1. Marketing Research Aaker, Kumar, Day Seventh Edition Instructor’s Presentation Slides

2. Chapter Twenty-Two Multidimensional Scaling and Conjoint Analysis

3. © Marketing Research 7th EditionAaker, Kumar, Day Multidimensional Scaling Used to Identify dimensions by which objects are perceived or evaluated Position the objects with respect to those dimensions Make positioning decisions for new and old products

4. © Marketing Research 7th EditionAaker, Kumar, Day Approaches to Create Perceptual Maps Attribute based approaches Non attribute based approaches

5. © Marketing Research 7th EditionAaker, Kumar, Day Perceptual map Attribute data Nonattribute data SimilarityPreference Correspondence analysis MDS Discriminant analysis Factor analysis Approaches To Creating Perceptual Maps

6. © Marketing Research 7th EditionAaker, Kumar, Day Attribute Based Approaches If MDS used on attribute data, it is known as attribute based MDS Assumption The attributes on which the individuals' perceptions of objects are based, can be identified Methods Used to Reduce the Attributes to a Small Number of Dimensions Factor Analysis Discriminant Analysis

7. © Marketing Research 7th EditionAaker, Kumar, Day Basic Concepts of Multidimensional Scaling(MDS) MDS uses proximities among different objects as input (proximity is a value which denotes how similar or how different two objects, are perceived to be) MDS uses this proximities data to produce a geometric configuration of points (objects), in a two-dimensional space as output

8. © Marketing Research 7th EditionAaker, Kumar, Day Evaluating the MDS Solution The fit between the derived distances and the two proximities in each dimension is evaluated through a measure called stress The appropriate number of dimensions required to locate the objects can be obtained plotting the stress values against the number of dimensions

9. © Marketing Research 7th EditionAaker, Kumar, Day Advantages of Attribute- based MDS Attributes can have diagnostic and operational value Attribute data is easier for the respondents to use Dimensions based on attribute data predicted preference better as compared to non-attribute data

10. © Marketing Research 7th EditionAaker, Kumar, Day Disadvantages of Attribute- based MDS If the list of attributes is not accurate and complete, the study will suffer accordingly Respondents may not perceive or evaluate objects in terms of underlying attributes May require more dimensions to represent them than the use of flexible models

11. © Marketing Research 7th EditionAaker, Kumar, Day Application of MDS With Nonattribute Data Similarity Data Reflect the perceived similarity of two objects from the respondents' perspective Perceptual map is obtained from the average similarity ratings The power of the technique lies in the ability 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 space

12. © Marketing Research 7th EditionAaker, Kumar, Day Application of MDS With Nonattribute Data (Contd.) Preference Data An ideal object is the combination of all customers' preferred attribute levels Location of ideal objects is to identify segments of customers who have similar ideal objects, since customer preferences are always heterogeneous

13. © Marketing Research 7th EditionAaker, Kumar, Day Issues in MDS Perceptual mapping has not been shown to be reliable across different methods The effect of market events on the perceptual maps cannot be ascertained The interpretation of dimensions is difficult When more than two or three dimensions are needed, the usefulness is reduced

14. © Marketing Research 7th EditionAaker, Kumar, Day Conjoint Analysis An extremely powerful and useful analysis tool Used to determine the relative importance of various attributes to respondents, based on their making trade-off judgments Useful in Helping to select features on a new product/service Predicting sales Understanding relationships

15. © Marketing Research 7th EditionAaker, Kumar, Day Input The dependent variable is the preference judgment that a respondent makes about a new concept The independent variables are the attribute levels that need to be specified Respondents make judgments about the concept either by considering Two attributes at a time Trade-off approach Full profile of attributes Full profile approach

16. © Marketing Research 7th EditionAaker, Kumar, Day Output A value of relative utility is assigned to each level of an attribute called partworth utilities The combination with the highest utilities should be the one that is most preferred And the combination with the lowest total utility is the least preferred

17. © Marketing Research 7th EditionAaker, Kumar, Day Limitations In the trade-off approach, the task is too unrealistic Trade-off judgments is being made on two attributes, holding the others constant In the full-profile approach, the task can get very demanding, if there are multiple attributes and attribute levels