Lee Cooper, Robert Marks, David Midgley
Paper #: 95-06-052
We are interested in the strategic implications of asymmetric competition. Previous work (Carpenter, Cooper, Hanssens & Midgley [CCHM] 1988) has estimated the Nash-equilibrium prices and advertising expenditures for asymmetric market-share models in the extreme cases of “no competitive reaction” and “optimal competitive reaction.” There are, however, three important limitations to building marketing plans on either of these competitive scenarios. First, such “static,” single-period strategies do not provide insight into the actions undertaken over time by major manufacturers and retailers. Strategies such as ad pulsing “versus” more continuous exposures, or every-day-low-pricing “versus” deep discounting are played out over time. As was called for in the CCHM study, it is time to investigate dynamic, multiperiod strategies. Second, major sources of asymmetries are missing from the CCHM equilibrium analysis. There are two main sources of asymmetries. Asymmetries can arise from stable, cross-competitive effects. The price-tiers hypothesis (Blattberg and Wisniewski 1989), for example, indicates that when national brands go on sale they exert a stable, competitive pressure on regional brands that the regional brands can not counter with their own price reductions. When regional brands go on sale they exert pressure on the economy and private-label brands that these brands can not return. These are asymmetric cross-competitive effects. But asymmetries also arise from temporary differences in marketing offerings. One brand on sale by itself might gain much more than if it was promoted along with four other brands in the category. The “distinctiveness” of a brand's offering can produce asymmetric competition in a particular choice context (Nakanishi et al. 1974, Cooper and Nakanishi 1983 and 1988). While the CCHM study incorporated measures of distinctiveness into their development of methods for reflecting asymmetric competition, the equilibrium analysis used a simpler model that did not account for this source of asymmetries. Third, the CCHM effort studied market share, while the great swings in sales levels we observe in retail scanner data encourage us to study the strategic implications of asymmetric “sales” response. We want to investigate multi-period strategies, when the market response is fundamentally asymmetric in both sales volume and market share. There are major barriers to traditional avenues of investigation. Mathematical exploration is hampered because sources of asymmetry explicitly violate the global-convexity requirements of normative economic models. One major alternative to mathematical exploration is multi-period simulations, such as Axelrod's first tournament (Axelrod 1984) or the Fader/Hauser tournament (Fader & Hauser 1988). While these have the advantage of allowing strategies to be played out over time, so far they have only been undertaken with symmetric and hypothetical market-response functions. We want to use asymmetric market-response functions that characterize brand behavior in “real” markets to study the evolution of robust strategies. Genetic algorithms (Holland 1975) provide a mechanism by which we can pursue the study of the evolution of robust strategies. The next section describes “genetic algorithms” and how adaptable they are to the study of marketing strategy. This is followed by a discussion of a regional U.S. coffee market previously analyzed from a perspective of asymmetric competition (Cooper 1988, Cooper and Nakanishi 1988). Data from an asymmetric model of this market are then used along with a genetic algorithm to breed simple artificial agents for this market. “We will demonstrate that in the limited tests we can feasibly conduct these agents outperform the historical actions of brand managers in this regional market.” Finally, we will discuss the reasons why this might be so and what can be done to extend our approach. While we will primarily focus on one set of modeling techniques, and one particular market, it is important to stress that the methods we propose have greater applicability. Indeed they can be used in any marketing situation where there is a good representation of the profit consequences of competitive marketing actions. This representation might be in the form of an explicit linear or non-linear model or it might be more of a “black-box” representation (e.g., numerical approximation or neural net). Given such a profit function, artificial agents can be formulated and genetically optimized to play multi-period dynamic games in a robust and profitable manner. Our emphasis on asymmetric market modeling in the CCHM tradition, and on a regional coffee market, simply provides one case illustration of the overall approach.