Multidimensional Scaling and Conjoint Analysis

Marketing ResearchAaker, Kumar, Leone and Day Twelfth EditionInstructor’s Presentation Slides1Chapter Twenty-One2Multidimensional Scaling and Conjoint AnalysisMarketing Research 12th Edition Multidimensional ScalingUsed to: Identify dimensions by which objects are perceived or evaluated Position the objects with respect to those dimensionsMake positioning decisions for new and old products3Marketing Research 12th Edition 4Perceptual mapAttribute dataNonattribute dataSimilarityPreferenceCorrespondence analysisMDSDiscriminant analysisFactor analysisApproaches To Creating Perceptual MapsMarketing Research 12th Edition Attribute Based ApproachesAttribute based MDS - MDS used on attribute dataAssumption The attributes on which the individuals' perceptions of objects are based can be identifiedMethods used to reduce the attributes to a small number of dimensions Factor AnalysisDiscriminant Analysis LimitationsIgnore the relative importance of particular attributes to customersVariables are assumed to be intervally scaled and continuous5Marketing Research 12th Edition Comparison of Factor and Discriminant AnalysisIdentifies clusters of attributes on which objects differIdentifies a perceptual dimension even if it is represented by a single attributeStatistical test with null hypothesis that two objects are perceived identicallyGroups attributes that are similarBased on both perceived differences between objects and differences between people's perceptions of objectsDimensions provide more interpretive value than discriminant analysis6Factor AnalysisDiscriminant AnalysisMarketing Research 12th Edition Perceptual Map of a Beverage Market7Marketing Research 12th Edition Basic Concepts of Multidimensional Scaling (MDS)MDS uses proximities (value which denotes how similar or how different two objects are perceived to be) among different objects as input Proximities data is used to produce a geometric configuration of points (objects) in a two-dimensional space as outputThe fit between the derived distances and the two proximities in each dimension is evaluated through a measure called stressThe appropriate number of dimensions required to locate objects can be obtained by plotting stress values against the number of dimensions8Marketing Research 12th Edition Determining Number of Dimensions9Due to large increase in the stress values from two dimensions to one, two dimensions are acceptableMarketing Research 12th Edition Attribute-based MDSAdvantagesAttributes can have diagnostic and operational value Attribute data is easier for the respondents to useDimensions based on attribute data predicted preference better as compared to non-attribute data10DisadvantagesIf the list of attributes is not accurate and complete, the study will suffer Respondents may not perceive or evaluate objects in terms of underlying attributesMay require more dimensions to represent them than the use of flexible modelsMarketing Research 12th Edition Application of MDS With Nonattribute DataSimilarity DataReflect the perceived similarity of two objects from the respondents' perspectivePerceptual map is obtained from the average similarity ratingsAble to find the smallest number of dimensions for which there is a reasonably good fit between the input similarity rankings and the rankings of the distance between objects in the resulting space11Marketing Research 12th Edition Similarity Judgments12Marketing Research 12th Edition Perceptual Map Using Similarity Data13Marketing Research 12th Edition Application of MDS With Nonattribute Data (Contd.)Preference DataAn ideal object is the combination of all customers' preferred attribute levelsLocation of ideal objects is to identify segments of customers who have similar ideal objects, since customer preferences are always heterogeneous14Marketing Research 12th Edition Issues in MDSPerceptual mapping has not been shown to be reliable across different methodsThe effect of market events on perceptual maps cannot be ascertained The interpretation of dimensions is difficultWhen more than two or three dimensions are needed, usefulness is reduced15Marketing Research 12th Edition Conjoint AnalysisTechnique that allows a subset of the possible combinations of product features to be used to determine the relative importance of each feature in the purchase decisionUsed to determine the relative importance of various attributes to respondents, based on their making trade-off judgmentsUses:To select features on a new product/servicePredict salesUnderstand relationships16Marketing Research 12th Edition Inputs in Conjoint AnalysisThe dependent variable is the preference judgment that a respondent makes about a new conceptThe independent variables are the attribute levels that need to be specifiedRespondents make judgments about the concept either by considering Two attributes at a time - Trade-off approachFull profile of attributes - Full profile approach17Marketing Research 12th Edition Outputs in Conjoint AnalysisA value of relative utility is assigned to each level of an attribute called partworth utilitiesThe combination with the highest utilities should be the one that is most preferred The combination with the lowest total utility is the least preferred18Marketing Research 12th Edition Applications of Conjoint AnalysisWhere the alternative products or services have a number of attributes, each with two or more levels Where most of the feasible combinations of attribute levels do not presently existWhere the range of possible attribute levels can be expanded beyond those presently availableWhere the general direction of attribute preference probably is known19Marketing Research 12th Edition Steps in Conjoint Analysis1Choose product attributes (e.g. size, price, model)2Choose the values or options for each attribute3Define products as a combination of attribute options4A value of relative utility is assigned to each level of an attribute called partworth utilities5The combination with the highest utilities should be the one that is most preferred20Marketing Research 12th Edition Utilities for Credit Card Attributes21Source: Paul E. Green, ‘‘A New Approach to Market Segmentation,’’Marketing Research 12th Edition Utilities for Credit Card Attributes (Contd.)22Marketing Research 12th Edition Full-profile and Trade-off Approaches23Source: Adapted from Dick Westwood, Tony Lunn, and David Bezaley, ‘‘The Trade-off Model and Its Extensions’’Marketing Research 12th Edition Limitations of Conjoint AnalysisTrade-off approachThe task is too unrealistic Trade-off judgments are being made on two attributes, holding the others constantFull-profile approachIf there are multiple attributes and attribute levels, the task can get very demanding24Marketing Research 12th Edition 25End of Chapter Twenty-OneMarketing Research 12th Edition