MEASURING ADVERTISING EFFECT BY PERCEPTUAL MAPPING: A CAUTIONARY TALE

In this study author has discussed about how the use of multidimensional scaling for the identification of static consumer perceptions and preferences has been extended to Perceptual mapping of advertising effect change. A variety of techniques mentioned below have been already used by many other authors to operationalize the concept.
• Factor analysis has been used to plot individuals' positions in a product attribute space both before and after their exposure to a massive advertising campaign.
• Individual differences scaling has been used to evaluate the differences in perceptions held by subjects in different experimental conditions
• Nonmetric scaling of brand similarities data has been used to compare subjects' perceptions of and preferences for breakfast cereals in two experimental conditions
• Perceptual mapping of attitude change has been used to compare groups or individuals through time or between experimental conditions.
As we know that Information presented in visual form is readily assimilable. This property of communication speed and impact carries with it the inherent disadvantage that it is all too easy to transmit misinformation through visual data. This temptation is especially strong when the information is in the form of an effect which is visually "obvious", To make this clear author has provided an illustration of the misinformation capacity of multidimensional scaling.
ILLUSTRATION:
• Data on breakfast cereal perceptions were collected as supplementary information during a study of the effects of variety in advertising.
• Three hundred housewife subjects were randomly assigned to two experimental conditions.
• The 150 subjects assigned to condition X were exposed to a campaign consisting of two 30 second television commercials for breakfast cereal brand X and four print advertisements for X
• The 150 subjects assigned to the Y condition were exposed to a similar campaign for brand Y.
After the advertising exposures, both groups completed a series of measures of their brand perceptions.
• A series of cereal brand pair similarity rating questions were asked from subjects. Each subject was asked to rate each of 14 pairs of breakfast cereal brand names for similarly by using an eleven point rating scale ranging from "totally different" to "identical".
• The 14 pairs were samples from the 55 possible pairings of 11 brand names.
• This method of obtaining similarities judgements, by dividing the brand pairs amongst subjects and using rating scales was very economical on questionnaire time (less than five minutes in this study) and permitted the similarity items to be incorporated in a questionnaire which contained many other items.
• One of the 11 cereal brands was a hypothetical brand labeled "your ideal breakfast cereal". This hypothetical brand was used to introduce the concept of preference into the context of similarity ratings. Brands judged to be similar to the "ideal" were presumably preferred to brands judged to be less similar to the "ideal". This method of establishing preference within similarities is called the explicit ideal point
• Scaling of averaged similarity data can be misleading unless the subjects who have similar product perceptions. Market segments were used in this study to increase the likelihood of subjects having similar market place perceptions. The segmenting variable reported here is usage of nutritional cereals.
• Average ratings were calculated for each brand pair within the user segment. Non-metric scaling program MDSCAL was used. The program reproduces the similarities ratings in a more concise and viewable form.
• Clusters of similarly perceived brands were located in the MDSCAL configurations by Johnson's cluster analysis. The clusters were named by reference to open ended questions on reasons for subjects liking their most liked brands and disliking their least liked brands.
• The scaling was compared by using the program CONGRU. This program rotates and stretches pairs of non-metric scaling configurations into maximal agreement, and provides test statistics for the similarities of the configurations.
• Figure 1 shows the CONGRU obtained positions of cereal brands in two dimensions for the segment "nutritional cereal users".
• There are obvious differences between perceptions in the X and Y conditions For brand X, the effect of the brand X advertisements (X condition versus Y condition) was to dissociate X from the other "crunchie" cereals and move it closer to the ideal. For brand Y, the effect of the brand Y advertisements (Y condition versus X condition) was to dissociate Y from the other "unappetizing" cereal but not to move it anywhere nearer the ideal.
Does result appearance changes in three or four dimensions? It does not.
• In three dimensions there are minimal differences between the X and Y conditions brand positioning. This is reflected in an rsk = .91 correlation between the positioning for the two conditions. A similar null result obtains for four dimension, with an rSk = .87.
• Clusters did not differ between the X and Y conditions. Also a subsidiary analysis showed little difference between the X and Y conditions. In this subsidiary analysis similarities data were pooled over treatments for the unadvertised brands, X, Y, and "ideal" were represented separately for each treatment group, and a single map produced.
• The sets of averages for the X and Y conditions were first compared using t tests. Clearly there is no evidence that the sets of means differ significantly between the two experimental conditions.
• The rank order correlation between the averaged similarity ratings for the two conditions was calculated as an additional check. A value of .87 was obtained, indicating considerable and significant (p<.001) agreement between the ratings for the two experimental conditions.
Possibility to predict the spurious two dimensional result in advance:
• The mean stress values for the two, three and four dimensional configurations were .153, .077 and .038 respectively.
• The corresponding stress values for 95% points were .182, .093, and .049. Thus all the dimensionalities were reasonable in relation to Klahr's values.
• Three or four dimensions might have been selected as appropriate dimensionalities owing to the reduction in stress achieved in going from two to three, and three to four dimensions
• On the other hand low dimensionalities are often used in scaling analyses because of the laudable objectives of parsimony and ease of display.
• Both three and four dimensionalities have lower than desirable degrees of freedom ratios. That is the ratio of the number of independent items of input information are low compared to the number of independent items of output information. Hence a dimensionality of two might have been selected.
CONCLUSIONS:
Perceptual mapping of attitude change is not a valueless technique. The null result obtained in the present study agreed with the null results obtained with more traditional measures and thus indicated the potential value of scaling as a cross validating measure. Such validation is facilitated when similarities data are collected in 4 reduced form as in the illustration above. But a need for caution is evident. A six stage check on data used for comparison of scaling is suggested.
• First the input data should be compared. If there are no significant differences between the data sets to be compared by scaling, then any subsequent differences shown by scaling should be regarded with suspicion.
• Second, the scaling analyses should be replicated in several dimensionalities,. Interesting effects that are not consistently reproduced in varied dimensionalities should be regarded with suspicion.
• Third, alternative analysis methods should be compared.
• Fourth, the results obtained from scaling should be compared with the results from other measures. Results that show only in the scaling should be regarded with suspicion.
• Fifth, if the data base is of sufficient size, split-half analysis should be used to test for the stability of effects found within the current data base.
• Sixth, additional data bases should be used to test for the stability of the effects found across data collection occasions and relevant contexts.

Submitted by: Jagdeep Kaur (12138)
Source: Roger M. Heeler (1974), "MEASURING ADVERTISING EFFECT BY PERCEPTUAL MAPPING: A CAUTIONARY TALE", in Advances in Consumer Research Volume 01, eds. Scott Ward and Peter Wright, : Association for Consumer Research, Pages: 192-200.