Marketing Research

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

2. Chapter Twenty-two Multidimensional Scaling and Conjoint Analysis http://www.drvkumar.com/mr9/

3. 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 http://www.drvkumar.com/mr9/

4. Approaches To Creating Perceptual Maps Perceptual map Attribute data Nonattribute data Preference Similarity Factor analysis Correspondence analysis Discriminant analysis MDS http://www.drvkumar.com/mr9/

5. Attribute Based Approaches • Attribute based MDS - MDS used on attribute data • 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 • Limitations • Ignore the relative importance of particular attributes to customers • Variables are assumed to be intervally scaled and continuous http://www.drvkumar.com/mr9/

6. Discriminant Analysis Identifies clusters of attributes on which objects differ Identifies a perceptual dimension even if it is represented by a single attribute Statistical test with null hypothesis that two objects are perceived identically Factor Analysis Groups attributes that are similar Based on both perceived differences between objects and differences between people's perceptions of objects Dimensions provide more interpretive value than discriminant analysis Comparison of Factor and Discriminant Analysis http://www.drvkumar.com/mr9/

7. Perceptual Map of a Beverage Market http://www.drvkumar.com/mr9/

8. Perceptual Map of Pain Relievers Gentleness . Tylenol . Bufferin Effectiveness . Bayer . Private-label aspirin . Advil . Nuprin . Anacin . Excedrin http://www.drvkumar.com/mr9/

9. 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 output • 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 objects can be obtained by plotting stress values against the number of dimensions http://www.drvkumar.com/mr9/

10. Determining Number of Dimensions Due to large increase in the stress values from two dimensions to one, two dimensions are acceptable http://www.drvkumar.com/mr9/

11. Attribute-based MDS Advantages • 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 http://www.drvkumar.com/mr9/

12. Attribute-based MDS (contd.) Disadvantages • If the list of attributes is not accurate and complete, the study will suffer • 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 http://www.drvkumar.com/mr9/

13. 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 • Able 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 http://www.drvkumar.com/mr9/

14. Similarity Judgments http://www.drvkumar.com/mr9/

15. Perceptual Map Using Similarity Data http://www.drvkumar.com/mr9/

16. 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 http://www.drvkumar.com/mr9/

17. Issues in MDS • Perceptual mapping has not been shown to be reliable across different methods • The effect of market events on perceptual maps cannot be ascertained • The interpretation of dimensions is difficult • When more than two or three dimensions are needed, usefulness is reduced http://www.drvkumar.com/mr9/

18. Conjoint Analysis • Technique 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 decision http://www.drvkumar.com/mr9/

19. Conjoint Analysis • Used to determine the relative importance of various attributes to respondents, based on their making trade-off judgments • Uses: • To select features on a new product/service • Predict sales • Understand relationships http://www.drvkumar.com/mr9/

20. Inputs in Conjoint Analysis • 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 http://www.drvkumar.com/mr9/

21. Outputs in Conjoint Analysis • 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 • The combination with the lowest total utility is the least preferred http://www.drvkumar.com/mr9/

22. Applications of Conjoint Analysis • Where 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 exist • Where the range of possible attribute levels can be expanded beyond those presently available • Where the general direction of attribute preference probably is known http://www.drvkumar.com/mr9/

23. Steps in Conjoint Analysis • Choose product attributes (e.g. size, price, model) • Choose the values or options for each attribute • Define products as a combination of attribute options • 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 http://www.drvkumar.com/mr9/

24. Utilities for Credit Card Attributes Source:Paul E. Green, ‘‘A New Approach to Market Segmentation,’’ http://www.drvkumar.com/mr9/

25. Utilities for Credit Card Attributes (contd.) http://www.drvkumar.com/mr9/

26. Full-profile and Trade-off Approaches Source:Adapted from Dick Westwood, Tony Lunn, and David Bezaley, ‘‘The Trade-off Model and Its Extensions’’ http://www.drvkumar.com/mr9/

27. Conjoint Analysis - Example http://www.drvkumar.com/mr9/

28. Conjoint Analysis – Regression Output http://www.drvkumar.com/mr9/

29. Part-worth Utilities http://www.drvkumar.com/mr9/

30. Relative Importance of Attributes http://www.drvkumar.com/mr9/

31. Limitations of Conjoint Analysis Trade-off approach • The task is too unrealistic • Trade-off judgments are being made on two attributes, holding the others constant Full-profile approach • If there are multiple attributes and attribute levels, the task can get very demanding http://www.drvkumar.com/mr9/