We analyze the market segment of antibiotics prescribed for treatment of viral and bacterial infections occurring in the respiratory passages and lungs. The analysis will be used to identify new business opportunities for a product already competing in this market. The same map, however, may also be used to identify a sustainable positioning strategy for a new brand.
We use a European market where 40,000 doctors write about 45 million prescriptions to treat 300 different diseases, with 60 anti-infective agents marketed by 35 different companies.
A table of 300 rows and 60 columns, disease by product, was used to run a first round of Brand Mapping, so as to reduce the number of items to focus on. After 3 rounds we had reduced the original data to the table below, which refers to products and diagnoses considered worthy of analysis.
The figures are in thousands and come from a panel of doctors who report which agent they have used to treat which disease. Asthma, for example, was treated 372,000 times, of which 57,000 were treated with Augme. Although one may not know the product names, the diseases should sound familiar. The white column on the left shows that this segment accounts for 9,982,000 prescriptions of the total anti-infective market. The white row shows the total prescriptions by product: the market shares in prescriptions. The data in the yellow area is the input we used to run the analysis summarized in the tables and the maps below. The data in the gray zone was removed for the reasons explained below.
Looking at the table below (coefficients refer to both the yellow and gray zones above) we can see that the level of diversity across brands and diseases is low (sum of eigenvector values » 0,8 < 9). This means, that in order to succeed in such an environment, brands are required to find elements of differentiation beyond the simple technical performance. In this market it is not sufficient to say Brand X works against disease Y because most competitors of brand X are also efficacious against the same disease. Therefore, the lower the sum of eigenvector values, the lower the diversity across brands measured on prescribing habits, and the higher the need for the brand to be marketed with a sharp personality.
MarketingStat.com 65 Of the nine principal axes, the first three explain almost 90% of the total variability in the data. This means that 90% of the relationships
occurring in the raw data of our example can be visualized on a 3-dimensional map, a very good map (which can be made using the tool 4D Map in MM4XL). The first axis alone explains 50% of variance, and it probably shows a meaningful segmentation which could be assigned a label.
Assigning labels to regions of the map is useful for guiding the strategic reading and reasoning.
However, the 2D row-column association is low (.34), and one should refrain from a direct
interpretation. Pneumonia, chronic bronchitis, and chronic sinusitis, although very small entities, had a very strong impact in orientating the map. For this reason they have been removed and the new map, more easily readable yet still stable, can be seen below. The coefficients that follow refer to this second map.
Tip:
The x-axis (the horizontal one) is rescaled from min -600 and max 600 to min -400 and max 400. This makes the map more readable.
Brand Mapping produces a so-called dual display, which allows the simultaneous display of both row and column bubbles on the same low dimensional space. When there are many bubbles on the map and when the row-column association is low, it helps to look first at the two spaces separately, as shown below.
It is evident the left-hand side refer to diseases located in the throat, while the diseases on the right are located in the lungs. One label for the x-axis could be Lower (left) and Upper (right) Respiratory Ways. The vertical split isn't very sharp. It might be however related to the duration of the disease. On the upper side of the map there are the acute inflammatory diseases, which tend to come and go in a short window of time, and the more chronic or persistent diseases are on the lower side of the map.
The vertical axis appears better defined when looking at the brands only. The lower cluster of products is comprised mainly of cephalosporines, while penicillin is found in the upper-left cluster. The former are prescribed against more aggressive infections like those located in the lungs and the latter are mainly used to treat acute and less aggressive bacteria, like those located in the medium respiratory ways. Labels could
3. Brand Mapping
be Inflammatory Process on the upper part and Viral Infection on the lower one.
Tip:
Assigning labels and clustering bubbles is a good way to add prior knowledge to the map and to facilitate the interpretation.
Before looking at the map from a strategic point of view it is necessary to verify its accuracy, and this is accomplished by looking at the coefficients that Brand Mapping prints in the output report.
The first numbers to look at are the Squared Cosines. These coefficients measure the level of correlation between one point and each axis. A well-represented space has all points mainly associated with the first three axes (this is a dimensionality that human beings are used to dealing with). With some effort the fourth dimension may be interpreted, yet a higher dimensionality may result in a less understandable output.
As shown in the table below, the first four diseases plus Tonsillitis are well displayed on our map, Throat Infection is mainly associated with the third axis, and Infection of Respiratory Ways is associated with the fourth axis.
The brands show an analogous situation with Zinac and Panac poorly represented and the rest of products with a good association on the first three axes. We should be cautious about Zimox whose very high association with the first axis may be suspicious.
In general, this visual display seems to be accurate enough, and the high portion of variance explained by the first two axes also suggests it.
A point strongly associated with one axis is not always the major driver of the orientation of that axis. This may be due to a unique combination of proportions in the profile that leads the point to exert a strong or weak impact on the orientation that the space takes. In the table above, for example, Zimox contributes heavily to orient the first axis (645‰) and it contributes very little to the second axis. Contributions are also useful to identify outliers. An outlier is a minor point, with high mass, placed outside of the major space, which impacts heavily the orientation of the map. In some cases one can get rid of outliers, as we did at the beginning of this example with the diagnoses in the gray shaded area.
The mass values can be read as market shares. In our example, given the total sum of the raw data is close to 10000 and the mass values are expressed in thousands, there is a relationship of almost 1 to 10 between the two. Again, our analysis is quite stable, so there are no alarming values: large products account for large amounts of inertia. Should this not be the case the analyst has to find out the reasons why this happened and must take into account the corresponding level of inaccuracy.
In certain cases it makes sense to look at the 3D space in order to highlight data partitions not evident on a flat display. In the map we have used arrows to give an idea of the direction and depth of the position of some bubbles on the 3rd axis. One can, however, use the tool 4D Map in MM4XL for drawing a better chart. Klacid for instance, seems to go significantly above the plane in the same direction as the disease Asthma. The two are strongly associated, indeed Klacid is the most used drug for the treatment of asthma, and these prescriptions strongly characterize Klacid's profile. This is however not the case for the second largest prescribed drug against Asthma: Augme.
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