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Internal preference mapping and the issue of satiety
Food Quality and Preference 24 (2012) 67–74
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Food Quality and Preference journal homepage: www.elsevier.com/locate/foodqual
Internal preference mapping and the issue of satiety Benoît Rousseau a,⇑, Daniel M. Ennis a, Frank Rossi b a b
The Institute for Perception, Richmond, VA, USA Kraft Foods Inc., Glenview, IL, USA
a r t i c l e
i n f o
Article history: Received 19 January 2011 Received in revised form 17 August 2011 Accepted 15 September 2011 Available online 29 September 2011 Keywords: Internal preference mapping Landscape Segmentation Analysis Vectors Ideal points Satiety
a b s t r a c t Internal preference mapping (IPM) and Landscape Segmentation AnalysisÒ (LSA) are two techniques broadly used to unfold consumers’ overall product liking ratings and create spatial maps that will provide further insights on consumers’ preferences. IPM is based on a vector model while LSA involves an ideal point model. Through a simulation and the analysis of 27 market research data sets, it is shown that IPM consistently creates a hedonic dimension that prevents the identification of satiety prone attributes (intensities higher or lower than a optimal level being disliked by the consumers) on that dimension. As a result, subsequent steps taken upon generating an IPM map such as the investigation of drivers of liking, population segmentation and the estimation of optimal product profiles have also a strong likelihood of resulting in distorted results, the level of distortion being dependent on the actual configuration of the underlying structure that IPM tried to uncover. It is also shown that a technique based on ideal points such as LSA does not exhibit this systematic artifact when unfolding liking data. Consequently, sensory scientists and market researchers should use caution when interpreting and using results issued from an internal preference mapping analysis. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction The quest to understand what drives consumers’ preferences is one of the most important activities of consumer product companies. Colossal budgets are invested every year to identify the variables and characteristics that drive consumers’ liking of a particular product category. In the food, beverage and personal care industries, one aspect fundamental to those characteristics is that of ‘satiety’. Satiety refers to the fact that consumers will generally have an optimal level on each of the relevant attributes that will optimize their liking response. This idea, first outlined by Coombs (1964), is that, for instance, the sweetness of a soft drink has an optimal level and so does the hardness of a candy and the fragrance level of a room deodorizer. All of them can be ‘too weak’ or ‘too intense’. This relationship is illustrated in Fig. 1. It does make sense that a majority of consumer products’ drivers of liking (DOL) will exhibit this particular relationship with liking. Of course, a smaller number of attributes might show more of a linear relationship, such as an off-flavor which will induce lower hedonic ratings as its intensity increases. If we agree with this general ‘view of the world’, techniques that explore liking data from consumers with the objective to extract the DOLs should have the capability to identify mostly attributes ⇑ Corresponding author. Address: The Institute for Perception, 7629 Hull Street Road, Richmond, VA 23235, USA. Tel.: +1 804 675 2980; fax: +1 804 675 2983. E-mail address: [email protected] (B. Rousseau). 0950-3293/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodqual.2011.09.003
exhibiting satiety. Numerous analytical methods are available that purport to find DOLs. They often aim at uncovering additional insights, such as the profiles of optimal products and the possibility of population segmentation. Such techniques include internal and external preference mapping, partial least square regression, justabout-right scaling, Landscape Segmentation AnalysisÒ, etc. A recent publication by Meullenet, Xiong, and Findlay (2007) offers a useful overview of the analytical approaches available to the sensory evaluation and market research scientists. Because of satiety, these techniques should not simply uncover liking itself as a DOL, nor should they uncover variables that are liking surrogates, otherwise one ends up with a recommendation such as ‘‘to make a more acceptable beer, make it taste better!’’. Two techniques on which this article will focus are internal preference mapping (IPM) and Landscape Segmentation Analysis (LSA). Both use the same type of data: liking ratings by consumers on a set of products using, for instance, a 9-point hedonic scale. They aspire to extract the same information as well by ‘unfolding’ liking: create a map that will summarize how the individual consumers see the products that were evaluated. It is worth mentioning here the fundamental difference between these two techniques and others that first create a space based on product characteristics such as external preference mapping (EPM). IPM and LSA do not assume anything about the underlying product space structure and generate a spatial representation based solely on the consumer’ liking information. In contrast, EPM first uses information usually from a trained panel’s descriptive data to create a space on which
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A
F
E
B
D C
Liking
Sweetness
Optimal level
Intensity Fig. 1. Illustration of a sensory attribute’s satiety property.
the consumer information will be projected/regressed. Consequently, EPM assumes that the most obvious differences, which are driven by attributes in the first two dimensions, are the drivers of liking. While it might be the case, it is also possible that true drivers of liking be ‘hidden’ in higher dimensions. IPM and LSA do not make any assumptions about the potential DOLs. They can be seen as representing the world from the consumer’s point of view, while EPM is representing the world as the analyst sees it. The objective of this paper is to outline IPM’s ability to handle satiety and to contrast it with an ideal point model-based technique such as LSA. It is worth mentioning that other ideal point models exist, such as that described by Busing, Groenen, and Heiser (2005) and Busing, Heiser, and Cleaver (2010) which is based on individual ideals or others based on ideal distributions (Ashby & Ennis, 2002; Ennis, 1993; Ennis & Johnson, 1994; Ennis & Mullen, 1992; MacKay, 2001, 2006). Even though IPM and LSA use the same data, their respective underlying modeling approaches are very different. – IPM is a particular application of the biplot developed by Gabriel (1971). The analysis results in a multidimensional space of which the first two components are typically focused on. They outline a plane where products are represented as points and consumers as vectors. The vectors indicate liking directions for each subject, i.e., the further away a product is located in that direction, the more a given consumer will like it. – LSA is based on a similarity model developed by Ennis, Palen, and Mullen (1988) and Ennis and Johnson (1993). Consumers are not represented by vectors but by ideal points. The model links a consumer’s liking rating L of a product at a particular moment to the distance d between the product and the consumer ideal point using the model: L = b exp(d2), where b represents a parameter taking into account the consumer’s scale usage behavior (varying between 0 and 1, 1 indicating no bias (the consumer uses the whole length of the scale)). The closer an ideal point is to a product, the more he/she likes it, and vice versa, subject to some further effects such as product variances and consumer biases. The background and applications of LSA have been published (Ennis (2001), Ennis and Rousseau (2004)). Upon generation of the map, either with IPM or LSA, further steps can be taken. The first one relates to the uncovering of the underlying drivers of liking. In IPM and LSA, the DOLs are identified by regressing the sensory attributes and trying to maximize the correlation between the original product intensities on the attribute and the projections of the product in a particular direction on the map. This process is illustrated in Fig. 2. In this example, a map was first generated using data describing six products, A
Second dimension
F
E
A
B
D C Sweetness
First dimension Fig. 2. Attribute regression process on a product space.
through F. Then the sweetness intensity attribute, obtained for instance from a trained panel, was regressed to assess whether it would explain a particular map direction. In this article, the size of the symbols is shown as inversely proportional to the corresponding intensity for efficiency (while it is somewhat counterintuitive, it was necessary to permit a better readability of the resulting maps shown later on in this article). All possible angles were considered and the one that allowed the greatest correlation between the original sweetness data and the map projections of the products on the attribute was retained. As can be seen in our example, ‘sweetness’ was found to be a DOL, as it describes the northeast/southwest direction very well. Attributes with high correlations are deemed DOLs, while those with lower correlations are not because this is a sensory space. Attributes are usually selected as DOLs if their correlation coefficients are significant at the 5% or 1% level. Based on this example, it is clear that the product locations directly drive the regression process and thus spaces with different product structures will yield different sets of drivers of liking. In addition to uncovering the DOLs, IPM and LSA also allow the study of population segmentation. This can be done visually by considering groups of vectors pointing in different directions, or densities of ideal points in various areas of the map. In other cases, a cluster analysis of overall liking scores can be conducted and subsequent IPMs performed on the subgroups that were identified. A recently developed methodology (Euclidean Distance Ideal Point Modeling or EDIPM) by Meullenet, Lovely, Threlfall, Morris, and Striegler (2008) and Tubbs, Oupadissakoon, Lee, and Meullenet (2010) even uses a reversed approach to finding ideal points once a map has been generated with IPM or any other spatial technique representing the products’ configuration. This research article is structured into two different sections. The first section describes a simulation conducted to illustrate expected and actual results from a market research study for a product category with a majority of satiety-‘prone’ attributes. The data are analyzed with both IPM and LSA. The second section summarizes the results of 27 actual market research studies with data analyzed again using both techniques.
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2. Simulation 2.1. Simulation parameters Simulations play a very important role in the validation of a model. If a simulation cannot recover the parameters associated with a known space structure, how confident can we be of its ability to recover parameters associated with a space that is not known? For any simulation reasonable assumptions for the data that will be modeled are necessary. The one reported here was conducted with Coombs’ idea in mind. It involved independent bi-variate normal variables [mean (0, 0) and variances (0.2, 0.2)] from which 350 ideal points (consumers) were sampled randomly. In addition, ten products were spread across the space as shown in Fig. 3. In terms of sensory attributes, two types of descriptive information were also generated. A set of eight attributes exhibiting satiety (S set) and a set of four attributes highly correlated to liking and thus without a satiety point (non-satiety N set). For this simulation, plotting the product means of each of the attributes against the product means on liking show the characteristic inverted U shape for the S attributes, while a linear relationship is found for the N attributes (see Fig. 4). These attributes were chosen in such a way that, in the space illustrated in Fig. 3, the S attributes are strong drivers while the N attributes are not (most liked product and highest density of ideal points at the center of the space and less liked products spread evenly around it, thus preventing a non-satiety attribute to fit on the map). It seems reasonable that, upon unfolding liking, the S attributes should be found as drivers of liking, while the N attributes should not, or at least to a lesser extent. Attribute S2 in Fig. 3 illustrates how such attributes exhibit satiety properties. For each individual ideal point, the optimal S2 intensity can be estimated through the orthogonal projection of the ideal point onto the attribute. Once the optimal level has been identified, weaker (to the north) or stronger (to the south) S2 intensities will result in lower overall liking for this particular consumer. This relates to the liking/intensity fundamental relationship imagined by Coombs and represented in Fig. 1. 2.2. Data generation The next step of the simulation was to produce the liking ratings that will be used for the IPM and LSA analyses. They were generated using the function L = 9 exp(d2), where L is the predicted liking score and d the Euclidean distance between a given ideal
7
2
Products
6 3
1
Ideal points
8
5
10
4
9
S2
Fig. 3. Simulation: starting configuration.
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point and a given product. This function leads to ‘‘9’’ ratings when ideals and products occupy the same point and lower ratings as d increases. Theoretically if d = 1, the liking would be rated at zero. Thus 3500 liking scores were generated (350 consumers 10 products) and the average product liking means are provided in Table 1. Note that in this case b = 1. Products closer to the center of the ideal point cloud will receive higher average liking ratings (e.g., P1 and P5, shorter distances to most ideal points, thus higher liking ratings) while products located towards its periphery will exhibit lower average liking scores (e.g., P8 and P9). The individual consumer liking scores were then used with our two methodologies of interest. 2.3. IPM and LSA on the generated data 2.3.1. Unfolding liking First, a principal component analysis of the liking data (IPM) using the covariance matrix was first conducted using the JMPÒ 8.0 software. The first two components of the resulting space (with the associated variance explained) are shown in Fig. 5a. Only the products are represented to simplify the visual representation of the results, i.e., the consumer vectors are not included (the majority of them are pointing toward the upper right corner of the map). The filled-in circles represent the product locations, their size being inversely proportional to their overall average liking ratings (i.e., a larger circle represents a product with a lower average liking and vice versa). Just looking at the general direction of the circles on Fig. 5a, one can see that there seems to be an hedonic direction going southwest–northeast on the map, products that are liked the most are at one end of the map, while those that are liked the least are at the opposite end. Regressing liking on the map confirms this, as it fits with a correlation coefficient of 0.9987. In contrast, LSA (conducted using IFPrograms™ 8.9) provides a map portraying a very different product structure (Fig. 5b). Subject r2 and product r2 values convey the goodness of fit of the model (the r2 values indicate the correlation between the observed and predicted liking ratings at the individual subject level (raw scores) and product level (average ratings)). Due to the nature of the simulated data, the subject r2 value is particularly high and not typical of what is usually observed with real data. Again, consumers are not represented so as to facilitate the visual depiction of the results (they are mainly located close to P1, while their density decreases fairly evenly as one travels away from the center of the map, recovering the configuration of the simulated data). As described previously, products closer to the center of the map, thus closer to the greater consumer density, will receive higher average liking ratings. This can be seen by the size of the filled-in circles. When regressing liking onto the map, a correlation coefficient of only 0.28 is found, thus ‘liking’ is not a driver of liking (a minimum coefficient of 0.63 is necessary to be significant at the 5% level). The effect described here was first described by Ennis (2005) in a three dimensional simulation using a cube configuration. It is worth noting that although it fits the simulation data, LSA is not entirely based on the Gaussian function described earlier to generate them. It also includes product variances and subject biases and so is a probabilistic model in contrast to the deterministic model used in the simulation. 2.3.2. Uncovering the drivers of liking The spaces uncovered by IPM and LSA will result in different sets of drivers of liking using the approach described in Fig. 2 (only attributes deemed to be DOLs, i.e., with a significant correlation coefficient, are represented on the maps). For IPM, because of its very strong hedonic dimension, only attributes correlated to liking, and thus not exhibiting satiety, will fit in that direction. In our example, the non-satiety attributes fit well there, but are not diag-
Liking g
Liking
Liking
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Liking
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S2
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S5
Liking
S4
Liking
Liking
Liking
S1
Liking
Liking
Liking
Liking
S8
S7
S6
N2
N1
N4
N3
Fig. 4. Partial plots: attributes vs. liking.
Table 1 Simulation: mean liking scores for the ten products. Products
Mean liking
P1 P5 P3 P2 P4 P10 P7 P6 P8 P9
8.31 8.03 7.75 7.40 7.38 6.83 6.37 6.32 5.31 5.17
nostic as they are simply surrogates to liking itself, as it is often found with consumer related attributes (Fig. 6a). While non-satiety attributes might be relevant in certain studies, they were not here according to the simulation scenario that was specifically generated. The satiety attributes fit at a right angle to the hedonic direction, which is the only way to account for satiety. S1, S4, S5, and S8 are not even identified as DOLs (correlation coefficients of 0.24, 0.57, 0.14, and 0.35, respectively). IPM, as an unfolding technique
was thus unable to recover the starting configuration of the simulation used to generate the data. This is understandable as the theory behind IPM does not include satiety, but the problem for experimenters using IPM is that they usually do not know in advance that satiety will not occur. The set of important attributes identified by LSA is quite different (Fig. 6b). All the satiety attributes are found as DOLs, while the non-satiety attributes are not (correlation coefficients ranging 0.24–0.40). All the DOLs must exhibit satiety, as the optimal level will be close to the center of the map (at the average location of the ideal points), with overall liking decreasing when going on either side of the optimum. This simulated scenario illustrates the fact that an internal preference mapping on consumers’ individual hedonic ratings of a set of products can create a strong hedonic dimension when the process used by consumers to generate their ratings involves an ideal point. Consequently, the product structure that needs to be uncovered can be significantly altered, which in turn will result in important side effects in terms of the interpretability of subsequent analytical steps, such as the identification of the drivers of liking. The end result is that in situations where non-satiety attributes are true DOLs, IPM, and LSA as it will illustrated later on, will most
a
b Liking (r = 0.999)
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Subject r2 = 0.99 Product r2 = 1
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Liking (r = 0.28)
P2
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P3 P1
P3
P8
P8
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Comp 1 (52%) Fig. 5. Simulation: IPM and LSA maps with regressed liking attribute. Greater circle sizes indicate lower average liking ratings.
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b
a S3 (0.89)
S2 (0.99)
S7 (1)
Comp 2 (28%)
N4 N3 (0.97) (0.96) N1 (0.95) N2 (0.95)
S6 (1)
Subject r2 = 0.99 Product r2 = 1
S5 (1)
S8 (1)
S4 (1)
S1 (1)
S7 S6 (0.97) (0.69)
S3 (1) S2 (1)
Comp 1 (52%) Fig. 6. Simulation: IPM and LSA maps with significant drivers of liking (a = 5%). Attribute correlation coefficients in parentheses.
likely be able to uncover them. However, in situations where they are not actual DOLs, these non-satiety attributes, which are often simply surrogates of liking, will be mistakenly identified as DOLs, while true DOLs that exhibit a satiety point will not be. 3. Real data
Table 2 List of 27 market studies analyzed using IPM and LSA. Number of studies
Product category
Location(s)
Number of products
Number of consumers
1
Salad dressing Meat products Cream cheese Mac & Cheese Mayonnaise Sliced cheese
US
27
318
US
13–22
198–272
US, Canada, Germany, Italy, UK, and Australia US
21–24
201–216
30
109, 318
US US, Canada, Spain, UK, Mexico, Italy, and Australia US US, France US
26 24–30
307 202–212
13 24, 20 19
207 270, 405 226
US
24
247
3
Following the learning from the simulation, the same analyses were conducted using actual market research data. They were extracted from 27 category appraisals conducted with different types and numbers of products, with different consumer sample sizes and in various countries. Table 2 summarizes the data sets that were analyzed. First the results of three of the studies will be summarized here. The first study involved 19 cheddar cheese samples that were evaluated by 226 consumers in the United States. Fig. 7a shows the first two components from the internal preference mapping analysis. Upon regressing liking onto the map, it is found to be a very strong driver with a correlation coefficient of 0.996. On the other hand, a Landscape Segmentation Analysis of the same data yields a space with an overall different product structure and an appreciably lower correlation coefficient for liking (0.65; Fig. 7b). This effect echoes that observed in the simulation. An even stronger illustration can be seen in Fig. 8. This figure summarizes the data collected in the US using 212 consumers who evaluated 26 samples of melted cheese slices served on a grilled cheese sandwich. As for the simulation and the previous example, a very strong hedonic dimension is uncovered, with a correlation coefficient for liking of 0.989 (Fig. 8a). In contrast, the LSA product structure is again noticeably different, with a correlation coefficient for liking of 0.16 (Fig. 8b). A third analysis that is interesting to consider is one where both LSA and IPM uncover a strong hedonic dimension. It is the case in the ‘‘SC Italy’’ data set where 205 consumers evaluated 24 processed cheese samples (see Fig. 9a and b). In this particular case, some of the strongest drivers of liking separate two groups of products, one with products receiving higher liking ratings situated to the right-hand side of the map while the other with products with –very– low average liking ratings on the left-hand side. Consequently, liking can be seen increasing linearly from left to right with drivers linked to appearance (orange to white color) and a specific cheese off-flavor (increasing from right to left) explaining that
6 2 1 9
1 2 1 1
Pizza Coffee Cheddar cheese Whipped topping
direction with high accuracy. LSA handled this situation by placing most of the ideal points close to the products with higher hedonic scores. This is an illustration that when at least some of the major drivers of liking exhibit a non-satiety behavior, LSA will adjust the resulting solution to accommodate this information. In such situations, LSA and IPM will generate similar spatial outcomes. The two analyses were conducted on each of the 27 data sets available and Fig. 10 summarizes the results. On this figure, the correlation coefficients for liking for each study are plotted for both IPM and LSA, the studies being ordered by decreasing order of the IPM liking correlation coefficient. As can be clearly seen, the effect observed in our simulation as well as in the three examples above does not follow a random pattern but is consistent across all 27 studies. Liking exhibits a very high correlation on the IPM maps in all 27 studies, systematically creating a strong hedonic dimension, while its value is much more variable for the LSA maps and will depend on the data set under consideration. Consequently, IPM consistently did not succeed in unfolding liking, since the exact information it is trying to unfold is found virtually unchanged on the plane explaining the greatest amount of variance in the data.
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a
b
Comp 2 (13%)
Liking (r = 0.996)
Subject r2 = 0.53 Product r2 = 0.98
Liking (r = 0.65)
Comp 1 (26%) Fig. 7. IPM and LSA analyses of the cheddar cheese data.
a
b r2
Subject = 0.50 Product r2 = 0.99
Comp 2 (15%)
Liking (r = 0.989)
Liking (r = 0.16)
Comp 1 (18%) Fig. 8. IPM and LSA analyses of the sliced cheese data (hot presentation, USA).
4. Discussion The results from the simulation along with those of the 27 category appraisals are quite consistent. While only 28 data sets are considered here, the authors have analyzed many others which resulted in the same outcomes. IPM maps published in the literature, or presented at conferences, show the same very clear tendency (for instance, see Felberg, Deliza, Farah, Calado, and Donangelo (2010), Pastor, Costell, Izquierdo, and Durán (1996), SerranoMegías, Pérez-López, Núñez-Delicado, Beltrán, and López-Nicolás (2005), Yackinous, Wee, and Guinard (1999), and Yates and Drake (2007) and numerous others). This effect is illustrated further by some work conducted by Meullenet and colleagues on Euclidean Distance Ideal Point Mapping. The method can be applied to any spatial representation of a set of products, including IPM. Each consumer is regressed onto the map and his/her ideal location, represented by a point, is esti-
mated so that the distance between the ideal point and each of the products is inversely proportional to the observed overall liking ratings. The strong hedonic dimension created by IPM will have the effect of locating the highest density of ideal points at the periphery of the map, close to the products that were liked the most and away from those that were liked the least. This effect is very clearly visible in Fig. 2 and Fig. 4 of Tubbs et al. (2010) and in Fig. 1 of Lovely and Meullenet (2009). Consequently, the optimal levels of attributes aiming in the direction of the greatest density of consumers, i.e. linearly correlated with liking, will be the highest (if positively correlated), or lowest (if negatively correlated), values exhibited in the set of products considered in the research, and thus incompatible with the concept of satiety. While the authors conceived a useful method to locate ideal points on any spatial representation of a set of products, an IPM space will distort the outcomes of the analysis. Their method would not have been
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a
b
Comp 2 (4.5%)
Subject r2 = 0.70 Product r2 = 0.999
Liking (r = 0.986)
Liking (r = 0.999)
Comp 1 (62%)
SC
M
It a ly ay on Ch na e d ise Sa da r la d W dr hi e s p s CC ped ing to U pp M S in &C g SC A d ul UK ts M &C CC K id C s CC ana Ita da SC ly U CC S c ol A d CC ust r U a lia SC K C SC ana Au da SC st c ol r d M alia SC exi co US CC h o G t Bo erm lo a n g P i na y zz a Ba co Ho n tD Co og f fe SC e U S Ca Co na f fe da ho SC e F t r Sp anc ai e n
Fig. 9. IPM and LSA analyses of the sliced cheese data (Italy).
1
Correlation of liking on map
0.9
0.8
0.7
0.6
Landscape Segmentation Analysis 0.16 Internal Preference Mapping
0.5 SC: Sliced Cheese CC: Cream Cheese
Fig. 10. Summary of the correlation of liking on the IPM and LSA spaces for the 27 studies.
successful at recreating the starting configuration used in the simulation described above. Also, issues would arise when studying potential segmentation based on ideal point locations and trying to classify the consumers in terms of their demographics or usage patterns. It needs to be noted that these comments apply to using the Euclidean Distance Ideal Point Mapping technique with IPM and that it can still have useful applications when using other product spaces such as those created using a principal component analysis of product sensory information, in an approach similar to external preference mapping. Also, the investigation of segmentation based solely on the map’s vector information is still suitable. It is important to note that this research does not imply that a vector based model such as IPM and an ideal point model such as LSA will always yield different solutions and interpretations of consumer studies, as was illustrated with the example depicted in Fig. 9. As one can see in Fig. 10 that while IPM always exhibited a high correlation for liking (>0.9), the same was true for at least a
few of the LSA analyses. Consequently, both techniques would yield similar spatial representation of the test products and as a result similar drivers of liking sets, as was illustrated with the SC Italy data set. This situation will occur when on at least one sensory dimension important to consumers, the products tested did not go over, or below, the optimal intensity level. Another example is the existence of an off-flavor in the products, whose presence systematically decreases overall product acceptability. As indicated earlier on, it is worth noting that if an attribute with a linear relationship to liking is a strong driver of liking, LSA will handle the overall space structure by locating a density of ideal points towards the periphery of the map so that products in the vicinity, which will have a high level of a positive attribute, or a low level of a negative attribute, will exhibit a high average liking value. A legitimate question is to wonder how large an impact these two modeling approaches will have on the nature of the findings, which is what any researcher will ultimately want to know. As
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one can expect, it will be very dependent on the difference in the unfolded product structure. Two interesting references are available which compared several product optimization methodologies. In the first one (Meullenet et al., 2007, chap. 5), LSA and IPM were compared directly and the predicted optimal profiles were fairly close, with the exception of a few attributes. Overall the authors conclude that the solutions were similar, even though they did not address the fact that the attributes with different predictions might be strong drivers of liking. In another paper (Lovely & Meullenet, 2009), the authors compared external preference mapping, IPM and LSA. In this case, there were greater differences among the ideal profiles predicted by the IPM and LSA methods, even though a confirmatory study did not find any significant difference between the two optimal products that were manufactured and tested with consumers. These were of course only two direct comparisons of the methodologies. It is worth mentioning that in both cases the overall product spatial structures were similar, thus resulting in similar predictions. Also, these analyses were conducted to find the optimal profile of only one product, i.e., a product that will be expected to satisfy the consumer population tested. However, companies very often investigate potential consumer segmentation to be able to target subgroups appropriately so as to optimize the appeal of their products. This will lead to the consideration of optimal product portfolios which will accentuate the differences in the solutions uncovered by different unfolding methodologies. In conclusion, what has been discussed here illustrates the need for further research on this topic. Nevertheless, there is now good evidence that internal preference mapping conducted on consumer overall liking ratings will create a spatial configuration of the products that might be incompatible with the actual underlying structure that it was trying to reveal since it will always find a driving hedonic dimension. In turn, this dimension will prevent the identification of drivers of liking exhibiting sensory satiety, i.e., an ideal point, and thus in many situations will produce biased results. This is something a sensory scientist should consider when using this technique and interpreting its findings. A central question to this issue is what is the underlying psychological process used by consumers to generate liking ratings? The simulation described earlier, which assumed that consumers use ideal points to generate liking ratings, resulted in an IPM map with a very strong hedonic dimension. As mentioned previously, simulations with the same assumption will always find such dimension in an IPM map. When considering actual market data, we showed here that an IPM analysis, at least for all the data sets that were analyzed and the published results that were reviewed, will always uncover an hedonic dimension. This might be the first indication that a model based on ideal points could be the most suitable to analyze consumer generated hedonic data. Finally, ease of access to multivariate analysis software makes it relatively easy to conduct analyses, such as those used in this paper. Interpretation of the results of those analyses depends very much on the user’s understanding of the assumptions made in constructing the models. Generally, one does not know the underlying structure, so there is no way to know how badly distorted the resulting map might be by inspection after an analysis. Tests of
goodness of fit may not be sufficient to avoid this problem. The best strategy is to think about the most likely structure underlying the data generating process and use modeling approaches that are as faithful as possible to that theory. Otherwise, one is very likely to end up with confusing, sometimes conflicting, results that have limited applicability. Note: This research was first presented at the 8th Pangborn Sensory Science Symposium in Florence, Italy, in July 2009. References Ashby, F. G., & Ennis, D. M. (2002). A Thurstone–Coombs model of concurrent ratings with sensory and liking dimensions. Journal of Sensory Studies, 17, 43–59. Busing, F. M. T. A., Groenen, P. J. F., & Heiser, W. J. (2005). Avoiding degeneracy in multidimensional unfolding by penalizing on the coefficient of variation. Psychometrika, 70, 71–98. Busing, F. M. T. A., Heiser, W. J., & Cleaver, G. (2010). Restricted unfolding: Preference analysis with optimal transformations of preferences and attributes. Food Quality and Preference, 21, 82–92. Coombs, C. H. (1964). A Theory of Data. New York: Wiley. Ennis, D. M. (1993). A single multidimensional model for discrimination, identification, and preferential choice. Acta Psychologica, 84, 17–27. Ennis, D. M. (2001). Drivers of LikingÒ for multiple segments. IFPress, 4(1), 2–3. Ennis, D. M. (2005). Analytic approaches to accounting for individual ideal points. IFPress, 8(2), 2–3. Ennis, D. M., & Johnson, N. L. (1993). Thurstone–Shepard similarity models as special cases of moment generating functions. Journal of Mathematical Psychology, 37, 104–110. Ennis, D. M., & Johnson, N. L. (1994). A general model for preferential and triadic choice in terms of central F distribution functions. Psychometrika, 59(1), 91–96. Ennis, D. M., & Mullen, K. (1992). A general probabilistic model for triad discrimination, preferential choice, and two-alternative identification. In F. Gregory Ashby (Ed.), Multidimensional Models of Perception and Cognition. Mahwah, NJ: Lawrence Erlbaum Associates. Ennis, D. M., & Rousseau, B. (2004). Motivations for product consumption: Application of a probabilistic model to adolescent smoking. Journal of Sensory Studies, 19, 107–117. Ennis, D. M., Palen, J., & Mullen, K. (1988). A multidimensional stochastic theory of similarity. Journal of Mathematical Psychology, 32, 449–465. Felberg, I., Deliza, R., Farah, A., Calado, E., & Donangelo, C. M. (2010). Formulation of a soy–coffee beverage by response surface methodology and Internal Preference Mapping. Journal of Sensory Studies, 25, 226–242. Gabriel, K. R. (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58, 453–467. Lovely, C., & Meullenet, J.-F. (2009). Comparison of preference mapping techniques for the optimization of strawberry yogurt. Journal of Sensory Studies, 24, 45–47. MacKay, D. (2001). Probabilistic unfolding models for sensory data. Food Quality and Preference, 12, 427–436. MacKay, D. (2006). Chemometrics, econometrics, psychometrics—How best to handle hedonics? Food Quality and Preference, 17, 529–535. Meullenet, J.-F., Xiong, R., & Findlay, C. (2007). Multivariate and probabilistic analyses of sensory science problems. Oxford, UK: Blackwell Publishing. Meullenet, J.-F., Lovely, C., Threlfall, R., Morris, J. R., & Striegler, R. K. (2008). An ideal point density plot method for determining an optimal sensory profile for muscadine grape juice. Food Quality and Preference, 19, 210–219. Pastor, M. V., Costell, E., Izquierdo, L., & Durán, L. (1996). Optimizing acceptability of a high fruit-low sugar peach nectar using aspartame and guar gum. Journal of Food Science, 61, 852–855. Serrano-Megías, M., Pérez-López, A. J., Núñez-Delicado, E., Beltrán, F., & LópezNicolás, J. M. (2005). Optimization of tropical juice composition for the Spanish market. Journal of Food Science, 70, S28–S33. Tubbs, J., Oupadissakoon, G., Lee, Y. S., & Meullenet, J.-F. (2010). Performance and representation of Euclidean Distance Ideal Point Modeling (EDIPM) using a third dimension. Food Quality and Preference, 21, 278–285. Yackinous, C., Wee, C., & Guinard, J.-X. (1999). Internal preference mapping of hedonic ratings for Ranch salad dressings varying in fat and garlic flavor. Food Quality and Preference, 10, 401–409. Yates, M. D., & Drake, M. A. (2007). Texture properties of Gouda cheese. Journal of Sensory Studies, 22, 493–506.
Contents lists available at SciVerse ScienceDirect
Food Quality and Preference journal homepage: www.elsevier.com/locate/foodqual
Internal preference mapping and the issue of satiety Benoît Rousseau a,⇑, Daniel M. Ennis a, Frank Rossi b a b
The Institute for Perception, Richmond, VA, USA Kraft Foods Inc., Glenview, IL, USA
a r t i c l e
i n f o
Article history: Received 19 January 2011 Received in revised form 17 August 2011 Accepted 15 September 2011 Available online 29 September 2011 Keywords: Internal preference mapping Landscape Segmentation Analysis Vectors Ideal points Satiety
a b s t r a c t Internal preference mapping (IPM) and Landscape Segmentation AnalysisÒ (LSA) are two techniques broadly used to unfold consumers’ overall product liking ratings and create spatial maps that will provide further insights on consumers’ preferences. IPM is based on a vector model while LSA involves an ideal point model. Through a simulation and the analysis of 27 market research data sets, it is shown that IPM consistently creates a hedonic dimension that prevents the identification of satiety prone attributes (intensities higher or lower than a optimal level being disliked by the consumers) on that dimension. As a result, subsequent steps taken upon generating an IPM map such as the investigation of drivers of liking, population segmentation and the estimation of optimal product profiles have also a strong likelihood of resulting in distorted results, the level of distortion being dependent on the actual configuration of the underlying structure that IPM tried to uncover. It is also shown that a technique based on ideal points such as LSA does not exhibit this systematic artifact when unfolding liking data. Consequently, sensory scientists and market researchers should use caution when interpreting and using results issued from an internal preference mapping analysis. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction The quest to understand what drives consumers’ preferences is one of the most important activities of consumer product companies. Colossal budgets are invested every year to identify the variables and characteristics that drive consumers’ liking of a particular product category. In the food, beverage and personal care industries, one aspect fundamental to those characteristics is that of ‘satiety’. Satiety refers to the fact that consumers will generally have an optimal level on each of the relevant attributes that will optimize their liking response. This idea, first outlined by Coombs (1964), is that, for instance, the sweetness of a soft drink has an optimal level and so does the hardness of a candy and the fragrance level of a room deodorizer. All of them can be ‘too weak’ or ‘too intense’. This relationship is illustrated in Fig. 1. It does make sense that a majority of consumer products’ drivers of liking (DOL) will exhibit this particular relationship with liking. Of course, a smaller number of attributes might show more of a linear relationship, such as an off-flavor which will induce lower hedonic ratings as its intensity increases. If we agree with this general ‘view of the world’, techniques that explore liking data from consumers with the objective to extract the DOLs should have the capability to identify mostly attributes ⇑ Corresponding author. Address: The Institute for Perception, 7629 Hull Street Road, Richmond, VA 23235, USA. Tel.: +1 804 675 2980; fax: +1 804 675 2983. E-mail address: [email protected] (B. Rousseau). 0950-3293/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodqual.2011.09.003
exhibiting satiety. Numerous analytical methods are available that purport to find DOLs. They often aim at uncovering additional insights, such as the profiles of optimal products and the possibility of population segmentation. Such techniques include internal and external preference mapping, partial least square regression, justabout-right scaling, Landscape Segmentation AnalysisÒ, etc. A recent publication by Meullenet, Xiong, and Findlay (2007) offers a useful overview of the analytical approaches available to the sensory evaluation and market research scientists. Because of satiety, these techniques should not simply uncover liking itself as a DOL, nor should they uncover variables that are liking surrogates, otherwise one ends up with a recommendation such as ‘‘to make a more acceptable beer, make it taste better!’’. Two techniques on which this article will focus are internal preference mapping (IPM) and Landscape Segmentation Analysis (LSA). Both use the same type of data: liking ratings by consumers on a set of products using, for instance, a 9-point hedonic scale. They aspire to extract the same information as well by ‘unfolding’ liking: create a map that will summarize how the individual consumers see the products that were evaluated. It is worth mentioning here the fundamental difference between these two techniques and others that first create a space based on product characteristics such as external preference mapping (EPM). IPM and LSA do not assume anything about the underlying product space structure and generate a spatial representation based solely on the consumer’ liking information. In contrast, EPM first uses information usually from a trained panel’s descriptive data to create a space on which
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A
F
E
B
D C
Liking
Sweetness
Optimal level
Intensity Fig. 1. Illustration of a sensory attribute’s satiety property.
the consumer information will be projected/regressed. Consequently, EPM assumes that the most obvious differences, which are driven by attributes in the first two dimensions, are the drivers of liking. While it might be the case, it is also possible that true drivers of liking be ‘hidden’ in higher dimensions. IPM and LSA do not make any assumptions about the potential DOLs. They can be seen as representing the world from the consumer’s point of view, while EPM is representing the world as the analyst sees it. The objective of this paper is to outline IPM’s ability to handle satiety and to contrast it with an ideal point model-based technique such as LSA. It is worth mentioning that other ideal point models exist, such as that described by Busing, Groenen, and Heiser (2005) and Busing, Heiser, and Cleaver (2010) which is based on individual ideals or others based on ideal distributions (Ashby & Ennis, 2002; Ennis, 1993; Ennis & Johnson, 1994; Ennis & Mullen, 1992; MacKay, 2001, 2006). Even though IPM and LSA use the same data, their respective underlying modeling approaches are very different. – IPM is a particular application of the biplot developed by Gabriel (1971). The analysis results in a multidimensional space of which the first two components are typically focused on. They outline a plane where products are represented as points and consumers as vectors. The vectors indicate liking directions for each subject, i.e., the further away a product is located in that direction, the more a given consumer will like it. – LSA is based on a similarity model developed by Ennis, Palen, and Mullen (1988) and Ennis and Johnson (1993). Consumers are not represented by vectors but by ideal points. The model links a consumer’s liking rating L of a product at a particular moment to the distance d between the product and the consumer ideal point using the model: L = b exp(d2), where b represents a parameter taking into account the consumer’s scale usage behavior (varying between 0 and 1, 1 indicating no bias (the consumer uses the whole length of the scale)). The closer an ideal point is to a product, the more he/she likes it, and vice versa, subject to some further effects such as product variances and consumer biases. The background and applications of LSA have been published (Ennis (2001), Ennis and Rousseau (2004)). Upon generation of the map, either with IPM or LSA, further steps can be taken. The first one relates to the uncovering of the underlying drivers of liking. In IPM and LSA, the DOLs are identified by regressing the sensory attributes and trying to maximize the correlation between the original product intensities on the attribute and the projections of the product in a particular direction on the map. This process is illustrated in Fig. 2. In this example, a map was first generated using data describing six products, A
Second dimension
F
E
A
B
D C Sweetness
First dimension Fig. 2. Attribute regression process on a product space.
through F. Then the sweetness intensity attribute, obtained for instance from a trained panel, was regressed to assess whether it would explain a particular map direction. In this article, the size of the symbols is shown as inversely proportional to the corresponding intensity for efficiency (while it is somewhat counterintuitive, it was necessary to permit a better readability of the resulting maps shown later on in this article). All possible angles were considered and the one that allowed the greatest correlation between the original sweetness data and the map projections of the products on the attribute was retained. As can be seen in our example, ‘sweetness’ was found to be a DOL, as it describes the northeast/southwest direction very well. Attributes with high correlations are deemed DOLs, while those with lower correlations are not because this is a sensory space. Attributes are usually selected as DOLs if their correlation coefficients are significant at the 5% or 1% level. Based on this example, it is clear that the product locations directly drive the regression process and thus spaces with different product structures will yield different sets of drivers of liking. In addition to uncovering the DOLs, IPM and LSA also allow the study of population segmentation. This can be done visually by considering groups of vectors pointing in different directions, or densities of ideal points in various areas of the map. In other cases, a cluster analysis of overall liking scores can be conducted and subsequent IPMs performed on the subgroups that were identified. A recently developed methodology (Euclidean Distance Ideal Point Modeling or EDIPM) by Meullenet, Lovely, Threlfall, Morris, and Striegler (2008) and Tubbs, Oupadissakoon, Lee, and Meullenet (2010) even uses a reversed approach to finding ideal points once a map has been generated with IPM or any other spatial technique representing the products’ configuration. This research article is structured into two different sections. The first section describes a simulation conducted to illustrate expected and actual results from a market research study for a product category with a majority of satiety-‘prone’ attributes. The data are analyzed with both IPM and LSA. The second section summarizes the results of 27 actual market research studies with data analyzed again using both techniques.
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2. Simulation 2.1. Simulation parameters Simulations play a very important role in the validation of a model. If a simulation cannot recover the parameters associated with a known space structure, how confident can we be of its ability to recover parameters associated with a space that is not known? For any simulation reasonable assumptions for the data that will be modeled are necessary. The one reported here was conducted with Coombs’ idea in mind. It involved independent bi-variate normal variables [mean (0, 0) and variances (0.2, 0.2)] from which 350 ideal points (consumers) were sampled randomly. In addition, ten products were spread across the space as shown in Fig. 3. In terms of sensory attributes, two types of descriptive information were also generated. A set of eight attributes exhibiting satiety (S set) and a set of four attributes highly correlated to liking and thus without a satiety point (non-satiety N set). For this simulation, plotting the product means of each of the attributes against the product means on liking show the characteristic inverted U shape for the S attributes, while a linear relationship is found for the N attributes (see Fig. 4). These attributes were chosen in such a way that, in the space illustrated in Fig. 3, the S attributes are strong drivers while the N attributes are not (most liked product and highest density of ideal points at the center of the space and less liked products spread evenly around it, thus preventing a non-satiety attribute to fit on the map). It seems reasonable that, upon unfolding liking, the S attributes should be found as drivers of liking, while the N attributes should not, or at least to a lesser extent. Attribute S2 in Fig. 3 illustrates how such attributes exhibit satiety properties. For each individual ideal point, the optimal S2 intensity can be estimated through the orthogonal projection of the ideal point onto the attribute. Once the optimal level has been identified, weaker (to the north) or stronger (to the south) S2 intensities will result in lower overall liking for this particular consumer. This relates to the liking/intensity fundamental relationship imagined by Coombs and represented in Fig. 1. 2.2. Data generation The next step of the simulation was to produce the liking ratings that will be used for the IPM and LSA analyses. They were generated using the function L = 9 exp(d2), where L is the predicted liking score and d the Euclidean distance between a given ideal
7
2
Products
6 3
1
Ideal points
8
5
10
4
9
S2
Fig. 3. Simulation: starting configuration.
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point and a given product. This function leads to ‘‘9’’ ratings when ideals and products occupy the same point and lower ratings as d increases. Theoretically if d = 1, the liking would be rated at zero. Thus 3500 liking scores were generated (350 consumers 10 products) and the average product liking means are provided in Table 1. Note that in this case b = 1. Products closer to the center of the ideal point cloud will receive higher average liking ratings (e.g., P1 and P5, shorter distances to most ideal points, thus higher liking ratings) while products located towards its periphery will exhibit lower average liking scores (e.g., P8 and P9). The individual consumer liking scores were then used with our two methodologies of interest. 2.3. IPM and LSA on the generated data 2.3.1. Unfolding liking First, a principal component analysis of the liking data (IPM) using the covariance matrix was first conducted using the JMPÒ 8.0 software. The first two components of the resulting space (with the associated variance explained) are shown in Fig. 5a. Only the products are represented to simplify the visual representation of the results, i.e., the consumer vectors are not included (the majority of them are pointing toward the upper right corner of the map). The filled-in circles represent the product locations, their size being inversely proportional to their overall average liking ratings (i.e., a larger circle represents a product with a lower average liking and vice versa). Just looking at the general direction of the circles on Fig. 5a, one can see that there seems to be an hedonic direction going southwest–northeast on the map, products that are liked the most are at one end of the map, while those that are liked the least are at the opposite end. Regressing liking on the map confirms this, as it fits with a correlation coefficient of 0.9987. In contrast, LSA (conducted using IFPrograms™ 8.9) provides a map portraying a very different product structure (Fig. 5b). Subject r2 and product r2 values convey the goodness of fit of the model (the r2 values indicate the correlation between the observed and predicted liking ratings at the individual subject level (raw scores) and product level (average ratings)). Due to the nature of the simulated data, the subject r2 value is particularly high and not typical of what is usually observed with real data. Again, consumers are not represented so as to facilitate the visual depiction of the results (they are mainly located close to P1, while their density decreases fairly evenly as one travels away from the center of the map, recovering the configuration of the simulated data). As described previously, products closer to the center of the map, thus closer to the greater consumer density, will receive higher average liking ratings. This can be seen by the size of the filled-in circles. When regressing liking onto the map, a correlation coefficient of only 0.28 is found, thus ‘liking’ is not a driver of liking (a minimum coefficient of 0.63 is necessary to be significant at the 5% level). The effect described here was first described by Ennis (2005) in a three dimensional simulation using a cube configuration. It is worth noting that although it fits the simulation data, LSA is not entirely based on the Gaussian function described earlier to generate them. It also includes product variances and subject biases and so is a probabilistic model in contrast to the deterministic model used in the simulation. 2.3.2. Uncovering the drivers of liking The spaces uncovered by IPM and LSA will result in different sets of drivers of liking using the approach described in Fig. 2 (only attributes deemed to be DOLs, i.e., with a significant correlation coefficient, are represented on the maps). For IPM, because of its very strong hedonic dimension, only attributes correlated to liking, and thus not exhibiting satiety, will fit in that direction. In our example, the non-satiety attributes fit well there, but are not diag-
Liking g
Liking
Liking
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Liking
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S2
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S5
Liking
S4
Liking
Liking
Liking
S1
Liking
Liking
Liking
Liking
S8
S7
S6
N2
N1
N4
N3
Fig. 4. Partial plots: attributes vs. liking.
Table 1 Simulation: mean liking scores for the ten products. Products
Mean liking
P1 P5 P3 P2 P4 P10 P7 P6 P8 P9
8.31 8.03 7.75 7.40 7.38 6.83 6.37 6.32 5.31 5.17
nostic as they are simply surrogates to liking itself, as it is often found with consumer related attributes (Fig. 6a). While non-satiety attributes might be relevant in certain studies, they were not here according to the simulation scenario that was specifically generated. The satiety attributes fit at a right angle to the hedonic direction, which is the only way to account for satiety. S1, S4, S5, and S8 are not even identified as DOLs (correlation coefficients of 0.24, 0.57, 0.14, and 0.35, respectively). IPM, as an unfolding technique
was thus unable to recover the starting configuration of the simulation used to generate the data. This is understandable as the theory behind IPM does not include satiety, but the problem for experimenters using IPM is that they usually do not know in advance that satiety will not occur. The set of important attributes identified by LSA is quite different (Fig. 6b). All the satiety attributes are found as DOLs, while the non-satiety attributes are not (correlation coefficients ranging 0.24–0.40). All the DOLs must exhibit satiety, as the optimal level will be close to the center of the map (at the average location of the ideal points), with overall liking decreasing when going on either side of the optimum. This simulated scenario illustrates the fact that an internal preference mapping on consumers’ individual hedonic ratings of a set of products can create a strong hedonic dimension when the process used by consumers to generate their ratings involves an ideal point. Consequently, the product structure that needs to be uncovered can be significantly altered, which in turn will result in important side effects in terms of the interpretability of subsequent analytical steps, such as the identification of the drivers of liking. The end result is that in situations where non-satiety attributes are true DOLs, IPM, and LSA as it will illustrated later on, will most
a
b Liking (r = 0.999)
P4 P10
Subject r2 = 0.99 Product r2 = 1
P7
P6
P9 P1
Comp 2 (28%)
Liking (r = 0.28)
P2
P5
P3 P1
P3
P8
P8
P5 P10
P6 P4
P2 P7
P9
Comp 1 (52%) Fig. 5. Simulation: IPM and LSA maps with regressed liking attribute. Greater circle sizes indicate lower average liking ratings.
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b
a S3 (0.89)
S2 (0.99)
S7 (1)
Comp 2 (28%)
N4 N3 (0.97) (0.96) N1 (0.95) N2 (0.95)
S6 (1)
Subject r2 = 0.99 Product r2 = 1
S5 (1)
S8 (1)
S4 (1)
S1 (1)
S7 S6 (0.97) (0.69)
S3 (1) S2 (1)
Comp 1 (52%) Fig. 6. Simulation: IPM and LSA maps with significant drivers of liking (a = 5%). Attribute correlation coefficients in parentheses.
likely be able to uncover them. However, in situations where they are not actual DOLs, these non-satiety attributes, which are often simply surrogates of liking, will be mistakenly identified as DOLs, while true DOLs that exhibit a satiety point will not be. 3. Real data
Table 2 List of 27 market studies analyzed using IPM and LSA. Number of studies
Product category
Location(s)
Number of products
Number of consumers
1
Salad dressing Meat products Cream cheese Mac & Cheese Mayonnaise Sliced cheese
US
27
318
US
13–22
198–272
US, Canada, Germany, Italy, UK, and Australia US
21–24
201–216
30
109, 318
US US, Canada, Spain, UK, Mexico, Italy, and Australia US US, France US
26 24–30
307 202–212
13 24, 20 19
207 270, 405 226
US
24
247
3
Following the learning from the simulation, the same analyses were conducted using actual market research data. They were extracted from 27 category appraisals conducted with different types and numbers of products, with different consumer sample sizes and in various countries. Table 2 summarizes the data sets that were analyzed. First the results of three of the studies will be summarized here. The first study involved 19 cheddar cheese samples that were evaluated by 226 consumers in the United States. Fig. 7a shows the first two components from the internal preference mapping analysis. Upon regressing liking onto the map, it is found to be a very strong driver with a correlation coefficient of 0.996. On the other hand, a Landscape Segmentation Analysis of the same data yields a space with an overall different product structure and an appreciably lower correlation coefficient for liking (0.65; Fig. 7b). This effect echoes that observed in the simulation. An even stronger illustration can be seen in Fig. 8. This figure summarizes the data collected in the US using 212 consumers who evaluated 26 samples of melted cheese slices served on a grilled cheese sandwich. As for the simulation and the previous example, a very strong hedonic dimension is uncovered, with a correlation coefficient for liking of 0.989 (Fig. 8a). In contrast, the LSA product structure is again noticeably different, with a correlation coefficient for liking of 0.16 (Fig. 8b). A third analysis that is interesting to consider is one where both LSA and IPM uncover a strong hedonic dimension. It is the case in the ‘‘SC Italy’’ data set where 205 consumers evaluated 24 processed cheese samples (see Fig. 9a and b). In this particular case, some of the strongest drivers of liking separate two groups of products, one with products receiving higher liking ratings situated to the right-hand side of the map while the other with products with –very– low average liking ratings on the left-hand side. Consequently, liking can be seen increasing linearly from left to right with drivers linked to appearance (orange to white color) and a specific cheese off-flavor (increasing from right to left) explaining that
6 2 1 9
1 2 1 1
Pizza Coffee Cheddar cheese Whipped topping
direction with high accuracy. LSA handled this situation by placing most of the ideal points close to the products with higher hedonic scores. This is an illustration that when at least some of the major drivers of liking exhibit a non-satiety behavior, LSA will adjust the resulting solution to accommodate this information. In such situations, LSA and IPM will generate similar spatial outcomes. The two analyses were conducted on each of the 27 data sets available and Fig. 10 summarizes the results. On this figure, the correlation coefficients for liking for each study are plotted for both IPM and LSA, the studies being ordered by decreasing order of the IPM liking correlation coefficient. As can be clearly seen, the effect observed in our simulation as well as in the three examples above does not follow a random pattern but is consistent across all 27 studies. Liking exhibits a very high correlation on the IPM maps in all 27 studies, systematically creating a strong hedonic dimension, while its value is much more variable for the LSA maps and will depend on the data set under consideration. Consequently, IPM consistently did not succeed in unfolding liking, since the exact information it is trying to unfold is found virtually unchanged on the plane explaining the greatest amount of variance in the data.
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a
b
Comp 2 (13%)
Liking (r = 0.996)
Subject r2 = 0.53 Product r2 = 0.98
Liking (r = 0.65)
Comp 1 (26%) Fig. 7. IPM and LSA analyses of the cheddar cheese data.
a
b r2
Subject = 0.50 Product r2 = 0.99
Comp 2 (15%)
Liking (r = 0.989)
Liking (r = 0.16)
Comp 1 (18%) Fig. 8. IPM and LSA analyses of the sliced cheese data (hot presentation, USA).
4. Discussion The results from the simulation along with those of the 27 category appraisals are quite consistent. While only 28 data sets are considered here, the authors have analyzed many others which resulted in the same outcomes. IPM maps published in the literature, or presented at conferences, show the same very clear tendency (for instance, see Felberg, Deliza, Farah, Calado, and Donangelo (2010), Pastor, Costell, Izquierdo, and Durán (1996), SerranoMegías, Pérez-López, Núñez-Delicado, Beltrán, and López-Nicolás (2005), Yackinous, Wee, and Guinard (1999), and Yates and Drake (2007) and numerous others). This effect is illustrated further by some work conducted by Meullenet and colleagues on Euclidean Distance Ideal Point Mapping. The method can be applied to any spatial representation of a set of products, including IPM. Each consumer is regressed onto the map and his/her ideal location, represented by a point, is esti-
mated so that the distance between the ideal point and each of the products is inversely proportional to the observed overall liking ratings. The strong hedonic dimension created by IPM will have the effect of locating the highest density of ideal points at the periphery of the map, close to the products that were liked the most and away from those that were liked the least. This effect is very clearly visible in Fig. 2 and Fig. 4 of Tubbs et al. (2010) and in Fig. 1 of Lovely and Meullenet (2009). Consequently, the optimal levels of attributes aiming in the direction of the greatest density of consumers, i.e. linearly correlated with liking, will be the highest (if positively correlated), or lowest (if negatively correlated), values exhibited in the set of products considered in the research, and thus incompatible with the concept of satiety. While the authors conceived a useful method to locate ideal points on any spatial representation of a set of products, an IPM space will distort the outcomes of the analysis. Their method would not have been
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a
b
Comp 2 (4.5%)
Subject r2 = 0.70 Product r2 = 0.999
Liking (r = 0.986)
Liking (r = 0.999)
Comp 1 (62%)
SC
M
It a ly ay on Ch na e d ise Sa da r la d W dr hi e s p s CC ped ing to U pp M S in &C g SC A d ul UK ts M &C CC K id C s CC ana Ita da SC ly U CC S c ol A d CC ust r U a lia SC K C SC ana Au da SC st c ol r d M alia SC exi co US CC h o G t Bo erm lo a n g P i na y zz a Ba co Ho n tD Co og f fe SC e U S Ca Co na f fe da ho SC e F t r Sp anc ai e n
Fig. 9. IPM and LSA analyses of the sliced cheese data (Italy).
1
Correlation of liking on map
0.9
0.8
0.7
0.6
Landscape Segmentation Analysis 0.16 Internal Preference Mapping
0.5 SC: Sliced Cheese CC: Cream Cheese
Fig. 10. Summary of the correlation of liking on the IPM and LSA spaces for the 27 studies.
successful at recreating the starting configuration used in the simulation described above. Also, issues would arise when studying potential segmentation based on ideal point locations and trying to classify the consumers in terms of their demographics or usage patterns. It needs to be noted that these comments apply to using the Euclidean Distance Ideal Point Mapping technique with IPM and that it can still have useful applications when using other product spaces such as those created using a principal component analysis of product sensory information, in an approach similar to external preference mapping. Also, the investigation of segmentation based solely on the map’s vector information is still suitable. It is important to note that this research does not imply that a vector based model such as IPM and an ideal point model such as LSA will always yield different solutions and interpretations of consumer studies, as was illustrated with the example depicted in Fig. 9. As one can see in Fig. 10 that while IPM always exhibited a high correlation for liking (>0.9), the same was true for at least a
few of the LSA analyses. Consequently, both techniques would yield similar spatial representation of the test products and as a result similar drivers of liking sets, as was illustrated with the SC Italy data set. This situation will occur when on at least one sensory dimension important to consumers, the products tested did not go over, or below, the optimal intensity level. Another example is the existence of an off-flavor in the products, whose presence systematically decreases overall product acceptability. As indicated earlier on, it is worth noting that if an attribute with a linear relationship to liking is a strong driver of liking, LSA will handle the overall space structure by locating a density of ideal points towards the periphery of the map so that products in the vicinity, which will have a high level of a positive attribute, or a low level of a negative attribute, will exhibit a high average liking value. A legitimate question is to wonder how large an impact these two modeling approaches will have on the nature of the findings, which is what any researcher will ultimately want to know. As
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one can expect, it will be very dependent on the difference in the unfolded product structure. Two interesting references are available which compared several product optimization methodologies. In the first one (Meullenet et al., 2007, chap. 5), LSA and IPM were compared directly and the predicted optimal profiles were fairly close, with the exception of a few attributes. Overall the authors conclude that the solutions were similar, even though they did not address the fact that the attributes with different predictions might be strong drivers of liking. In another paper (Lovely & Meullenet, 2009), the authors compared external preference mapping, IPM and LSA. In this case, there were greater differences among the ideal profiles predicted by the IPM and LSA methods, even though a confirmatory study did not find any significant difference between the two optimal products that were manufactured and tested with consumers. These were of course only two direct comparisons of the methodologies. It is worth mentioning that in both cases the overall product spatial structures were similar, thus resulting in similar predictions. Also, these analyses were conducted to find the optimal profile of only one product, i.e., a product that will be expected to satisfy the consumer population tested. However, companies very often investigate potential consumer segmentation to be able to target subgroups appropriately so as to optimize the appeal of their products. This will lead to the consideration of optimal product portfolios which will accentuate the differences in the solutions uncovered by different unfolding methodologies. In conclusion, what has been discussed here illustrates the need for further research on this topic. Nevertheless, there is now good evidence that internal preference mapping conducted on consumer overall liking ratings will create a spatial configuration of the products that might be incompatible with the actual underlying structure that it was trying to reveal since it will always find a driving hedonic dimension. In turn, this dimension will prevent the identification of drivers of liking exhibiting sensory satiety, i.e., an ideal point, and thus in many situations will produce biased results. This is something a sensory scientist should consider when using this technique and interpreting its findings. A central question to this issue is what is the underlying psychological process used by consumers to generate liking ratings? The simulation described earlier, which assumed that consumers use ideal points to generate liking ratings, resulted in an IPM map with a very strong hedonic dimension. As mentioned previously, simulations with the same assumption will always find such dimension in an IPM map. When considering actual market data, we showed here that an IPM analysis, at least for all the data sets that were analyzed and the published results that were reviewed, will always uncover an hedonic dimension. This might be the first indication that a model based on ideal points could be the most suitable to analyze consumer generated hedonic data. Finally, ease of access to multivariate analysis software makes it relatively easy to conduct analyses, such as those used in this paper. Interpretation of the results of those analyses depends very much on the user’s understanding of the assumptions made in constructing the models. Generally, one does not know the underlying structure, so there is no way to know how badly distorted the resulting map might be by inspection after an analysis. Tests of
goodness of fit may not be sufficient to avoid this problem. The best strategy is to think about the most likely structure underlying the data generating process and use modeling approaches that are as faithful as possible to that theory. Otherwise, one is very likely to end up with confusing, sometimes conflicting, results that have limited applicability. Note: This research was first presented at the 8th Pangborn Sensory Science Symposium in Florence, Italy, in July 2009. References Ashby, F. G., & Ennis, D. M. (2002). A Thurstone–Coombs model of concurrent ratings with sensory and liking dimensions. Journal of Sensory Studies, 17, 43–59. Busing, F. M. T. A., Groenen, P. J. F., & Heiser, W. J. (2005). Avoiding degeneracy in multidimensional unfolding by penalizing on the coefficient of variation. Psychometrika, 70, 71–98. Busing, F. M. T. A., Heiser, W. J., & Cleaver, G. (2010). Restricted unfolding: Preference analysis with optimal transformations of preferences and attributes. Food Quality and Preference, 21, 82–92. Coombs, C. H. (1964). A Theory of Data. New York: Wiley. Ennis, D. M. (1993). A single multidimensional model for discrimination, identification, and preferential choice. Acta Psychologica, 84, 17–27. Ennis, D. M. (2001). Drivers of LikingÒ for multiple segments. IFPress, 4(1), 2–3. Ennis, D. M. (2005). Analytic approaches to accounting for individual ideal points. IFPress, 8(2), 2–3. Ennis, D. M., & Johnson, N. L. (1993). Thurstone–Shepard similarity models as special cases of moment generating functions. Journal of Mathematical Psychology, 37, 104–110. Ennis, D. M., & Johnson, N. L. (1994). A general model for preferential and triadic choice in terms of central F distribution functions. Psychometrika, 59(1), 91–96. Ennis, D. M., & Mullen, K. (1992). A general probabilistic model for triad discrimination, preferential choice, and two-alternative identification. In F. Gregory Ashby (Ed.), Multidimensional Models of Perception and Cognition. Mahwah, NJ: Lawrence Erlbaum Associates. Ennis, D. M., & Rousseau, B. (2004). Motivations for product consumption: Application of a probabilistic model to adolescent smoking. Journal of Sensory Studies, 19, 107–117. Ennis, D. M., Palen, J., & Mullen, K. (1988). A multidimensional stochastic theory of similarity. Journal of Mathematical Psychology, 32, 449–465. Felberg, I., Deliza, R., Farah, A., Calado, E., & Donangelo, C. M. (2010). Formulation of a soy–coffee beverage by response surface methodology and Internal Preference Mapping. Journal of Sensory Studies, 25, 226–242. Gabriel, K. R. (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58, 453–467. Lovely, C., & Meullenet, J.-F. (2009). Comparison of preference mapping techniques for the optimization of strawberry yogurt. Journal of Sensory Studies, 24, 45–47. MacKay, D. (2001). Probabilistic unfolding models for sensory data. Food Quality and Preference, 12, 427–436. MacKay, D. (2006). Chemometrics, econometrics, psychometrics—How best to handle hedonics? Food Quality and Preference, 17, 529–535. Meullenet, J.-F., Xiong, R., & Findlay, C. (2007). Multivariate and probabilistic analyses of sensory science problems. Oxford, UK: Blackwell Publishing. Meullenet, J.-F., Lovely, C., Threlfall, R., Morris, J. R., & Striegler, R. K. (2008). An ideal point density plot method for determining an optimal sensory profile for muscadine grape juice. Food Quality and Preference, 19, 210–219. Pastor, M. V., Costell, E., Izquierdo, L., & Durán, L. (1996). Optimizing acceptability of a high fruit-low sugar peach nectar using aspartame and guar gum. Journal of Food Science, 61, 852–855. Serrano-Megías, M., Pérez-López, A. J., Núñez-Delicado, E., Beltrán, F., & LópezNicolás, J. M. (2005). Optimization of tropical juice composition for the Spanish market. Journal of Food Science, 70, S28–S33. Tubbs, J., Oupadissakoon, G., Lee, Y. S., & Meullenet, J.-F. (2010). Performance and representation of Euclidean Distance Ideal Point Modeling (EDIPM) using a third dimension. Food Quality and Preference, 21, 278–285. Yackinous, C., Wee, C., & Guinard, J.-X. (1999). Internal preference mapping of hedonic ratings for Ranch salad dressings varying in fat and garlic flavor. Food Quality and Preference, 10, 401–409. Yates, M. D., & Drake, M. A. (2007). Texture properties of Gouda cheese. Journal of Sensory Studies, 22, 493–506.