Editor's note: The following article was adapted from a presentation made at the 1989 Sawtooth Software Conference. Charles Stannard is senior vice president, director of research and marketing at the Bloomfield Hills, Mich. office of D'Arcy Masius Benton & Bowles.
This article will discuss some common problems and issues analysts have to deal with in studies using perceptual mapping and cluster analysis. It will describe the problems and the various ways we dealt with each of them in specific studies.
The first problem, common to much marketing and advertising research, is how to deal with owners and nonowners of a brand. The context for this discussion is a large study of the appliance category. The second issue concerns evaluating segments based on cluster analysis. The data are also from the appliance study. The third issue concerns the use and interpretation of maps in advertising research. Here the data come from a study of the automotive category.
Dealing with owners and nonowners
The context for the discussion of owners and nonowners is a large positioning study conducted for a major maker of appliances. The study was done a year ago to assist the development of image objectives for the brand. We wanted to understand the characteristics (attributes and benefits) by which purchasers of major appliances distinguish manufacturers, and to determine the importance of these characteristics in the purchase decision. The project had two phases: a qualitative phase to learn which attributes and benefits consumers use to differentiate among manufacturers, and to understand qualitatively the process by which consumers purchase major appliances; and a quantitative phase in which we quantified and tested what we learned in the qualitative phase.
Any time we seek information from consumers, the problem of to whom do we talk confronts us. This problem is very similar to the problem confronting anthropologists studying a strange culture. In anthropology it is called the problem of the informed informant. Whether we are anthropologists in New Guinea or market researchers in the United States, the problem is the same; while just about everyone we question will respond with an answer, not all "answers" are equally valid and valuable. Naturally we want good answers, but since the canons of objectivity, not to mention feasibility, prevent us from ruling on the "goodness" of each and every answer, we move from evaluating answers to evaluating "answerers."
In choosing to whom we talk, we estimate whether it is reasonable to expect that a given person will provide us with good answers. The key criterion we use to judge whether a person can give us good answers is whether he or she is likely to be knowledgeable about the subject we are investigating. Typically, we make these judgments based on whether a person can indicate experience with the subject in which we are interested. An important indicator of experience is ownership or use of specific products or brands. We codify the criteria for judging the likely value of a respondent in the qualifications he or she must meet to enter the study.
The major appliance category (refrigerators, ovens and ranges, dishwashers, clothes washers, dryers, and microwaves) has several characteristics that determined the requirements people had to meet to participate in the study. The first is that the repurchase cycle is quite long-10 or more years. Moving and remodeling can shorten the cycle, but typically consumers are only sporadically in the market, usually after a long absence. Second, while consumers frequently have several different brands of appliances in their homes, they often cannot list them by brand when asked in an interview. Finally, except for moving or remodeling, it appears that many purchases are unanticipated, being a quick response to the actual or expected failure of the product.
These characteristics indicate that most consumers have little current knowledge about manufacturers and brands. They become interested in the category when they are about to purchase one or more appliances. Sometimes, as in remodeling and moving, the purchase is foreseen and the search for information about products and manufacturers can be leisurely and thoughtful. In other instances, when a current product fails, the search process is much more hurried and even haphazard. In either case, we think people move from a state of relatively low awareness and knowledge of manufacturers and their product offerings to one of relatively high awareness and knowledge in a short period of time, which is characterized by a comparatively vigorous search for brand and product knowledge.
For our purposes, therefore, we wanted a sample of people who would have greater than average knowledge of and involvement in the appliance category. They would better represent those people who are in the market for an appliance and thus would provide a better picture of the market from their point of view. Therefore, in addition to the usual demographic, appliance and brand ownership qualifications, we wanted people who were recently in the market for a major appliance, or anticipated being in the market in the near future. They best represented the state of mind and knowledge of consumers at the time of purchase and are the target of the advertising and marketing efforts.
In addition to choosing the right people, we also have to ask them the right questions. In a positioning study, we typically ask respondents to do two things. First, we ask them what attributes are important in distinguishing between brands. Then we have them rate brands on the attributes. Choosing the right questions means asking them to rate brands they are familiar with on attributes that are important to them in choosing between brands in the purchase decision. Since the number of attributes and brands of interest was too large-7 brands and 28 attributes-for any one person to rate all combinations of brands and attributes, we had each respondent rate 4 brands on 12 attributes. The brands and attributes were selected as follows: Each person rated the client's brand and three other brands with which he or she was familiar. The attributes were classified a priori into six categories based on their content. Each person rated two attributes in each category. The specific attributes were the two he or she rated most highly in each of the six categories.
The major part of the analysis of the appliance market involved producing a map of the market that located brands and attributes. The advantages of maps are well known. They provide an economical summary of a great deal of data on brands and attributes, in our case 7 brands and 28 attributes. Another advantage of maps is that the audience, usually managers, often finds them easier to understand, more revealing, and certainly more interesting than other ways of presenting the same data. From the analyst's and presenter's points of view, maps are often easier to present and interpret for an audience than are complex tables of numbers and coefficients.
Having thoroughly considered-or so we thought-the important issue of product and brand ownership, we were chagrined to discover that our initial map did not make a great deal of sense. In analyzing it we found much less discrimination among brands than we expected, and what appeared to be some odd juxtapositions of brands. We found some of the large, middle-range brands were positioned very close to smaller, expensive and high-quality brands. Everything we knew about the market suggested that consumers perceive the smaller brands as different from the larger/middle range brands.
Thus, instead of shouting "Eureka!," we invoked the first rule of nonsensical analysis: whenever we find something truly new and unexpected in an analysis, look for an error-either in the logic of the analysis or in the data themselves. We know from experience that the odds favoring an error are much greater than those favoring the discovery of something truly new.
We identified two related aspects of brand ownership as possibly causing the strange map. First, the proportion of the sample owning specific brands varied greatly, mirroring the reality of the marketplace. Second, as is usually the case, people rated more highly the appliance brands they owned than the brands they did not own. In fact, the differences between brand owners and nonowners were greater in many instances than the differences among brands, when ownership was controlled. In combination, these two aspects of brand ownership in our sample could be the reason the larger brands ended up in close proximity to some of the smaller, more expensive and higher quality brands.
The obvious solution, if these were the cause of the problem, was to separate owners and nonowners. We did this by creating 14 brands, seven as seen by owners and seven as seen by nonowners, and estimated the space using 14 brands-the seven original owner brands plus the seven nonowner brands. This approach has the advantage of using all the information (i.e., the total sample of ratings) in the sample, rather than a portion of it, as would be the case if the space were created using only owners. The disadvantage is that it can be difficult to crease mutually exclusive and exhaustive groups of owners and nonowners when there is extensive multiple ownership of brands.
We then re-estimated the space, this time using the 14 brands. The first function or dimension captured the differences between owners and nonowners. It grouped at one end the owners of the various brands and placed the nonowners of the brands at the other end. Furthermore, there was no overlap between brand owners and nonowners of the brands. In effect, the first dimension accounted for the effects of ownership on the brand ratings.
We based our map on the next two dimensions, which successfully described the marketplace. One dimension was price/value. Brands at one end of the dimension were characterized as offering the lowest prices for comparably featured appliances; brands at the other end of the dimension were seen as saving money in the long run. The third dimension described quality in two different ways. One was called "promised quality." Brands offering promised quality were highly recommended by others and promised to honor warranty claims without hassle or difficulty. The other end of the third dimension was "experienced quality." Brands offering experienced quality were seen as a pleasure to own and extremely durable and long lasting.
Thus, we eliminated the negative effects of brand ownership by creating brands to represent nonowners, estimating the space with the owner and nonowner brands and discarding the first dimension. This worked because owners rate their brands higher than they rate brands they do not own, and these differences, in addition to being consistent, are also substantial, generally being greater than the differences among brands. This explains why it was the first dimension. The fact that in most categories owners rate their brands higher than do nonowners suggests that what we did in the appliance category may have greater utility and generality as a solution to the problem of owners and nonowners.
Segments in positioning research
In addition to mapping the usual groups or segments of consumers like users and non-users, men and women, and so on, it is also possible to map segments derived from psychographic data. The latter segments emerge from the data in cluster analysis, as opposed to the former, which are predetermined according to explicit criteria. Because the segments in cluster analysis are based on psychographics, they can provide richer and fuller explanations of behavior and market structure. That is, instead of saying, based on the interpretation of a map, people choose brand A because it is low priced and readily available, we can, in the ideal case, elaborate on the reasons people choose Brand A. For example, we might find that the people choosing Brand A really make up two different segments, one which is very price sensitive because of low family income, and another which has very little interest in the category and therefore opts for the low-priced, convenient brand in this category. Of course, this is the promise of psychographically-based segments. In reality, though, we know promises are not always kept.
In our appliance positioning study we also included 12 psychographic statements relating to appliances and shopping in addition to the 28 appliance manufacturer attributes. The psychographic statements included items like "In buying major appliances the reputation of the store is more important than the brand name," "When buying appliances, it pays to buy the best model even though it is more expensive," "It is more important to have good appliances in the home than good furniture."
Clustering the items produced four consumer types. We named them "Flashy Flora and Fred," "Needy Nan and Neil," "Classy Carl and Cristy," and "Apathetic Al and Ann." The demographic and psychographic portraits of the groups appeared to have integrity and make sense. For instance, Needy Nan and Neil, as their name implied, had the lowest total family income, with 46 percent of them having total incomes of less than $25,000. Concomitantly, they were the least educated, with 40 percent having a high school education or less. They also had the largest families and were the second youngest of the clusters.
Their attitudes towards appliances fit their demographics. Nan and Neil were very price sensitive. They wanted to buy the lowest priced appliances from among similar makes and models. At the same time, they had to have appliances that lasted, more so than any of the other clusters. Their extreme price sensitivity created a problem for them. They could not rely on the brand name-an important indicator of quality and durability-to help them choose the best and lowest-priced brand of appliance. As a result they had to look to other sources of information to help them choose among brands. They, more than the other segments, relied on two sources to help them do this. One was Consumer Reports. The other was whether the thought the manufacturer was a specialist in kitchen or laundry appliances. They took specialization as an indication of durability, an important attribute in appliances for them.
Classy Carl and Cristy, by way of contrast, were the wealthiest segment. They had the highest household income (7 percent were over $35,000), were the oldest on average, and had the largest homes as indicated by the number of bedrooms and bathrooms. This segment also had the largest number of college graduates of any segment. Their views about appliances were quite different from Needy Nan and Neil's. Classy Carl and Cristy were not very price sensitive. They were the least likely of all segments to look for the least expensive brand of appliance. Rather, they thought it paid to buy the best mode] appliance, even though it was more expensive. For them. however, having appliances that were a pleasure to own was also very important, as were appliances that were easy to clean and keep clean. Their attitudes towards appliances were echoed in their views about their kitchens: they were very proud of them and the way they looked. Indeed, it is likely that Carl and Cristy judged kitchen appliances for the looks as well as for their quality and features. Perhaps because they bought the tees, appliances, Carl and Cristy, of all the segments, had the most positive attitudes towards appliance makers. This was manifest in their agreement with those statements that implied a willingness of manufacturers to value customers and stand behind their products.
While the portraits that cluster analysis creates can be interesting and plausible, it is important that they relate to product ownership and usage in intuitively meaningful ways. In the appliance category, we expected to find sharp difference, among the clusters in brand penetration. For instance, we expected to find penetration of the more expensive brands to be greater for Classy Carl and Cristy than for Needy Nan and Neil. And we expected the opposite penetration for the lower-priced brands.
In fact, however, we did not find the expected pattern of brand penetration among the segments. Instead, we found that brand penetration was relatively flat among the segments. This was puzzling and demanded an explanation. In thinking about the purchase process, however, an explanation of the lack of differential brand penetration suggested itself. The explanation focused on two aspects of the retail side of the appliance business. First, retail sales are increasingly dominated by "power retailers" that continually have sales featuring specific brands. Second, appliances are as much sold as they are bought. Salespeople often receive "spiffs" or special sales inducements above the regular commission from manufacturers for sales of their brand or specific models of their brand. When this occurs, salespeople work hard to steer people toward these brands, with a fair amount of success, according to them. When we take these two aspects of the market into account, the lack of differential brand penetration among segments in brand penetration might reflect a retail reality which is working against the manufacturers' efforts at creating and sustaining brand character and differentiation.
This certainly was a plausible explanation of our findings. The question now was whether to show the segmentation results in conjunction with the perceptual maps. After some discussion we decided not to show the segmentation results. We though that they would be hard to interpret; essentially explaining the absence of differences is much harder than showing and explaining differences. In this case, it would be even harder since the argument was both long and subtle. And, since the results of the segmentation added little to our overall understanding of the appliance, not presenting them could be done with little loss.
Parenthetically, I would argue against advancing very subtle explanations of data except when absolutely necessary. While we may appreciate our subtlety and cleverness in teasing our implications and formulating explanations, they can be lost on our audiences and can confuse them as well.
Assessing advertising with perceptual mapping
Perceptual mapping is often used to determine the actual or desired positioning of brands. The results of such analyses, as was the case in the appliance category, frequently become the basis for efforts at repositioning a brand in consumers' minds. We use perceptual mapping much less often to assess whether advertising is in fact positioning brands in the desired ways. This section will present results from a study that uses perceptual mapping to assess how advertising is positioning manufacturers.
The data come from an ongoing study of the automotive category. The study is designed to assess the effect of advertising on the images or positionings of various manufacturers. The aim of the study is to determine in which direction on a map the advertising for specific manufacturers is moving the images of these manufacturers. Of course, not all directions are equal; the desire is that the advertising will move in a direction consonant with the desired and agreed upon positioning of the specific manufacturer, and this will be the only manufacturer moving in that direction.
In the study, respondents first rate several automobile manufacturers on 15 image attributes (quality, sporty, technologically advanced, and so on). They then see six commercials and read two print ads for several manufacturers and rate each manufacturer based on what the commercial or ad communicates about the manufacturer. The research is unique in that it attempts to assess simultaneously the effects of many campaigns, as opposed to individual commercials and ads, on the perceptions of many manufacturers.
There are two ways to determine the impact of advertising on the images or positions of automobile makers. Both ways begin with a map showing the structure of the market prior to exposure to the advertising. The structure is shown in Figure 1.
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This map shows that people distinguish among manufacturers in the following ways. On one dimension they see cars that offer value and appeal to younger people; M best exemplifies this type of manufacturer. At the other end of this dimension they see cars that appeal to older people and offer more power and luxury; E is an example of such a maker. The other dimension has "technologically-advanced" and "high-quality" as its defining characteristics on one end, and family cars on the other end of the dimension. Both the dimensions and the placement of the makers make sense to people familiar with the automotive category. From the map it appears as though consumers have fairly clear pictures of a number of cars. Where there is confusion in images, it is primarily among the American manufacturers who are the largest producers in the United States market and have had the greatest difficulty in differentiating the many models and brands they produce. The classification analysis bears this out. Overall we correctly classify 33 percent of the respondents, but the correct classification by maker varies from 13 percent for a domestic manufacturer to 74 percent for a foreign maker.
It is after exposure to the advertising that we have alternative ways of looking at and portraying the structure of the market. One option is to apply the original structure to the post-advertising ratings of each car; the other is to re-estimate the structure using only the post-advertising ratings. We have done this and the results of these two options are quite different.
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Figure 2 presents the results of applying the original structure to the post-advertising ratings. This map is radically different from the market represented in Figure 1. All of the makes are now located in the lower left quadrant, whereas originally only makes I, J, K, and M were there. Clearly, drastic changes have occurred, changes that most advertisers would not be pleased with. Figure 2 implies much less differentiation among brands based on the advertising.
This is apparent when we look at our ability to correctly classify people based on their post-advertising ratings of manufacturers. Whereas originally we could correctly classify 33 percent of the respondents, our ability drops to 6 percent based on the post-advertising ratings. Looking at the map it appears as though every maker's advertising is directed against the same strategy and communicating the same message.
Figure 3 presents the results of re-estimating the structure using the post-advertising ratings. This produces a very different picture from Figure 2. There is greater dispersion among the manufacturers than in Figure 2. With the exception of maker I, which in the original map was away from the center and closest to H, the general pattern seems similar to the original map. At least, we could all agree that this one might be based on the original structure, whereas we would be much harder pressed to agree with this statement regarding Figure 2. And our ability to correctly classify makers is not different from our ability in Figure 1, 34 percent versus 33 percent.
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What do these two very different maps tell us? Should we use both to understand what is happening, or should we choose between them? I think each tells us something important about what the advertising is communicating about manufacturers and how it is working.
Figure 2 tells us two things. First, it says that the original structure does not adequately describe the market based on the exposure to the advertising. There is virtually no differentiation among makers, and only 25 percent of the space is being used. At the same time, this map tells us something very important from a marketing sense. It says that everybody seems to be singing the same song about their cars. Everybody wants people to think their cars are youthful, offer good value for the money, and are technologically advanced and high quality. The net result is that manufacturers are blurring, rather than sharpening, their images.
Figure 3 tells us how the structure has changed based on the advertising, and thus provides insights into how the advertising is working. The horizontal dimension, the first dimension in this and the original solution, remains basically the same, describing characteristics of cars that are seen to appeal to older and younger people.
It is the second dimension that changes after advertising. Instead of being a family/affordable car versus high quality and technologically advanced car dimension, it changes to family/ affordable car on one end to exciting, powerful quality car on the other end. This suggests that the advertising is attempting to change the relative importance of the criteria people use to judge cars. Another way of saying this is that the model of advertising as agenda setting appears to describe the way advertising is working in the automotive category.
By examining both maps, we have learned some important things about automotive advertising. There may be a lesson in this for mapping studies that are repeated at regular intervals. The lesson is that perceptual mapping can demonstrate the direction of change as well as the changes in the underlying structure of consumers' perceptions of the marketplace. Each complements the other and adds to our understanding of consumers and the structure of the marketplace.
Summary
This article discussed three different issues in positioning research. It offered a way of dealing with the potential problem of owners and nonowners producing spurious or misleading maps. The solution was to create owner and nonowner brands and estimate the space using owner and nonowner brands. We suggested that it was likely that one dimension would differentiate owners and nonowners and thereby eliminate their effects from the other dimensions. The article also discussed using segments based on cluster analysis in maps. The example discussed showed that there may be occasions when it is better not to display the segments. Finally, the article showed how maps can be used to assess advertising.