Market Structure Analysis at Columbia Drugstores, Inc. (Case Study)


A.               Describe the overall explanatory power of this regression model, as well as the relative importance of each continuous variable.

B.               Based on the importance of the binary or dummy variable that indicates superstore competition, do superstores pose a serious threat to Columbia’s profitability?

C.               What factors might Columbia consider in developing an effective competitive strategy to combat the superstore influence?


CASE STUDY SOLUTION


A.               The coefficient of determination R2 = 77.7% means that 77.7% of the total variation in Columbia’s profit‑margins can be explained by the regression model.  This is a relatively high level of statistically significant explanation (F = 13.38) for a cross-section study such as this, suggesting that the model provides useful insight concerning the determinants of profitability.  The standard error of the estimate (S.E.E. = 2.1931%) means that there is roughly a 95% chance that the actual profit margins for a given store will lie within the range of the estimated or fitted value ± 2 × S.E.E., or ± 2 × 2.1931%.
The intercept coefficient of 6.155 has no economic meaning because it lies far outside the relevant range of observed data.  The 0.189 coefficient for the market‑share variable means that, on average, a 1% (unit) rise in Columbia’s market share leads to a 0.189% (unit) rise in Columbia’s profit margin.  Similarly, as expected, Columbia’s profit margin is positively related to capital intensity, advertising intensity, and the rate of growth in the market area.  Conversely, high concentration has the expected limiting influence.  Because of the effects of leading‑firm rivalry, a 1% rise in industry concentration will lead to a 0.156% decrease in Columbia’s profit margin.  This means that relatively large firms compete effectively with Columbia.


B.               Yes, the regression model indicates that superstore competition in one of Columbia’s market areas reduces Columbia’s profit margin on average by 2.102%.  Given that Columbia’s rate of return on sales routinely falls in the 10% to 15% range, the profit‑limiting effect of superstore competition is substantial.  Looking more closely at the data, it appears that Columbia faces superstore competition in only one of the seven lucrative markets in which the company earns a 20% to 25% rate of return on sales.  Both observations suggest that current and potential superstore competition constitutes a considerable threat to the company and one that must be addressed in an effective competitive strategy.
C.               Development of an effective competitive strategy to combat the influence of superstores involves the careful consideration of a wide range of factors related to Columbia’s business.  It might prove fruitful to begin this analysis by more carefully considering market characteristics for Store No. 6, the one Columbia outlet able to earn a substantial 20% profit margin despite superstore competition.  For example, this analysis might suggest that Columbia, like Store No. 6, should specialize in service (e.g., prescription drug delivery) or in a slightly different mix of merchandise.  On the other hand, perhaps Columbia should follow the example set by Wal‑Mart in its early development and focus its plans for expansion on small to medium‑size markets.  In the meantime, Columbia’s still‑profitable stores in major metropolitan areas could help fund future growth.
Although obviously only a first step, a regression‑based study of market structure such as that described here can provide a very useful beginning to the development of an effective competitive strategy.