The industry segmentation matrix for the firm

Having identified the relevant segmentation variables with struc­ tural  or value chain implications, the next  task   is to   combine   them into an overall segmentation of the industry. The task is usually difficult because there are many relevant segmentation variables— in some in­ dustries there can be dozens. The  challenge is to distill these variables into the most meaningful segments for developing competitive strategy. The first step in the distillation process is to apply a significance test to each segmentation variable. Only those variables with a truly significant impact on the sources of competitive advantage or industry structure should be isolated for strategic analysis. Other less important, though still meaningful,  segmentation  variables that are identified can be used for fine tuning in marketing or operations management.

The basic tool for translating the remaining variables into a seg­ mentation is the industry segmentation matrix. A simple segmentation matrix based on two segmentation variables is shown in Figure 7-3, illustrating the oil field equipment  industry  in which the size of the buyer oil company  and the stage of development of the country  in which the buyer is headquartered have been identified as the two segmentation variables.

The first practical problem in constructing  a segmentation  matrix is choosing   the   number  of categories   of each   segmentation  variable to select. In   Figure  7-3   I have chosen   three  discrete categories of buyer   size   and   two   categories   of a   country’s  stage   of development. In reality, buyer size is a continuous variable and country development goes through many stages. The way in which each segmentation vari­ able is broken  into   discrete   categories   should  reflect   the   categories that capture the most significant structural or value chain differences, balanced   against the   practical  need to   limit the   number  of segments to a   manageable  number.  Deciding  on   the   best   discrete   categories for strategic purposes almost always requires judgment and is an itera­ tive process.

The cells in Figure 7-3 are the individual  segments in the industry. It may well be that some of the cells are presently unoccupied. In addition, if there were no small independent oil companies based in developing countries and not likely ever to be any, this segment would be a null cell. For  purposes  of illustration,  Figure  7-3 shows null cells involving both  large and small independents.  Segments can often be eliminated   from   consideration  if they   are   null   cells.   However,   it is important to remember that null cells should be infeasible combina­ tions of the segmentation  variables and  not  merely cells in which no firm is currently operating. Feasible cells where no firm is operating represent a potential opportunity and it is im portant that such segments be highlighted, not eliminated, in segmentation.

Figure   7-3.     A Simple Industry Segmentation Matrix for an Oil Field Equipment Industry

Figure 7-3 portrays a case where there are two relevant segmenta­ tion variables. In practice, there may be many variables grouped under the four broad categories of product, buyer type, channel, and geogra­ phy. Looked at closely, most industries  are quite heterogeneous. With many significant segmentation variables, the number of segmentation matrices that  could   be plotted   multiplies  rapidly.   The  problem, then, is to convert the segmentation variables into a small number of segmen­ tation matrices that will be most illuminating for the strategy formula­ tion process.

1. Relationships Among Segmentation  Variables

To move from a number of segmentation variables to the most meaningful segmentation matrices, the first step is to probe the relation­ ships among the segmentation variables. The number of important segmentation variables can be reduced by collapsing segmentation vari­ ables together that  are correlated,  or  which effectively measure the same thing.   For  example,   geographic  location   may be associated with a particular  buyer  type (e.g., automobile  companies  are   located in the Midwest), or buyer type may be closely related to channel (small roofing contractors  are all   served   through  distributors).  Constructing a segmentation matrix with correlated segmentation variables will pro­ duce a matrix in which many cells are null.

Segmentation variables that are highly correlated  can be com­ bined, because one variable is a surrogate  for the   effect of the   other. In   less extreme cases,   the correlation  among  segmentation  variables is partial, but allows a significant reduction in the number of possible segments because many  cells in   the   matrix  are   null.   It   is important to identify all the relationships among the segmentation variables and use this to combine variables together and identify null cells.

It is also im portant to understand  why variables are related, be­ cause this will often   have im portant  ramifications.   If one variable is not a good surrogate  for another  but  rather  a reflection of current firm behavior  or happenstance,  combining  variables is a mistake. It will obscure unoccupied segments that may represent an unexploited opportunity. For example, if small roofing contractors  were served through distributors not for economic  reasons but  for historical rea­ sons, then eliminating direct sale to small contractors  as a segment would be a mistake. Telemarketing or remote order entry by salesper­ sons with portable computer terminals might make the segment feasible though it had not been previously.

2. Combining   Segmentation  Matrices

The significant and independent segmentation  variables that  re­ main after the process   described   above   represent  the   potential  axes for industry segmentation matrices. W here there are more than two segmentation variables, the industry segmentation matrix will no longer fit on a two-dimensional page. One  way of proceeding  is to construct a number of different segmentation matrices for each pair of variables. Each of these matrices can then be analyzed  for its strategic implica­ tions. This approach is not fully satisfactory, however, because mean­ ingful segments may be the result of combining more  than  two segmentation variables and may be overlooked.

To deal with more than two segmentation  variables, it is usually useful to create combined segmentation matrices. The process is illus­ trated in Figure  7 -4 . In oil field equipment  there are at least two other relevant buyer segmentation variables besides buyer type and geographic buyer location: the technological sophistication of the oil company and its ownership. In Figure 7 -4 , I have plotted the four variables in pairs and then combined  the two segmentations  together after eliminating null cells.

The process of combining  matrices  not  only reduces the number of segments by eliminating some null cells, but also exposes correlations among variables that  may have been missed. In Figure  7 -4 , I have noted the null cells representing infeasible combinations. Combining matrices is usually best done by combining all segmentation  variables within a category first. In   Figure   7 -4 ,   for example, I have combined all the buyer segmentation variables together.

After combining segmentation  variables of the same broad  cate-gory, one proceeds to combine  variables in   different   categories.   In doing so, it is usually best to create a segmentation  matrix  in which one axis reflects the   combined  product  segmentation  variables   and the other axis combines all the buyer-related variables (buyer  type, channel, geography). Where the num ber of segmentation variables is manageable, it is possible using this procedure to construct one two- dimensional industry segmentation matrix. This matrix  may be quite large, but  has the advantage  of  displaying the   entire industry  in a way that  facilitates strategic analysis. Figure  7-5 shows such a matrix for oil field equipment, after adding to the segmentation two product segmentation variables— premium  versus   standard   quality   products, and products with ratings for deep versus shallow drilling.

Figure 7 – 4 .    Combined Segmentation Matrix for an Oil Field   Equipment Industry

Sometimes the number of relevant segmentation variables and resulting segments is so great as to   make  a single matrix  unwieldy. The presence of a very large overall segmentation  matrix  should prompt the reexamination of the segmentation  variables and  the dis­ crete categories of each to ensure that  the differences are truly signifi­ cant. Where this is the case, it may be desirable to use two or three segmentation matrices in subsequent  analysis to avoid missing impor­ tant strategic implications.

The industry segmentation matrix should contain potential seg­ ments and not just segments that are currently  occupied. Potential segments may imply entirely   new segmentation  variables (e.g., channel is added because   there   is the   possibility   that  some direct  sales may be possible in the future instead of handling all sales through distribu­ tors) or new discrete categories of existing variables (e.g., a new perfor­ mance rating for an alloy).

A segmentation matrix is an analytical tool, not an end in itself. The analyst should  start with the longest list of segmentation  variables to avoid overlooking possibilities. Only  over the course of the analysis are variables combined or eliminated and the working  segmentation matrix  refined. The  whole   process   usually   involves   trying   a number of different segmentation schemes in which the product and buyer differences that are most important for industry structure are gradually exposed.

A segmentation matrix should be tested by examining the strate­ gies of competitors.  If the   scope of competitors’ activities   is plotted on the matrix, new segments or segmentation variables may be exposed. Conversely, competitors’ activities   may draw  attention  to   segments that must inevitably be served together.  I will have more to say about this below when interrelationships among segments are discussed. Fig­ ure 7 -6 summarizes  the steps required in industry segmentation.

Figure 7 – 6 .    The Industry Segmentation Process

Source: Porter Michael E. (1998), Competitive Advantage: Creating and Sustaining Superior Performance, Free Press; Illustrated edition.

Leave a Reply

Your email address will not be published. Required fields are marked *