Internal and External Preference Mapping: Understanding Market

Chapter 18

Downloaded by EAST CAROLINA UNIV on September 24, 2015 | http://pubs.acs.org Publication Date: July 19, 2002 | doi: 10.1021/bk-2002-0825.ch018

Internal and External Preference Mapping: Understanding Market Segmentation and Identifying Drivers of Liking Jean-Xavier Guinard Department of Food Science and Technology, University of California, Davis, CA 95616

Traditional methods relating consumer and sensory data (e.g., response surface methodology, the regression method) regress averaged hedonic ratings onto mean analytical ratings. B y contrast, preference mapping techniques examine the preferences of each consumer. Internal preference mapping analyzes hedonic ratings by consumers for a product set by principal component analysis (PCA) of the covariance matrix, and provides a summary of the main preference directions. Using a number of regression models (from linear to quadratic ones), external preference mapping regresses the preferences of each consumer onto the first two principal components of a P C A o f the products' sensory characteristics (derived from descriptive analysis or instrumental measurements). A case study with vanilla ice cream shows that consumer preferences can vary broadly for a given product type and that different regression models are required to relate individual consumer preferences to product sensory characteristics.

© 2002 American Chemical Society In Chemistry of Taste; Given, P., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2002.

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Introduction Successful product optimization requires good knowledge of the market and a solid understanding of which variables drive consumer liking. Among those variables that drive consumer liking are sensory attributes of the product (so-called 'drivers of liking'). To identify those, product developers have traditionally related consumer and sensory descriptive or instrumental data for a set of products using methods such as the Regression Method (7), stepwise and/or multiple regression, and Response Surface methodology (RSM) (2). These methods regress averaged hedonic ratings onto mean analytical ratings for a product set. By taking averaged hedonic ratings as the dependent variable, these methods assume that all consumers exhibit the same behavior, and that a single mean value is representative of all the consumers. Consumers actually are heterogeneous in their likes and dislikes of most products. This is called 'market segmentation'. It results that conclusions drawn from averaged hedonic ratings across the consumer population may not be accurate. Furthermore, traditional methods relating consumer and sensory data such as the Regression Method (/) assume a linear relationship between degree of liking and perceived intensity of a sensory attribute. While it may occur with some attributes, in most instances, the relation between liking and attribute intensity is curvilinear with an inverted-U shape, and a polynomial regression with a quadratic term is required to describe it. Finally, consumers have a limited vocabulary to describe their sensory perceptions of products. They typically produce comments about products which are hedonically based rather than related to specific attributes, so that a product developer is at a loss when attempting to have the consumer define what it is about the product that leads him to like or dislike it. Indirect methods (that regress consumer data onto sensory data) are therefore required to get at why a product is liked or disliked by a consumer. To remedy the limitations of traditional methods and to get at market segmentation and sensory drivers of liking, a variety of techniques have recently been developed that examine the preferences of each consumer, and in some instances relate them to product characteristics, by regressing hedonic ratings onto a set of analytical (sensory or instrumental) variables. These techniques include internal and external preference mapping (3, 4, 5, 6, 7, 8, 9, 10), multiple factor analysis (11, 12) and partial least squares regression (13). They require that hedonic ratings by consumers and descriptive ratings by experts

In Chemistry of Taste; Given, P., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2002.

229 and/or instrumental measurements be collected for a set o f products representative of the segment under study. We will use a case study with vanilla ice cream to illustrate the principles of internal and external preference mapping. Vanilla ice cream was manufactured according to a 3 factorial design with 8, 13 or 18% sucrose and 10, 14 or 18% butterfat for a total of 9 samples that met ice cream standards of identity and were representative of commercially-available products, as shown in Table I.


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Table I - Composition of the ice cream samples. Products

PI P2 P3 P4 P5 P6 P7 P8 P9

Sugar (%w/w)

Fat (%w/w)

Total Solids (%w/w)

8.94 10.85 11.61 13.65 13.54 13.29 17.65 18.81 17.91

8.73 14.28 17.68 9.94 14.99 18.75 11.40 15.08 19.30

32.49 39.90 45.29 39.32 43.95 47.31 44.15 49.19 53.16

The sensory properties of the ice creams were measured by a trained panel of 15 judges, using a modified Quantitative Descriptive Analysis (QDA) method (14). Fifteen attributes of appearance (color), flavor (milky/dairy, buttery, vanilla, custard/eggy, almond, sweetness, coolness/cooling) and texture/mouthfeel (melting rate, fatty, creamy, fluffy/aerated, doughy/pasty/elastic, ice crystals, mouthcoating afterfeel) were evaluated in duplicate across the samples. A consumer test was carried out with 146 users and likers of ice cream, 73 each men and women. Consumers tasted the 9 ice cream samples in one session and rated their overall degree of liking and degree of liking of the texture/mouthfeel and of the flavor of the samples on the 9-point hedonic scale from 1= 'dislike extremely' to 9= 'like extremely', with 5= 'neither like nor dislike' (15).


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Internal Preferenee Mapping


Internal preference mapping is a principal component analysis of the matrix of consumer hedonic ratings across the products, based on the covariance matrix. In that P C A , the consumers are the variables, and the products are the objects. The outcome of the analysis is a biplot of the consumers (the internal preference map) and a corresponding biplot of the products. Figures 1 and 2 show the distribution of the consumers and of the products in the internal preference map for the ice cream data. The combination of PCs 1 and 2 accounted for 48.8% of the variance in the data. The main conclusion to be drawn from that internal map is that consumer preferences were spread over 180 degrees on the biplot, with two clusters of consumers in the upper-left and lower-left quadrants. The first cluster liked P7, P8 and P9 - the high-sugar samples - the most. It can be characterized as a segment of the population that will favor highly-sweet vanilla ice cream, regardless of its fat content. The second cluster clearly liked P5 the most. P5 had medium sugar and medium fat contents. In this instance, the manufacturer would have to decide between marketing an ice cream like P5 with medium levels of sugar and fat (a decision he would have made had he/she consulted only the mean hedonic of the ice creams across all consumers - P5 had the highest), or manufacturing and marketing two types of vanilla ice cream, each targeting one of the two market segments. If electing to produce an ice cream to please the upper-left cluster, the manufacturer would need to make an ice cream high in sugar, yet different from P7, P8 and P9 in that it would need to be in the upper-left corner of the biplot. The product developer must then resort to external preference mapping to figure out what the characteristics of that ideal product should be (besides high sweetness). The first two principal components in an internal preference map may only account for a limited amount of the variance in the data (in this example, 49%). Indeed, the analysis reduces the variation in the data from as many original dimensions as there are consumers (146 in this case) to only two dimensions... That means that some information is lost, but the main preference directions usually are identified.

Internal Preference Clustering The best way to uncover and characterize market segmentation in the hedonic ratings of a consumer population is to carry out a preference clustering


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Rane1-2

-I

+ _

μ^, Axisl

Figure 1. Internal Preference map matrix of hedonic ratings for the 9 ice cream samples showing the consumers (n=146).


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Plane 1 - 2 Axis 2


P9

P7

H

!

1

1

h

4

P2μ-·

j

1

1

1

.

l->

P6 P1 P4 PS

P3

Figure 2. Internal Preference map matrix of hedonic ratings for the 9 ice cream samples showing the products (n=9).


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analysis. On the same matrix of consumer ratings across a set of products, one can run a cluster analysis which readily identifies segments or clusters in the population on the basis of similarity of their likes and dislikes for the products. A cluster analysis of the matrix of hedonic ratings for the ice cream samples confirmed the existence of two main clusters of 42 and 53 consumers, respectively, corresponding to the upper- and lower-left quadrants in Figure 1. Once market segments (clusters) have been identified, one can obtain mean hedonic ratings of the samples for each cluster, and confirm which samples are most- and least-liked by that cluster of consumers.

External Preference Mapping With external preference mapping, the hedonic ratings for each consumer are regressed onto the product coordinates obtained from the multivariate analysis (e.g., principal component analysis) of the sensory descriptive or instrumental data. The models used to regress the hedonic ratings onto these coordinates may be linear or involve squared and/or interactive terms. They are the vectorial, circular, elliptical (with maximum or saddle point) and quadratic models (10). The equation relating D O L (Y) for a consumer to PCI ( X ^ and PC2 ( X ) of a P C A of the descriptive or instrumental data therefore ranges from a simple, linear one, e.g., 2

Y = a + bX, + c X (vectorial) 2

To a complex, second-order one with quadratic and cross-product effects, e.g., 2

Y = a + bX, + c X + d X , + e X 2

2 2

+ f X , X (quadratic). 2

The first step is to carry out a principal component analysis of the descriptive analysis data, again based on the covariance matrix. The use of the correlation matrix would only be warranted i f the variables in the matrix had different units of measurements. That would be the case i f a matrix of instrumental measurements were used. The P C A of the descriptive analysis data for the ice cream samples is shown in Figure 3. The first two PCs accounted for 92.3% of the variance in the data. The ice cream samples differed along a dimension (PCI) which contrasted the attributes doughy, fatty, sweet and creamy, with the attributes ice crystals and cooling. PI and P2 are found at the right end of that dimension, whereas P8


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Plane 1 - 2

Figure 3. Principal component analysis (PCA) of the matrix of mean descriptive ratings acrosee the 9 icecream samples



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and P9 are found at the left end. The second dimension (PC2) contrasted a yellowish color with a fluffy mouthfeel and high melting properties, but it did not quite separate the products because of its limited contribution to the overall variance (4.5%). The external preference map shows how the preferences of each consumer relate to the two dimensions (PCI and PC2) of the sensory map described above. Examples of consumers fitted by vectorial, elliptical and quadratic models are shown in Figures 4, 5 and 6, respectively. There are two ways to display an external preference map. One is to show all the consumers fitted by one of the models - in this example, 104 out of 146 consumers. In Figure 7, the consumers whose preferences were fitted by one of the regression models are displayed with their best model. Straight lines represent vectorial models; triangles represent circular models (with positive ideal point for those triangles pointing up, and negative ideal points for those pointing down); elliptical models are shown as X ' s ; and quadratic models are shown as squares. It can be seen that most of the fitted consumers are fitted best by the vectorial model. And a majority of those consumers' preferences point to the center, bottom of the map, an area not covered by any of the 9 samples in the design, which would be that of an ice cream high in the fluffy/aerated and melting properties and off-white in color (as per Figure 3). Another way to show the results of the analysis is to display a response surface of number of 'satisfied consumers' on the sensory map of PCI and PC2 as shown in Figure 8. For a given point on the sensory map (a hypothetical product), one can view how many consumers would have given this product a hedonic rating higher than the mean hedonic rating for the set of 9 samples plus 50% of the range of ratings. But the criterion for inclusion in the response surface may be anything set by the analyst (number of consumers who would give a hedonic rating above 7 to the product on the 9-point hedonic scale, for example). From Figure 8, it appears that the area of the sensory map that would satisfy the majority of consumers is the lower-left corner of the map, with coordinates of-8.5 on PCI and -3.0 on PC2. External preference mapping requires a high number of products, to achieve sufficient power for quadratic and cross-product regression. A minimum of 6 products is required, but we recommend at least 10 products to run a meaningful analysis. The selected products should also provide adequate representation of the sensory characteristics of the product segment. If preference mapping is being carried out for reformulation purposes, we recommend designing a sample set that includes both existing (commercial) products and prototypes. This is an excellent way to find out whether the prototypes meet the needs/preferences of consumers, or at least of the main consumer segment, as shown by preference mapping or clustering.



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Figure 4. Example of a consumerfittedby a vectorial model



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Figure 5. Example of a consumer fitted by an elliptical model



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Figure 6. Example of a consumer fitted by a quadratic model




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Figure 8. Example of an external preference map showing response of "satisfied" consumers


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One limitation of external preference mapping is that some consumers may not be fitted by any of the models. This occurs for a variety of reasons. A low number of samples means less degrees of freedom for the regression models. By increasing the number of samples in the design and the power of the regressions, more consumers are usually fitted by a model. Mathematical models may not always fit actual behavior. There clearly are some consumers whose degree of liking for a set of products does not follow any preset predictice model. Even though some information is lost with 'unfitted consumers', the main market segments and drivers of liking are sorted out with external preference mapping. The question of whether principal components from a P C A of descriptive or instrumental data are 'actionable' is often raised. Is the manufacturer, in the process of optimizing the sensory quality of a product, able to adjust the product formulation on the basis of principal components from a P C A of sensory or instrumental data? The answer typically is 'yes' because these principal components usually relate to ingredient or process variations (which can be acted upon). There may even be cases when principal components are more actionable than a set of individual sensory attributes which have been identified as drivers of liking.

Conclusions Internal and external preference mapping, and internal preference clustering, as applied to hedonic ratings by consumers and descriptive ratings by trained judges for a set of 9 ice cream samples varying in fat and sugar, proved useful methods for uncovering market segmentation and identifying ways to optimize product sensory quality to satisfy a majority of consumers. Additional information about the consumers, such as that gathered from uses and attitudes ( U & A ) measures, may be factored into these analyses to characterize the market segments in terms of consumer characteristics. Furthermore, individual sensory attributes may also be confirmed as drivers of liking by regressing mean liking for a market segment onto each sensory attribute using a polynomial regression, and examining the curvature of the ensuing function. Knowing what characterizes a market segment and where its preferences lie in terms of product sensory characteristics, makes for a very powerful product optimization tool.


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Internal and External Preference Mapping: Understanding Market

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