Welcome to Powerpoint slides for Chapter 15 Multidimensional Scaling for Brand Positioning

1. Welcome to Powerpoint slides for Chapter 15 Multidimensional Scaling for Brand Positioning Marketing Research Text and Cases by Rajendra Nargundkar

2. Slide 1 1. The most common and useful marketing application of multidimensional scaling is in brand positioning. 2. Positioning is essentially concerned with mapping a consumer’s mind and placing all the competing brands of a product category in appropriate slots or “positions” on it. 3. For example, a product category of shampoos could be identified as having 5 attributes important to the consumer - price, lather, fragrance, consistency and favorable effects on hair. 4. If these were to be rated on a 7 point scale for say, six leading brands of shampoo A, B, C, D, E and F, then we could pickup any two attributes and plot the six brands on a map according to the consumer ratings. 5. This is called a perceptual map of consumer perception about competing brands in a product category. This is the type of map useful for deliberate positioning of a new brand, based on "gaps" in the current map, or for finding out the current position of an existing brand on the map. If the desired position of an existing brand owned by our company is different from the one perceived by consumers, an option is to "reposition" the brand.

3. Slide 2 1. The above method may not capture the consumer’s mind accurately. 2. If we assume that the consumer simultaneously thinks of several product dimensions or attributes rather than one attribute at a time, the above method is only an approximation of that process 3. Multidimensional scaling, on the other hand, captures the complex interactions between attributes and brands in a particular way, and then “derives” attributes or dimensions which explain the “positions” given by consumers to various brands. 4. There are two basic methods used in multidimensional scaling-Attribute based approach, and Similarity/Dissimilarity based approach 5. The attribute-based approach is similar to what we have described in the previous section, except that these input data are then further analysed using either factor analysis or discriminant analysis. 6. The second approach is very easy to understand intuitively, and quite useful in gaining a good understanding of consumer psyche, so we will discuss only this (similarity and dissimilarity based) approach.

4. Slide 3 • 1. In the similarity/dissimilarity-based approach, we need some kind of a distance measure between the brands being rated. The distance measure being input could be a simple ranking of distances between a brand and all other brands by a customer. • 2. One way to do this is to provide a customer (respondent) with cards, each containing a pair of brands written on it, and asking him to write down a number indicating the difference between the two brands on any numerical scale which can represent distance. • 3. This is then repeated for all pairs of brands being included in the research. No attributes are specified by which the customer is asked to decide on the difference. • 4. This distance measure or dissimilarity measure can be compiled into a matrix of the type shown in Fig.1.

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6. Slide 5 • 1. In Figs. 2(a), 2(b), 3(a), 3(b), 4(a) and 4(b), we have the SPSS outputs of multidimensional scaling on our data. • 2. Figs. 2(a) and 2(b) contain the 3-dimensional solution. Figs 3(a) and 3(b) contain the 2- dimensional solution. Figs. 4(a) and 4(b) contain the 1-dimensional solution. • 3. Our first task is to determine how many dimensions the data seems to indicate (in which we feel the best solution exists). For this, we look at the stress value for various solutions in different dimensions. From Figs. 2(a), 3(a) and 4(a), we see the following values of stress. • · 3-dimensional solution : 0.05230 • · 2-dimensional solution : 0.24015 • · 1-dimensional solution : 0.43159 • 4. Clearly, the 1- dimensional solution is not a good one. Remember, the stress value indicates lack of fit, so it should be as close to zero as possible. The 2- dimensional solution is better, but the 3-dimensional solution looks the best, as the stress value is a low 0.05.

7. Slide 6 1. Let us assume we have decided that the 3-dimensional solution is the best, based on the low stress value. 2. Then, our next task now would be to name the dimensions. For doing so, our previous knowledge of the brands may become important. For example, let us assume that the eight brands of TV were as follows :- 1. Aiwa 2. Videocon 3. LG 4. Samsung 5. Sony 6. Onida 7. Thomson 8. BPL

8. Slide 7 Stimulu Stimulus 1 2 3 1 VAR00001 1.9512 .2028 .0664 2 VAR00002 -.1995 1.3140 .7743 3 VAR00003 -.6043 -1.3429 .4680 4 VAR00004 -.9038 -.2969 -1.8497 5 VAR00005 .8931 -1.0092 -.0350 6 VAR00006 1.1045 .1529 -.7070 7 VAR00007 -1.1031 1.6088 -.1289 8 VAR00008 -1.1381 -.6295 1.4121

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13. Slide 9 • Additional Comments • 1. MDS can be performed even with a sample size of 1. • 2. It can be used to get a composite picture of a segment's perception, by combining the responses of any one segment, and repeating the MDS for each of the major segments. • 3. It can also be done across all segments (a single MDS) by aggregating responses for the entire sample. • 4. If we have a significant marketing decision hinging on the results, the author recommends that approaches 2 and 3 (segment wise and across segments) both be used and if there are significant differences, try and see if the positioning needs to be different for different segments. That may indeed be the case, in these days of diversity of consumer preferences. • 5. It would be tempting to do one MDS for each respondent, but the analysis would remain meaningless unless there are sufficient numbers of each consumer type which means determining the segments after the MDS. This is a possibility, but would involve a lot of work in the analysis stage. • 6. It is best left to the judgment of the researcher which approach he would like to follow.