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Multidimensional scaling & Conjoint Analysis

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Multidimensional scaling & Conjoint Analysis

  1. 1. Multidimensional Scaling and Conjoint Analysis By: Omer Maroof MBA: 3rd Sem……. Enroll: 110130 1
  2. 2. 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 2 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  3. 3. 3 Approaches To Creating Perceptual Maps Perceptual map Attribute data Nonattribute data Similarity Preference Correspondence analysis Discriminant MDS analysis Factor analysis Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  4. 4. 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 4 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  5. 5. Comparison of Factor and Discriminant Analysis Discriminant Analysis Factor 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 • 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 5 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  6. 6. Perceptual Map of a Beverage Market 6 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  7. 7. 7 Perceptual Map of Pain Relievers Gentleness . Tylenol . Bufferin Effectiveness . Advil . Nuprin . Excedrin . Private-label aspirin . Bayer . Anacin Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  8. 8. 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 8 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  9. 9. Determining Number of Dimensions 9 Due to large increase in the stress values from two dimensions to one, two dimensions are acceptable Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  10. 10. 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 10 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 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  11. 11. Application of MDS With Nonattribute Data Similarity Data 11 • 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 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  12. 12. Similarity Judgments 12 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  13. 13. Perceptual Map Using Similarity Data 13 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  14. 14. 14 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 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  15. 15. Issues in MDS • Perceptual mapping has not been shown to be reliable across different methods 15 • 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 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  16. 16. 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 • 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 16 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  17. 17. 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 17 • Respondents make judgments about the concept either by considering ▫ Two attributes at a time - Trade-off approach ▫ Full profile of attributes - Full profile approach Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  18. 18. 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 18 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  19. 19. 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 19 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  20. 20. Steps in Conjoint Analysis 20 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  21. 21. Utilities for Credit Card Attributes 21 Source: Paul E. Green, ‘‘A New Approach to Market Segmentation,’’ Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  22. 22. Utilities for Credit Card Attributes (Contd.) 22 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  23. 23. Full-profile and Trade-off Approaches 23 Source: Adapted from Dick Westwood, Tony Lunn, and David Bezaley, ‘‘The Trade-off Model and Its Extensions’’ Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  24. 24. Conjoint Analysis - Example Make Price MPG Door 0 Domestic $22,000 22 2-DR 1 Foreign $18,000 28 4-DR 24 24 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  25. 25. Conjoint Analysis – Regression Output 25 Model Summaryc R R Square Adjusted R Square Std. Error of the Estimate .785b .616 .488 6.921 Model 1 b. Predictors: Door, MPG, Price, Make c. Dependent Variable: Rank Model 1 a. Predictors: Door, MPG, Price, Make c. Dependent Variable: Rank Coefficientsa,b Unstandardized Coefficients B Std. Error Regression Residual Total Standardized Coefficients Beta Sum of Squares df Mean Square F Sig. 921.200 4 230.300 4.808 .015a 574.800 12 47.900 1496.000 16 t Sig. 1.200 3.095 .088 .388 .705 4.200 3.095 .307 1.357 .200 5.200 3.095 .380 1.680 .119 2.700 3.095 .197 .872 .400 Make Price MPG Door Model 1 a. Dependent Variable: Rank b. Linear Regression through the Origin ANOVAc Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  26. 26. Part-worth Utilities 1.4 1.2 1 0.8 0.6 0.4 0.2 0 Foreign Domestic Make Utility 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 18,000 22,000 Price Utility 6 5 4 3 2 1 0 28 22 MPG Utility 3 2.5 2 1.5 1 0.5 0 4-Dr 2-Dr Door Utility 26 26 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  27. 27. Relative Importance of Attributes Attribute Part-worth Utility Relative Importance Make 1.2 9% Price 4.2 32% MPG 5.2 39% Door 2.7 20% 27 27 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  28. 28. 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 28 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
  29. 29. Marketing Research http://www.drvkumar.com/mr10/ 10th Edition

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