1、PowerPoint to panyNaresh MalhotraJohn HallMike ShawPeter OppenheimPowerPoint to panyChapter 17Identifying and Mapping Consumer PreferencesChapter ObjectivesAfter reading this chapter you should be able to:Describe the basic concept and scope of multi-dimensional scaling (MDS) and its applications in

2、 marketing researchDescribe the steps involved in multi-dimensional scaling of perception dataExplain the multidimensional scaling of preference data and distinguish between internal and external analysis of preferencesExplain correspondence analysis and discuss its advantages and disadvantagesChapt

3、er Objectives (continued)Understand the relationship between MDS, discriminant analysis and factor analysisDescribe concept of conjoint analysis, contrast it with MDS and discuss its applications in marketing researchDescribe the procedures for conducting conjoint analysisDefine hybrid conjoint anal

4、ysis and explain how it simplifies data-collectionConduct a conjoint analysis using SPSS or similar software packageChapter OutlineBasic Concepts in Multidimensional ScalingConducting Multidimensional ScalingAssumptions and Limitations of MDSScaling Preference DataMapping Consumer Perceptions with S

5、PSSBasic Concepts in Conjoint AnalysisConducting Conjoint AnalysisAssumptions and Limitations of Conjoint AnalysisHybrid Conjoint AnalysisComputer ApplicationsSummaryTopic Basic Concepts in Multidimensional ScalingConducting Multidimensional ScalingAssumptions and Limitations of MDSScaling Preferenc

6、e DataMapping Consumer Perceptions with SPSSBasic Concepts in Conjoint AnalysisConducting Conjoint AnalysisAssumptions and Limitations of Conjoint AnalysisHybrid Conjoint AnalysisComputer ApplicationsSummaryBasic Concepts in Multidimensional ScalingA class of procedures for representing perceptions

7、and preferences of respondents spatially by means of a visual displayMarketing applications:Number and nature of dimensions consumers use to perceive brandsPositioning of current brand on these dimensionsPositioning of consumers ideal brand on these dimensionsRefer to Examples 17.1 and 17.2 pp. 648-

8、9 for illustrations of MDSTopic Basic Concepts in Multidimensional ScalingConducting Multidimensional ScalingAssumptions and Limitations of MDSScaling Preference DataMapping Consumer Perceptions with SPSSBasic Concepts in Conjoint AnalysisConducting Conjoint AnalysisAssumptions and Limitations of Co

9、njoint AnalysisHybrid Conjoint AnalysisComputer ApplicationsSummaryConducting Multidimensional ScalingFormulate the ProblemNeed to specify the purpose for which MDS will be used, select the brands or other stimuliMinimum of eight brands/stimuli maximum of 25Those selected depends upon nature of the

10、marketing problemObtain Input DataObtain Input DataPerception Data: Direct ApproachesRespondents asked to judge how similar/dissimilar brands are using own criteria using a Likert scale similarity judgmentsPerception Data: Derived ApproachesAttribute-based approaches requiring respondents to rate br

11、ands/stimuli on identified attributes using semantic differential or Likert scalesSee p. 651 for an exampleTable 17.1: Similarity Ratings of Toothpaste BrandsObtain Input DataDirect Versus Derived ApproachesDirect advantage researcher not having to identify salient attributesDirect disadvantages cri

12、teria influenced by brands being evaluated, an attribute could be masked and it may be difficult to label the spatial mapDerived advantages easy to identify respondents with homogenous perceptions, easier to label the dimensionsDerived disadvantage researcher must identify all salient attributesDire

13、ct approaches more frequently used but they can be used in complementary mannerObtain Input Data (continued)Preference Data:Order the brand in terms of respondents preferences for some propertyRankingsPaired comparisonsPreference ratings for various brandsWhen used to construct a spatial map distanc

14、e implies differences in preferenceConfiguration may differ greatly from that obtained from similarity dataTwo brands may be perceived as different in a similarity map but similar in a preference map, and vice versaSelect an MDS ProcedureDepends upon whether perception or preference data, or both, a

15、re being collectedNature of input data also relevant:Non-metric MDS assumes input is ordinal but result in metric outputMetric MDS assumes input is metric stronger relationship between input and output obtainedAnother factor whether analysis at the individual or aggregate levelDecide on the Number o

16、f DimensionsObjective is to obtain a spatial map that best fits the input data in the smallest number of dimensionsBut fit improves as number of dimensions increase compromise neededFit assessed by a stress measure:Higher value indicates poor fitDecide on the Number of Dimensions (continued)Suggeste

17、d guidelines for determining the dimension numbers:A priori knowledgeInterpretability of the spatial map difficult if more than three dimensionsElbow criterionPlot of stress versus dimensionality (Fig. 17.3)Ease of useStatistical approachesFigure 17.4: Spatial Map of Toothpaste BrandsLabel the Dimen

18、sions and Interpret the ConfigurationAfter spatial map configured the dimensions must be labelled and interpretedLabelling is subjective guidelines:Regression can be used for deriving axes which are then labelledRespondents criteria subjectively related to the mapAsk respondents to label the mapsUse

19、 objective characteristics of the brand eg, horsepowerSee Figure 17.5 p. 655Label the Dimensions and Interpret the Configuration (continued)Dimensions can represent more than one attributeInterpret by examining the coordinates and relative positions of the brands:Brands located near each other are c

20、loser rivalsIsolated brand has a unique imageBrands further along the descriptor are stronger on that characteristicSpatial map gaps may represent an opportunityFigure 17.5: Using Attribute Vectors to Label DimensionsAssess Reliability and ValiditySuggested guidelines:Index of fit (R-square) indicat

21、es proportion of variance accounted for by MDSValues .6 plus considered acceptableStress values indicate badness-of-fit proportion of variance not accounted forIf aggregate analysis done then split the data into a number of parts and repeat MDS on all parts and compare resultsAssess Reliability and

22、Validity (continued)Stimuli can be selectively eliminated and new solution determinedRandom error term can be added to the input data, conduct MDS and compare resultsInput data collected at two different time points and the test-retest reliability determinedFormal procedures available for validityTo

23、pic Basic Concepts in Multidimensional ScalingConducting Multidimensional ScalingAssumptions and Limitations of MDSScaling Preference DataMapping Consumer Perceptions with SPSSBasic Concepts in Conjoint AnalysisConducting Conjoint AnalysisAssumptions and Limitations of Conjoint AnalysisHybrid Conjoi

24、nt AnalysisComputer ApplicationsSummaryAssumptions and Limitations of MDSAssumes:Similarity of stimulus A to B is the same as B to AThe distance between two stimuli is some function of their partial similarities on each of the several perceptual dimensionsInterpoint scales are ratio scaled and that

25、the axes are interval scaledLimitations:Dimension interpreting relating physical changes in stimuli to changes in the perceptual map is difficultTopic Basic Concepts in Multidimensional ScalingConducting Multidimensional ScalingAssumptions and Limitations of MDSScaling Preference DataMapping Consume

26、r Perceptions with SPSSBasic Concepts in Conjoint AnalysisConducting Conjoint AnalysisAssumptions and Limitations of Conjoint AnalysisHybrid Conjoint AnalysisComputer ApplicationsSummaryScaling Preference DataAnalysis can be:Internal method of configuring a spatial map such that the map represents b

27、oth brands/stimuli and respondent points or vectors and is derived solely from preference dataExternal method of configuring a spatial map such that the ideal points or vectors based on preference data are fitted in a spatial map derived from perception dataExternal analysis is preferredSee Figure 1

28、7.1 and Example 17.3 pp. 658-9 for an illustration of external analysisTopic Basic Concepts in Multidimensional ScalingConducting Multidimensional ScalingAssumptions and Limitations of MDSScaling Preference DataMapping Consumer Perceptions with SPSSBasic Concepts in Conjoint AnalysisConducting Conjo

29、int AnalysisAssumptions and Limitations of Conjoint AnalysisHybrid Conjoint AnalysisComputer ApplicationsSummaryMapping Consumer Perceptions with SPSSRefer to pp. 659-63 for an illustration of obtaining a perception map of nine luxury car brandsTopic Basic Concepts in Multidimensional ScalingConduct

30、ing Multidimensional ScalingAssumptions and Limitations of MDSScaling Preference DataMapping Consumer Perceptions with SPSSBasic Concepts in Conjoint AnalysisConducting Conjoint AnalysisAssumptions and Limitations of Conjoint AnalysisHybrid Conjoint AnalysisComputer ApplicationsSummaryBasic Concepts

31、 in Conjoint AnalysisAttempts to determine the relative importance consumers attach to salient attributes and the utilities they attach to the levels of attributesInformation from consumers subjective evaluations of brandsStimuli are combinations of attribute levels determined by the researcherMDS &

32、 conjoint analysis are complementaryBasic Concepts in Conjoint Analysis (continued)Marketing applications:Determining the relative importance of attributes in the consumer choice processEstimating market share of brands that differ on attributesDetermining the composition of the most preferred brand

33、Segmentation based on similarity of preferences for attribute levelsTopic Basic Concepts in Multidimensional ScalingConducting Multidimensional ScalingAssumptions and Limitations of MDSScaling Preference DataMapping Consumer Perceptions with SPSSBasic Concepts in Conjoint AnalysisConducting Conjoint

34、 AnalysisAssumptions and Limitations of Conjoint AnalysisHybrid Conjoint AnalysisComputer ApplicationsSummaryConducting Conjoint AnalysisFormulate the ProblemMust identify the attributes and their levelsAttributes should be salient in influencing consumer preferenceNext salient attribute levels:May

35、be limited to assist respondent evaluationLevels may be non-linearGuided by marketplace levels somewhat largerConjoint analysis will be illustrated by examining how students evaluate running shoesTable 17.3: Attributes and Levels of Running ShoesConstruct the StimuliTwo broad approaches:Pairwise (tw

36、o-factor)Evaluate two attributes at a time (Figure 17.17)Advantage is that it is easier for respondents to provide judgmentsDisadvantage more evaluations neededFull-profile (multiple-factor)Full profiles constructed for all attributesCan reduce the number of stimulus profilesMore common approachAppr

37、oach adopted for this running shoe exampleSee Table 17.4 p. 667 for an illustration of reducing 27 profiles to nineFigure 17.17: Pairwise Approach to Collecting Conjoint DataTable 17.4: Full-profile Approach to Collecting Conjoint DataDecide on the Form of Input DataNon-metric:Gather rank-order eval

38、uationsMetric:Gather ratingsJudgments are independently madeDependent variable is usually preference or intention to buy but can include actual purchase and choiceTable 17.5: Running Shoe Profiles and Their RatingsSelect a Conjoint Analysis ProcedureThe mathematical model expressing the fundamental

39、relationship between attributes and utilitySee pp. 668-70 for a fuller description of the model and proceduresTable 17.7: Results of Conjoint AnalysisEstimation of part-worths and relative importance weights provides the basis for interpretationInterpret the ResultsInterpret the Results (continued)P

40、reference for rubber sole and leather upperInterpret the Results (continued)Preference for $30 as a price and price is the most important attributeAssess Reliability and ValidityProcedures:Goodness of fit of the model R2Test-retest respondents asked to evaluate certain attributes at a later stage va

41、lues of these stimuli then correlatedHold-out stimuli evaluations correlated with those from the respondentsIf aggregate-level analysis, split the sample and conduct conjoint on all sub-samples and compareR2 of .934 for data of Table 17.5 indicates a good fit. Correlation coefficient of .95 (sig. at

42、 .05) indicates good predictive abilityTopic Basic Concepts in Multidimensional ScalingConducting Multidimensional ScalingAssumptions and Limitations of MDSScaling Preference DataMapping Consumer Perceptions with SPSSBasic Concepts in Conjoint AnalysisConducting Conjoint AnalysisAssumptions and Limi

43、tations of Conjoint AnalysisHybrid Conjoint AnalysisComputer ApplicationsSummaryAssumptions and Limitations of Conjoint AnalysisAssumes:The importance attributes can be identifiedConsumers evaluate the choice alternatives using these attributes and make trade-offsLimitations:The trade-off model may

44、not be a good representation of the choice processData collection may be complexMitigated by Hybrid conjoint analysisMay not be so with brandsTopic Basic Concepts in Multidimensional ScalingConducting Multidimensional ScalingAssumptions and Limitations of MDSScaling Preference DataMapping Consumer P

45、erceptions with SPSSBasic Concepts in Conjoint AnalysisConducting Conjoint AnalysisAssumptions and Limitations of Conjoint AnalysisHybrid Conjoint AnalysisComputer ApplicationsSummaryHybrid Conjoint AnalysisA form of conjoint analysis that can simplify the data collection task and estimate selected

46、interactions as well as all main effectsRespondents evaluate a limited number of stimuli (usually nine)Respondents evaluate different setsEvaluate relative importance of and desirability of the attributesMDS and conjoint are complementary See Example 17.4 p. 674Topic Basic Concepts in Multidimensional ScalingConducting Multidimensional Scal