This hypothetical study involves 18 respondents doing in-depth interviews. It is written from the perspective of Allergan, i.e., I am assuming that Allergan is the client that I am advising. We ask these 18 respondents to evaluate Allergan as well as nine of Allergan’s competitors on eight firm attributes. We are asking them simply “does the firm possess this attribute or not.” In other words, we are creating a tally. Each time the respondent says yes, we make a tick to symbolize that the respondent agrees to the idea that the specific firm does in fact have the attribute in question. At the end, we count the number of ticks or yes responses; these counts form the data matrix or cross-tabulation table or contingency table results. The attributes are: Product Quality, Strategic Orientation, Overall Service Level, Delivery Speed, Price Level, Sales Force Image, Price Flexibility, and the firm’s Image. I picked the following companies to represent the pharmaceutical industry: Biodel, Merck, Bayer, Allergan, Novo Nordisk, Shire, Roche, Pfizer, Glaxo, and Novartis.
2. Discussion of the Contingency Table
Contingency Table
Biodel Merck Bayer Allergan Novo Shire
Quality 4 3 1 13 9 6
Strategi 15 16 15 11 11 14
Service 15 14 6 4 4 15
Speed 16 13 8 13 9 17
Price 14 14 10 11 11 14
Sforce 7 18 13 4 9 16
Flexibil 6 6 14 10 11 8
Image 15 18 9 2 3 15
Total 92 102 76 68 67 105
Roche Pfizer Glaxo Novartis Total
Quality 3 18 2 10 69
Strategi 16 12 14 14 138
Service 14 13 7 13 105
Speed 15 16 6 12 125
Price 12 13 10 14 123
Sforce 14 5 4 16 106
Flexibil 7 4 14 4 84
Image 16 7 8 8 101
Total 97 88 65 91 851
Now, this gigantic collection of counts in the contingency table is hard to interpret. It hides a bunch of important strategic findings. To make these findings obvious, I use correspondence analysis. The general idea is to draw an attribute-company combination close together if they are strongly associated with each other. Similarly, if an attribute-company combination is not strongly associated with each other, they are drawn apart. Moreover, the creation of dimensions or ‘components’ lets us know the key drivers in this industry when it comes to perceptions in the minds of the respondents.
A simple way of explaining the thought process behind correspondence analysis is as follows. Suppose that a new car brand is being presented to the public for the first time. This car brand offers a value proposition to the customers that can be described as fun, sporty, sexy, energetic, and cool. Suppose that two customer groups are being evaluated to see their degree of correspondence with this brand’s value proposition. The first group consists of 18 to 25 year old males. This group is looking for a fun, sporty, sexy, energetic, and cool looking car. Consequently, the value proposition that this car brand offers and the value proposition that the 18 to 25 year old males want are highly similar. This implies that the number of sales of this car brand to this customer group will be above average. The correspondence analysis will pick this up as actual sales to this group exceed expected sales (technically the expected cell value used to compute the chi-square statistic). The analysis will assign to this result a positive value and draw 18 to 25 year old males and this car brand close together on the perceptual map. Suppose further, that a second group consists of 55 to 65 year old males. This group is looking for a car that can be defined as refined, elegant, safe, dignified, and expensive. The value proposition offered by the car brand and the value proposition desired by this 55 to 65 year old group of males are dissimilar. Correspondence analysis will pick this up as actual sales to this group will be below the expected value. Intuitively, this makes sense. Since the car brand does not satisfy the needs of this customer group, then few sales will take place. The analysis will draw the 55 to 65 year old group and this car brand far apart on the perceptual map.
The graphical output or perceptual map is provided in this blog at the bottom of this section. The attributes are plotted with red squares; the companies are plotted with blue squares. To understand the competitive landscape, we look for the extreme attribute cases to try to figure out what the components (the axes in the graph) mean. Component 1, the y-axis, (i.e., why we can move up and down in this picture) is driven by Product Quality (at the top of the picture) and Firm Image (at the bottom). Component 2, the x-axis, (i.e., why we can move right and left in this picture) is driven by Price Flexibility (on the left) and Service (on the right). So now, we have the four key drivers that explain most of what is going on in this market space. We also know that component 1 (i.e., product quality and firm image) is more important than component 2. This is because component 1 explains 53.1% of the variation while component 2 explains only 33.2%. Moreover, when I look at the “explanation by component” information, Allergan is very heavy (88.2%) on component 1 and much lower on component 2 (9.3%). Further, when looking at the similarity values (i.e., signed chi-square values), the two largest values for Allergan are +10.165 on Quality and –4.566 for Image (the minus implies dissimilarity). All of this suggests that component 1, i.e., product quality and firm image are the most important things for the marketing manager at Allergan to address. The secondary issue to address is component 2, i.e., service and price flexibility.
From a strategic point of view, we see that Allergan is viewed as being very strong on product quality and very weak on firm image. With regard to component 2, Allergan does fine on price flexibility but not so well on service. This suggests an advertising strategy based on product quality mainly with a secondary focus on price flexibility, assuming the plan is to play to the existing strengths. We also observe that Allergan is perceived as being very similar to Pfizer since both firms are very strong on product quality. We might expect competition here because these two firms are very similar.
Moreover, suppose that you are no longer Allergan but rather a new entrant into this industry. One thing you might want to consider is where to position your new brand. One possible solution would be to analyze this perceptual map to see if any ‘gaps’ exist. Are there any areas where existing firms have not yet been positioned? For example, the top-left hand corner of the graph is currently unoccupied. No firm is currently offering the combination of strong product quality and strong price flexibility. Consequently, this could be one area to further study using a feasibility analysis (since one needs to verify the profitability of such a combination). If this combination were to be profitable, the new entrant would have achieved a successful differentiation strategy.
