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.
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References
1.
AkaikeH. (1974) A new look at statistical model identification.IEEE Transactions on Automatic Control, AC-19, pp. 716–723.
AllenbyG., FennellG., BemmaorA., BhargavaV., ChristenF., DawleyJ., DicksonP., EdwardsY., GarrattM., GinterJ., SawyerA., StaelinR., & YangS. (2002) Market segmentation research: beyond within and across group differences.Marketing Letters, 13, 3, pp. 233–243.
4.
AndrewsR.L., & CurrimI.S. (2003a) A comparison of segment retention criteria for finite mixture logit models.Journal of Marketing Research, 40, 2, pp. 235–243.
5.
AndrewsR.L., & CurrimI.S. (2003b) Retention of latent segments in regression-based marketing models.International Journal of Research in Marketing, 20, 4, pp. 315–321.
6.
AndrewsR.L., CurrimI.S., LeeflangP., & LimJ. (2008) Estimating the SCAN*PRO model of store sales: HB, FM or just OLS?International Journal of Research in Marketing, 25, 1, pp. 22–33.
7.
ArabieP., & HubertL. (1994) Cluster analysis in marketing research. In BagozziR.P. (ed.) Advanced Methods of Marketing Research.Oxford: Blackwell Business, pp. 160–189.
8.
BloomJ.Z. (2005) Market segmentation: a neural network application.Annals of Tourism Research, 32, 1, pp. 93–111.
9.
BooneD.S., & RoehmM. (2002) Evaluating the appropriateness of market segmentation solutions using artificial neural networks and the membership clustering criterion.Marketing Letters, 13, 4, pp. 317–333.
10.
BozdoganH. (1987) Model selection and Akaike's Information Criterion (AIC): the general theory and its analytical extensions.Psychometrika, 52, 3, pp. 345–370.
11.
BozdoganH. (1994) Mixture model cluster analysis using selection criteria and a new informational measure of complexity. In BozdoganH. (ed.) Multivariate Statistical Modelling.Dordrecht: Kluwer Academic Publishers, 2, pp. 69–113.
12.
BrunM., SimaC., HuaJ., LoweyJ., CarrollB., SuhE., & DoughertyE.R. (2007) Model-based evaluation of clustering validation measures.Pattern Recognition, 40, pp. 807–824.
13.
BruscoM.J., & CraditJ.D. (2001) A variable-selection heuristic for k-means clustering.Psychometrika, 66, 2, pp. 249–270.
14.
CalinskiR.B., & HarabaszJ. (1974) A dendrite method for cluster analysis.Communications in Statistics, 3, pp. 1–27.
15.
DeckerR. (2006) A growing self-organized neural network for lifestyle segmentation.Journal of Data Science, 4, pp. 147–168.
16.
DeckerR., & ScholzS.W. (2007) Unsupervised topic detection in document collections: an application in marketing and business journals.International Journal of Business Intelligence and Data Mining, 2, 3, pp. 347–364.
17.
DeckerR., HolsingC., & LerkeS. (2006) Generating normally distributed clusters by means of a self-organizing growing neural network – an application to market segmentation.International Journal of Computer Science, 1, 2, pp. 138–144.
18.
DeckerR., WagnerR., & ScholzS.W. (2005) Growing clustering algorithms in market segmentation: defining target groups and related marketing communication. In BockH.-H., GaulW., & VickiM. (eds) Data Analysis, Classification and the Forward Search.Berlin: Springer, pp. 23–30.
19.
DibbS. (2009) Bridging the segmentation theory/practice divide.Journal of Marketing Management, 25, 3-4, pp. 219–225.
20.
DimitriadouE., DolničarS., & WeingesselA. (2002) An examination of the number of indexes for determining the number of clusters in binary data sets.Psychometrika, 67, 2, pp. 137–160.
21.
DolničarS. (2003) Using cluster analysis for market segmentation – typical misconceptions, established methodological weaknesses and some recommendations for improvement.Australasian Journal of Market Research, 11, 2, pp. 5–12.
22.
DolničarS., & GrünB. (2008) Challenging factor-cluster segmentation.Journal of Travel Research, 47, 1, pp. 63–71.
23.
DolničarS., & LazarevskiK. (2009) Methodological reasons for the theory/practice divide in market segmentation.Journal of Marketing Management, 25, 3-4, pp. 357–373.
24.
DolničarS., & LeischF. (2004) Segmenting markets by bagged clustering.Australasian Marketing Journal, 12, 2, pp. 51–65.
25.
DudaR.O., & HartP.E. (1973) Pattern Classification and Scene Analysis.New York: Wiley.
26.
ElrodT., RussellG.J., ShockerA.D., AndrewsR.L., BaconL., BayusB., CarrollJ.D., JohnsonR.M., KamakuraW.A., LenkP., MazanecJ.A., RaoV.R., & ShankarV. (2002) Inferring market structure from customer response to competing and complementary products.Marketing Letters, 13, 3, pp. 219–230.
FennellG., AllenbyG.M., YangS., & EdwardsY. (2003) The effectiveness of demographic and psychographic variables for explaining brand and product category use.Quantitative Marketing and Economics, 1, 2, pp. 223–244.
29.
FormannA.K. (1984) Die Latent-Class-Analysis: Eine Einführung in der Theorie und Anwendung.Weinheim: Beltz.
30.
FrankeN., ReisingerH., & HoppeD. (2009) Remaining within-cluster heterogeneity: a meta-analysis of the ‘dark side’ of clustering methods.Journal of Marketing Management, 25, 3-4, pp. 273–293.
HahnC., JohnsonM.D., HermannA., & HuberF. (2002) Capturing consumer heterogeneity using a finite mixture PLS approach.Schmalenbach Business Review, 54, 3, pp. 243–269.
33.
HaimerlE., & OhnemusR. (2005) Was läuft falsch bei Marktsegmentationen? [What goes wrong with market segmentations?]Jahrbuch der Absatz- und Verbrauchsforschung, 51, pp. 242–265.
34.
HairJ.F., AndersonR.E., TathamR.L., & BlackW.C. (1998) Multivariate Data Analysis (5th edn). New Jersey: Prentice Hall.
35.
HairJ.F., BlackW.C., BabinB.J., AndersonR.E., & TathamR.L. (2007) Multivariate Data Analysis (6th edn). New Jersey: Prentice Hall.
36.
HarzingA.-W. (2007) Journal Quality List, University of Melbourne. Online at: www.harzing.com (accessed 15 September 2007).
37.
JedidiK., JagpalH.S., & DeSarboW.S. (1997) Finite-mixture structural equation models for response-based segmentation and unobserved heterogeneity.Marketing Science, 16, 1, pp. 39–59.
38.
KaufmannL., & RousseeuwP. (1990) Finding Groups in Data: An Introduction to Cluster Analysis.New York: Wiley.
39.
KetchenD.J., & ShookC.L. (1996) The application of cluster analysis in strategic management research: an analysis and critique.Strategic Management Journal, 17, 6, pp. 441–458.
40.
LarsonJ., BradlowE., & FaderP. (2005) An exploratory look in supermarket shopping paths.International Journal of Research in Marketing, 22, 4, pp. 395–414.
41.
MagidsonJ., & VermuntJ.K. (2002) Latent class models for clustering: a comparison with k-means.Canadian Journal of Marketing Research, 20, pp. 37–44.
42.
MazanecJ. (2001) Neural market structure analysis: novel-topology sensitive methodology.European Journal of Marketing, 35, 7/8, pp. 894–914.
43.
MilliganG.W., & CooperM.C. (1985) An examination of procedures for determining the number of clusters in a dataset.Psychometrika, 50, pp. 159–179.
44.
MyersJ.H. (1996) Segmentation and Positioning for Strategic Marketing Decisions.Chicago, IL: American Marketing Association.
45.
PunjG., & StewartD.W. (1983) Cluster analysis in marketing research: review and suggestions for application.Journal of Marketing Research, 20, 2, pp. 134–148.
46.
QuinnL., HinesT., & BennisonD. (2007) Making sense of market segmentation: an apparel retailing case.European Journal of Marketing, 41, 5-6, pp. 439–465.
47.
RatkowskyD.A., & LanceG.N. (1978) A criterion for determining the number of groups in a classification.Australian Computer Journal, 10, pp. 115–117.
48.
ReuttererT., & NatterM. (2000) Segmentation-based competitive analysis with MULTICLUS and topology representing network.Computers & Operations Research, 27, pp. 1227–1247.
49.
SarleW.S. (1983) Cubic Clustering Criterion (Tech. Rep. A-108). Cary, North Carolina: SAS Institute.
50.
SarstedtM. (2008) A review of recent approaches for capturing heterogeneity in partial least squares path modeling.Journal of Modelling in Management, 3, 2, pp. 140–161.
51.
SarstedtM., & SchwaigerM. (2008) Model selection in mixture regression analysis – a Monte Carlo simulation study. In PreisachC., BurkhardtH., Schmidt-ThiemeL., & DeckerR. (eds) Data Analysis, Machine Learning, and Applications.Berlin: Springer, pp. 61–68.
52.
SchwarzG. (1978) Estimating the dimension of a model.Annals of Statistics, 6, 2, pp. 461–464.
53.
SteinleyD., & BruscoM.J. (2008a) A new variable weighting and selection procedure for k-means cluster analysis.Multivariate Behavioural Research, 43, 1, pp. 77–108.
54.
SteinleyD., & BruscoM.J. (2008b) Selection of variables in cluster analysis: an empirical comparison of eight procedures.Psychometrika, 73, 1, pp. 125–144.
55.
TonksD.G. (2009) Validity and the design of market segments.Journal of Marketing Management, 25, 3-4, pp. 341–356.
WagnerR., ScholzS.W., & DeckerR. (2005) The number of clusters in market segmentation. In BaierD., DeckerR., & Schmidt-ThiemeL. (eds) Data Analysis and Decision Support.Heidelberg: Springer, pp. 157–176.
WedelM., & KamakuraW.A. (2002) Introduction to the special issue on market segmentation.International Journal of Research in Marketing, 19, pp. 181–183.
60.
XuL. (1997) Bayesian ying-yang machine, clustering and the number of clusters.Pattern Recognition Letters, 18, pp. 1167–1178.
61.
YankelovicD., & MeerD. (2006) Rediscovering market segmentation.Harvard Business Review, 84, 2, pp. 122–132.
