Multidimensional scaling & Conjoint Analysis
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The document discusses multidimensional scaling (MDS) and conjoint analysis, which are techniques used in marketing research. MDS is used to identify dimensions that objects are perceived by and position objects with respect to those dimensions. Conjoint analysis determines the relative importance of product attributes based on how consumers make trade-off judgments between different attribute combinations. Both techniques provide insights to help with product positioning and development.
Multidimensional scaling & Conjoint Analysis
- 1. Multidimensional Scaling and Conjoint Analysis By: Omer Maroof MBA: 3rd Sem……. Enroll: 110130 1
- 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 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. 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. 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. Perceptual Map of a Beverage Market 6 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
- 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. 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. 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. 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. 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. Similarity Judgments 12 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
- 13. Perceptual Map Using Similarity Data 13 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
- 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. 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. 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. 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. 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. 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. Steps in Conjoint Analysis 20 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
- 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. Utilities for Credit Card Attributes (Contd.) 22 Marketing Research http://www.drvkumar.com/mr10/ 10th Edition
- 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. 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. 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. 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. 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. 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. Marketing Research http://www.drvkumar.com/mr10/ 10th Edition