Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
History of marketing wikipedia , lookup
Services marketing wikipedia , lookup
Advertising campaign wikipedia , lookup
Marketing plan wikipedia , lookup
Marketing mix modeling wikipedia , lookup
Networks in marketing wikipedia , lookup
Global marketing wikipedia , lookup
1 Marketing Research Aaker, Kumar, Day and Leone Tenth Edition Instructor’s Presentation Slides 2 Chapter Twenty-two Multidimensional Scaling and Conjoint Analysis http://www.drvkumar.com/mr10/ Marketing Research 10th Edition 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/mr10/ Marketing Research 10th Edition 4 Approaches To Creating Perceptual Maps Perceptual map Attribute data Nonattribute data Preference Similarity Factor analysis http://www.drvkumar.com/mr10/ Correspondence analysis Discriminant analysis MDS Marketing Research 10th Edition 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/mr10/ Marketing Research 10th Edition 6 Comparison of Factor and Discriminant Analysis Discriminant Analysis Factor Analysis • Identifies clusters of attributes on which objects differ • Groups attributes that are similar • Identifies a perceptual dimension even if it is represented by a single attribute • Based on both perceived differences between objects and differences between people's perceptions of objects • Statistical test with null hypothesis that two objects are perceived identically • Dimensions provide more interpretive value than discriminant analysis http://www.drvkumar.com/mr10/ Marketing Research 10th Edition 7 Perceptual Map of a Beverage Market http://www.drvkumar.com/mr10/ Marketing Research 10th Edition 8 Perceptual Map of Pain Relievers Gentleness . Tylenol . Bufferin Effectiveness . Bayer . Private-label aspirin http://www.drvkumar.com/mr10/ . Anacin . Advil . Nuprin . Excedrin Marketing Research 10th Edition 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/mr10/ Marketing Research 10th Edition 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/mr10/ Marketing Research 10th Edition 11 Attribute-based MDS Advantages Disadvantages • Attributes can have diagnostic and operational value • If the list of attributes is not accurate and complete, the study will suffer • Attribute data is easier for the respondents to use • Respondents may not perceive or evaluate objects in terms of underlying attributes • Dimensions based on attribute data predicted preference better as compared to non-attribute data http://www.drvkumar.com/mr10/ • May require more dimensions to represent them than the use of flexible models Marketing Research 10th Edition 12 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/mr10/ Marketing Research 10th Edition 13 Similarity Judgments http://www.drvkumar.com/mr10/ Marketing Research 10th Edition 14 Perceptual Map Using Similarity Data http://www.drvkumar.com/mr10/ Marketing Research 10th Edition 15 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/mr10/ Marketing Research 10th Edition 16 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/mr10/ Marketing Research 10th Edition 17 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 http://www.drvkumar.com/mr10/ Marketing Research 10th Edition 18 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/mr10/ Marketing Research 10th Edition 19 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/mr10/ Marketing Research 10th Edition 20 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/mr10/ Marketing Research 10th Edition 21 Steps in Conjoint Analysis 1 2 3 • Choose product attributes (e.g. size, price, model) • Choose the values or options for each attribute • Define products as a combination of attribute options 4 • A value of relative utility is assigned to each level of an attribute called partworth utilities 5 • The combination with the highest utilities should be the one that is most preferred http://www.drvkumar.com/mr10/ Marketing Research 10th Edition 22 Utilities for Credit Card Attributes Source: Paul E. Green, ‘‘A New Approach to Market Segmentation,’’ http://www.drvkumar.com/mr10/ Marketing Research 10th Edition 23 Utilities for Credit Card Attributes (Contd.) http://www.drvkumar.com/mr10/ Marketing Research 10th Edition 24 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/mr10/ Marketing Research 10th Edition 25 Conjoint Analysis - Example http://www.drvkumar.com/mr10/ Make Price MPG Door 0 Domestic $22,000 22 2-DR 1 Foreign $18,000 28 4-DR 25 Marketing Research 10th Edition 26 Conjoint Analysis – Regression Output Model Summaryc Model 1 R R Square .785 b .616 Adjus ted R Square .488 Std. Error of the Es timate 6.921 b. Predictors : Door, MPG, Price, Make c. Dependent Variable: Rank ANOVAc Model 1 Sum of Squares 921.200 574.800 1496.000 Regress ion Res idual Total df 4 12 16 Mean Square 230.300 47.900 F 4.808 Sig. .015 a a. Predictors : Door, MPG, Price, Make c. Dependent Variable: Rank Coefficientsa, b Model 1 Make Price MPG Door Uns tandardized Coefficients B Std. Error 1.200 3.095 4.200 3.095 5.200 3.095 2.700 3.095 Standardized Coefficients Beta .088 .307 .380 .197 t .388 1.357 1.680 .872 Sig. .705 .200 .119 .400 a. Dependent Variable: Rank b. Linear Regres sion through the Origin http://www.drvkumar.com/mr10/ Marketing Research 10th Edition 27 Part-worth Utilities 1.4 4.5 1.2 4 3.5 3 Utility Utility 1 0.8 0.6 2.5 2 1.5 0.4 1 0.5 0.2 0 0 Foreign Domestic 18,000 Price 6 3 5 2.5 4 2 Utility Utility Make 3 1.5 2 1 1 0.5 0 0 28 22 4-Dr MPG http://www.drvkumar.com/mr10/ 22,000 2-Dr Door 27 Marketing Research 10th Edition 28 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% http://www.drvkumar.com/mr10/ 28 Marketing Research 10th Edition 29 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/mr10/ Marketing Research 10th Edition