Large Numbers

Cluster analysis is widely used in marketing research, especially in market segmentation studies. Though it is well known that cluster analysis approaches can be unreliable, the author's research suggests that unreliability may be a greater problem than has previously been suggested, especially with approaches involving large numbers of decisions about cluster membership.

A Cluster Analysis of Physician’s Values, Prescribing Behaviour and Attitudes towards Firms’ Marketing Communications

2012 ◽  
pp. 250-267 ◽  
Author(s):  
Despina Karayanni
Keyword(s):  
Cluster Analysis ◽  
Data Collection ◽  
Quantitative Research ◽  
Personal Values ◽  
Marketing Research ◽  
Marketing Communications ◽  
Primary Data ◽  
Exploratory Research ◽  
Prescribing Behaviour ◽  
Prescription Behavior

In this paper, the authors present an exploratory research on the associations between physicians’ personal values with physicians’ prescribing criteria and preferred marketing communications. The research involved extant marketing research and primary data collection. The resulting quantitative research instrument was then administered to a sample of 69 physicians, yielding a 69% response rate. All but the demographic measures were tapped by 5-point scales and a series of factor and reliability analyses assessed unidimentionality and reliability of research constructs. A series of ANOVAs and Tukey tests depicted the differences among three clusters. Implications are that physicians’ personal values may be a meaningful basis of segmentation for the pharmaceutical market, and findings may be useful for both marketing strategy planners and researchers examining physicians’ prescription behavior and attitudes towards firms’ marketing communication efforts.

scholarly journals Analysis of quality labels included in the European Union quality schemes

2014 ◽  
Vol 32 (No. 2) ◽  
pp. 194-203 ◽  
Author(s):  
Š. Velčovská ◽  
T. Sadílek
Keyword(s):  
European Union ◽  
Cluster Analysis ◽  
Food Quality ◽  
Marketing Research ◽  
Secondary Data ◽  
Geographical Indication ◽  
The European Union ◽  
Protected Designation Of Origin ◽  
Chi Squared ◽  
Definition Of

We analysed the use of European Union schemes of Protected Designation of Origin, Protected Geographical Indication and Traditional Speciality Guaranteed labels in the European Union market according to selected criteria including type of label, country of origin, and product classes. The first part of the paper deals with the definition of food quality, quality labels, and European Union quality scheme specification. The second part includes the discussion of marketing research results. Secondary data from the Database of Origin and Registration are used. The sample consists of 1146 labels registered in this database as to the 28<sup>th</sup> of March 2013. The frequency of label utilisation is analysed according to country, type of label, and product classes, while the cluster analysis is also applied. Pearson&rsquo;s chi-squared test of independence is used to determine if significant differences exist between the frequency of using the labels and the above-mentioned criteria. &nbsp;

scholarly journals Quantitative data systemization and visualisation in marketing research: groceries selection determinants

2010 ◽  
Vol 58 (6) ◽  
pp. 179-188
Author(s):  
Jitka Janová ◽  
Ladislav Stejskal
Keyword(s):  
Cluster Analysis ◽  
Marketing Research ◽  
Hierarchical Cluster ◽  
Research Data ◽  
Decision Making Process ◽  
Primary Research ◽  
Graphic Visualization ◽  
Marketing Data ◽  
Statistical Surveys ◽  
Marketing Information

The paper aims to fill in the gap in effective interpretation of the results obtained when systemizing the marketing data by cluster analysis. The graphic visualization of the cluster analysis results is developed in the way the marketing information can be more easily readable and interpretable. Using the primary research data concerning decision making process of consumer when purchasing gro­ce­ries, the systemization of consumers using hierarchical cluster analysis is performed for several sets of consumers’ characteristics and for each case the graphic visualization is developed. The graphical information is interpreted and the marketing impacts of the results obtained by the cluster analysis and presented by the visualization are discussed. Range of possible applications of the procedure constructed encompasses also other spheres of primary and secondary marketing research and ge­ne­ral­ly is useful for the effective analyses of various statistical surveys.

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