Comm 560 - Advertising Research Perceptual Mapping Perceptual mapping has been used to satisfy marketing and advertising information needs related to product positioning, 1 competitive market structure, 2 consumer preferences and brand perceptions. 3 Perceptual maps satisfy these types of information needs by analyzing and then translating consumers' numeric ratings, brand similarity data and brand preference data into a visual representation of how those consumers view the set of brands and products. 4 There are two approaches to perceptual mapping: attribute based and nonattribute based. Attribute based approaches require a respondent to evaluate a set of brands on a large number of specific attributes, typically those attributes felt to influence how consumers perceive, evaluate and distinguish among brands and products. Attribute based perceptual maps can be created through the use of one of three mathematical techniques: factor analysis, discriminant analysis and correspondence analysis. These approaches to attribute based perceptual mapping are discussed in the next section. Nonattribute based approaches require a respondent to rate brands in terms of similarities or preferences rather than attributes. A discussion of nonattribute based perceptual mapping is presented later. While attribute and nonattribute based approaches to perceptual mapping differ in terms of the types of data collected, both approaches share the fundamental assumption of perceptual maps: that consumers use broad dimensions to evaluate brands and products. The nature of dimensions Research indicates that consumers in most product categories try to evaluate brands using the least amount of time and energy. Consumers accomplish this goal by first identifying and then using a relatively few broad dimensions to compare brands or products. Consumers evaluate brands and products in terms of broad dimensions because, in doing so, they save time and energy. They can refer to and use a small number of broad dimensions instead of having to perform much more complex evaluations based on a larger set of narrower individual attributes. It is clearly easier to remember and act upon two or three broader dimensions rather than twenty or thirty individual attributes. All perceptual mapping attempts to make these dimensions explicit.
The approach to dimension creation differs according to the type of perceptual map and the corresponding differences in underlying data. Attribute based perceptual mapping creates dimensions from an analysis of the underlying brand and product attributes. For example, in the context of "gasoline," two dimensions that consumers might use to evaluate alternative brands of gasoline are "performance" and "convenience." Each dimension, in turn, is made up of a number of individual brand or product attributes. The dimension of gasoline "performance," for example, might contain the attributes: no knock, no run-on, smooth acceleration and quick acceleration. The dimension for "convenience" might contain the attributes: many locations; locations have many pumps; can pay by cash, charge or ATM; easy to pull in and out. Nonattribute based perceptual maps create dimensions from an analysis of consumers' evaluations of brand and product similarities or preferences. Here, dimensions reflect the implicit criteria consumers use to determine similarities and differences across brands or overall brand preference. The contribution of dimensions and mapping to decision-making Perceptual maps make an important contribution to advertising strategic planning. The visual presentation and underlying analysis represented in a perceptual map helps an advertiser understand: the number of dimensions consumers use to distinguish between brands or products. This information reveals the complexity of the product category from the consumer's perspective. Highly complex categories are those where consumers use a large number of dimensions to evaluate brands and products; less complex categories are typically those where fewer dimensions are used. the nature and characteristics of these dimensions. This information reveals the specific attributes or dimensions that consumers use to distinguish among products. the location of actual brands, as well as the ideal brand, on these dimensions. This information reveals consumers' evaluations of the advertiser's product versus other products and versus the ideal product on dimensions of importance. Further, it makes explicit from the consumers'
perspective, a brand's most direct competitors and provides a basis for determining the extent to which future advertising should reinforce or seek to change the brand's current positioning. The types of insights provided by perceptual mapping are illustrated in the hypothetical perceptual map of the beer category shown in the figure below. 5 First, the perceptual map indicates the two primary dimensions used by consumers to evaluate brands of beer. The horizontal dimension relates to quality while the vertical dimension relates to strength and taste. Second, the map identifies the specific characteristics of each dimension. The horizontal dimension is anchored by expensive, fine beers consumed when dining out and low quality beers drunk when alone. The second dimension is anchored by pale, sweet beers and fullbodied, malty beers. Third, it appears that consumers use these dimensions to separate beer brands into six distinct groups.
* Brands A, B and D are perceived similarly. These brands are all seen as very low in price and quality and are appropriate only for drinking alone. Their taste falls in the center of the continuum, neither too sweet nor too malty. * Brands G and H are also seen as very low in price and quality and are appropriate only for drinking alone. These brands, however, are felt to have a very full bodied, malty taste. * Brands C and E are felt to be of average quality and expense and are appropriate for drinking alone or dining out. These brands are felt to be very pale and sweet. * Brands K and L are also felt to be of average quality and expense and are appropriate for drinking alone or dining out. These brands are also felt to have a taste that falls in the center of the continuum, neither too sweet nor too malty. The perceived characteristics of these brands places them closest to the "ideal" beer. * Brand J has no direct competitors. It is seen as high quality, expensive and only appropriate for dining out. It is also felt to be pale and sweet. * Brands F and I are also seen as being high quality, expensive and are only appropriate for dining out. These brands, however, are felt to have a taste that falls in the center of the continuum, neither too sweet nor too malty. Fourth, the groupings of beer brands along the two dimensions provide important insights into brand positioning, both with regard to the "ideal" brand and competitive products. Brand G, for example, is seen as an inexpensive, low quality, full bodied, malty beer that is primarily consumed when drinking alone. It is a beer that most directly competes with Brand H and is considered to be far from the "ideal beer." Finally, the perceptual map provides a starting point for discussion of advertising strategy. Brand G, or any other beer brand, can examine the perceptual map and then decide if their brand's position and competitive set is acceptable (and thus should be supported in the advertising) or unacceptable (and thus should be addressed by advertising designed to alter brand perceptions).
Attribute-Based Perceptual Maps Attribute based perceptual maps make explicit the broader dimensions consumers use to evaluate and distinguish among brands and products. Attribute based perceptual maps begin with the creation of a list of specific product category attributes. Because dimensions are constructed from individual attributes, it is very important that the list of brand and product attributes contain all attributes that are known to be (or that judgment or research indicates could potentially be) important in consumers' evaluation of target brands or products. Important dimensions cannot be discovered in the absence of their component attributes. Next, semantic differential or Likert rating scales are developed. These scales enable respondents to rate each brand or product on each attribute. A consumer might, for example, be asked the following question to assess perceptions of brands on the attribute of "expense": Rate each brand of beer shown below on the basis of expense. Place a number after each brand of beer to indicate how expensive or inexpensive you feel that brand is. You can use any number between '1' (to represent "not at all expensive") and '10' (to represent "extremely expensive"). The rating of all brands on all attributes, and the subsequent mathematical combination of attributes into broader dimensions, reflects the underlying assumption of attribute-based perceptual maps, that is, that a respondent's "rating or judgments about specific attributes are manifestations of the underlying or latent dimensions that [they] use to distinguish between brands." 6 The next step in the development of an attribute based perceptual map is the selection of a mapping technique. Three approaches may be used: factor analysis, discriminant analysis and correspondence analysis. Factor analysis A positive correlation coefficient indicates that two measures move together in the same direction, for example, both measures tend to receive either a high or a low rating. A negative correlation coefficient indicates that two measures move in opposite directions, for example, one measures tends to receive high ratings at the same time the second measure tends to receive low scores.
Examining and interpreting the correlation of one pair of measures is simple. You simply note the direction and magnitude of the correlation coefficient. Examining and determining the meaning of the pattern of correlation coefficients for multiple measures is more difficult because of the large number of correlation coefficients that need to be examined. The intercorrelations of fifteen measures, for example, result in 105 pairs of correlations. Factor analysis responds to and solves this problem. Factor analysis is a statistical technique that identifies the relatively small number of factors or dimensions which represent the relationships among a large number of interrelated variables." 7 The process of factor analysis begins after consumers rate each of the target brands or products on each individual attribute. A factor analysis computer program examines the set of ratings data and calculates the correlation coefficient for each pair of variables. These correlations are the basis of the factor analysis. "The basic assumption of factor analysis is that underlying dimensions, or factors, can be used to explain complex phenomena. Observed correlations between variables result from their sharing these factors." 8 After correlation coefficients have been calculated for each pair of variables, factor analysis moves through the following steps: 1. The factor analysis computer program examines all pairs of correlation coefficients and then creates enough factors (typically equivalent to the number of variables) to account for 100% of sample variance. 2. The program calculates three important pieces of data for each factor. This data is illustrated in the table on the next page which displays fourteen factors formed from the hypothetical ratings of eleven brands of big screen/projection television sets on fourteen attributes. 9
Factor Eigenvalue Percent of Variance 1 6.71 47.9 2 4.51 32.2 3.64 4.6 4.54 3.9 5.46 3.3 6.42 3.0 7.21 1.5 8.17 1.2 9.11.8 10.07.7 11.06.7 12.06.7 13.02.1 14.02.1 100.0% The first column reports the factor number. The second column reports eigenvalue, the total variance explained by each factor. Eigenvalues greater than one typically indicate important factors while eigenvalues less than one typically indicte factors that are less important. Factor importance therefore increases as eigenvalues increase. The third column translates eigenvalues into percentages. The total of the eigenvalue column is equal to the number of variables. Thus, factor one, with an eigenvalue of 6.71, accounts for 47.9% of total sample variance (calculated as 6.71 14). The percent of variance is a very important calculation and indicates a factor's contribution to an understanding of the underlying pattern of response. Larger percentages indicate a greater contribution. 3. The researcher examines eigenvalues and percent of variance explained by each factor and selects the number of factors to be used in subsequent analyses. Typically, a researcher tries to select the least number of factors
that explain the highest amount of sample variance. (Obviously, "high" is a relative term that will vary from study to study.) In this example, Factor 1 and Factor 2 would most likely be selected. These two factors together account for 80.1% of the total variance. Adding additional factors results in little gains in explanation of total variance. 4. The factor analysis computer program reanalyzes the data restricting the number of factors to that specified in the prior step. 5. A factor loading for each measure is computed and examined. A factor loading is an indicator of the degree of association between an individual measure and a factor. Similar to a correlation coefficient, a positive factor loading indicates a positive association between the measure and a factor while a negative loading indicates a negative association. The factor analysis program then examines the patterns of factor loadings and generates a table in which variables are ordered to reflect their factor loadings, as shown in the table on the next page. This reordering permits the underlying pattern of association between measures and factors to be clearly seen.
Attribute Factor 1 Factor 2 Audio response +.876 -.025 Stereo separation +.775 +.122 Color accuracy +.712 -.122 High light viewing +.698 +.130 Low light viewing +.651 -.252 High sound reproduction +.599 -.197 Low sound reproduction +.489 +.058 Picture sharpness +.477 +.139 Ease of set up -.199 +.854 Quality of instructions -.258 +.721 Programming ease -.158 +.699 Remote control ease +.025 +.571 Picture in picture ease +.066 +.542 Visual displays -.258 +.426 6. The researcher examines the factors represented in the map and the attributes comprising each factor and then creates a name for each factor. Remember, the factor analysis computer program merely uses mathematical computations to identify the factors. The researcher must determine what the factors represent. Factor 1 contains attributes that directly relate to the viewing experience while Factor 2 contains attributes related to set up and usage. (It is interesting to note that Factor 1 contains attributes that relate to both audio and video. The factor would be quite different and would be
given a different name if audio and video attributes were associated with different factors.) 7. The factor analysis program calculates an average factor score for each brand. This score represents the average rating of each brand across the measures comprising an individual factor. The average factor scores for the eleven brands of big screen/projection televisions are shown in the table below. Brand Code Factor 1 Factor 2 A -1.3-1.8 B -1.7 +1.1 C -1.6-1.6 D +1.4-1.5 E -1.6-1.2 F +1.4-1.4 G +1.6 -.7 H -1.5 +1.5 I -1.3 +1.7 J -.3 +.3 K.1 -.1 IDEAL 1.5 +1.6 8. The average factor score is used to plot the brands on the perceptual map (see next page). Note how the brands tend to cluster into four groups and the absence of brands from the area that indicates the brand is both easy to set up and use and provides an excellent viewing experience. The absence of brands from this important area, which contains the ideal brand, clearly indicates an unmet niche and a marketing and advertising opportunity.
The prior example provided the simplest example of perceptual mapping using factor analysis. However, there are often times when more than two factors are identified or when more detail on the perceptual map is required for a true understanding of consumers' perceptions. Each of these situations is handled in the following manner. The presence of three or more factors requires that a perceptual map be formed for each pair of factors. Thus, a case in which there are three factors (Factor A, Factor B and Factor C) requires that three perceptual maps be created (Map 1: Factors A and B; Map 2: Factors A and C; Map 3: Factors B and C). Examining brand positions and competitive sets across multiple, related perceptual maps provides a comprehensive view of how consumers use important dimensions to evaluate and distinguish among brands and products.
An Example: Using Attribute-Based Perceptual Mapping to Measure Advertising Impact A study conducted by the advertising agency D'Arcy Masius Benton & Bowles 10 demonstrated how perceptual mapping can help to determine: * the dimensions by which consumers differentiate between brands in a product category, * how consumers perceive specific brands on these dimensions prior to advertising exposure, * the effect of advertising on dimensions used to differentiate between brands, and * the effect of advertising on consumers' brand perceptions. The study began by asking respondents to rate twelve automobile manufacturers on 15 attributes (such as quality, sporty, technologically advanced, etc.). The results of these initial ratings are shown the Pre-exposure Perceptual Map shown on the next page.
The perceptual map shows that, prior to advertising exposure, consumers use two dimensions to distinguish among car manufacturers. One dimension relates to type of driver and affordability. This dimension is anchored by "affordable, young person's car" (exemplified by manufacturer M) and "luxurious, comfortable, older person's car" (exemplified by manufacturers F and G). The second dimension relates to car characteristics and is anchored by "a family car" (exemplified by manufacturer B) and "high quality technologically advanced car" (exemplified by manufacturer I). Following this initial rating, respondents were exposed to multiple advertising campaigns. Each respondent viewed six television commercials and read two print ads for each automobile manufacturer evaluated in the initial ratings. Next, respondents once again rated each manufacturer on the same attributes used in the pre-exposure ratings. The perceptual map that resulted from this second set of ratings was very different than the pre-exposure map (see Post-Exposure perceptual map shown on the next page). The postexposure map showed four ways by which the advertising affected consumers' perceptions of both the automotive category and individual automotive brands.
* First, the advertising appears to have changed the dimensions with which consumers evaluate and distinguish among automobile manufacturers. The dimension displayed on the vertical axis changed from "family car - a high quality technologically advanced car" to "family car - exciting powerful fun car." This suggests that the advertisers have succeeded in changing the criteria by which consumers distinguish among car brands. * Second, it is this new dimension, "family car - exciting powerful fun car," that most differentiates car manufacturers. Manufacturers on the preexposure perceptual map were dispersed along both dimensions with many manufacturers at the extremes. The post-exposure perceptual map shows less differentiation along the horizontal axis (more brands now appear near the center) and more differentiation on the vertical axis. * Third, advertising does appear to affect manufacturer image. While some manufacturers' images (such as A, D, F, G, H) were relatively constant
(either as a result of the advertising purposively reinforcing that image or failing to change that image, if desired), other manufacturers showed great change in image. Consumers changed their perceptions of manufacturers J and K, for example, from a "high quality technologically advanced car" to "a family car." * Fourth, competitive sets appear to have changed for some manufacturers. Brand I, for example, which had no close competitors on the pre-exposure map, appears to compete with a number of manufacturers on the postexposure map. Similarly, manufacturers B and C, which were relatively far apart on the pre-exposure map, are seen as very close competitors on the post-exposure map.
1. Wayne S. DeSarbo and Vithala R. Rao (1984), "GENFOLD@: A Set of Models and Algorithms for the GENeral infolding Analysis of Preference/Dominance Data," Journal and Classification 1 (Winter), p. 147-186; Yoram Wind (1982), Product Policy: Concepts, Methods and Strategy, (Reading, MA: Addison-Wesley Publishing). 2. Rajendra K. Srivastava, Mark I. Alpert and Allan D. Shocker (1984), "A Consumer Oriented Approach for Determining Market Structures," Journal of Marketing 48 (Spring), p. 32-45; Allan D. Shocker and David W. Stewart (1983), "Strategic Marketing Decision Making and Perceptual Mapping," in F. S. Zufyden (ed.), Advances and Practices of Marketing Science - 1983 Proceedings (Providence, RI: Institute of Management Science) p. 224-239. 3. For a review of research in these areas see Lee G. Cooper (1983), "A Review of Multidimensional Scaling in Marketing Research," Applied Psychological Measurement 7 (4), p. 427-450. For specific applications see C. Carl Pegels and Chandra Sekar (1989), "Determining Strategic Groups Using Multidimensional Scaling," Interfaces 19 (May/June), p. 47-57; Grahame R. Dowling (1988), "Measuring Corporate Images: A Review of Alternative Approaches," Journal of Business Research 17, p. 27-34; George S. Day, Allan D. Shocker and Rajendra K. Srivastava (1979), "Consumer-Oriented Approaches to Identifying Product Markets," Journal of Marketing 43 (Fall), p. 8-20. 4. The most common use of perceptual mapping in advertising and marketing research relates to brand perceptions. However, perceptual mapping is appropriate for exploring perceptions of any set of objects, for example, types of television programs or political candidates. Perceptual maps can also be used to determine similarities and differences across groups of consumers. 5. This perceptual map is based on research conducted by Market Facts. The dimensions reflect the actual dimensions consumers use to evaluate beer brands. See Richard M. Johnson (1971), Market Segmentation: A Strategic Management Tool," Journal of Marketing Research 8 (February), p. 13-18. The placement of brands on the map, however, is not based on research and is for illustrative purposes only. 6. Tull and Hawkins, Marketing Research, p. 372. 7. Marija J. Norusis (1990), SPSS Introductory Statistics Student Guide, (Chicago, IL: SPSS Inc.) p. 321. 8. Norusis (1990), SPSS Introductory Statistics, p. 322. 9. The data presented in this section is hypothetical and for illustrative purposes only. Brand names are represented by capital letters.
10. The discussion in this section is based on Charles I. Stannard (1990), "Perceptual Mapping and Cluster Analysis: Some Problems and Solutions," Quirk's Marketing Research Review, March, p. 12-22.