Companies spend millions of dollars on advertising to boost a brand’s image, while simultaneously spending millions of dollars on promotion that calls attention to price and erodes brand equity. This situation arises because both advertising and promotion are necessary to compete effectively in dynamic markets. Consequently, brand managers need to account for interactions between marketing activities and interactions among competing brands in determining the appropriate level of budget and its allocation to marketing activities. By recognizing interaction effects between activities, managers can consider inter-activity tradeoffs in planning the marketing mix strategies. On the other hand, by recognizing interactions with competitors, managers can incorporate strategic foresight in their planning, which requires them to look forward and reason backwards in making optimal decisions. Looking forward means that each brand manager anticipates how other competing brands are likely to make future decisions and then, by reasoning backwards, deduces one’s own optimal decisions in response to the best decisions to be made by all other brands. The joint consideration of interaction effects and strategic foresight in planning marketing-mix strategies is a challenging and open marketing problem, which motivates this paper.
In this paper, we develop a methodology for planning optimal marketing-mix in dynamic competitive markets, taking into account strategic foresight and interaction effects. To estimate and infer the existence of interaction effects, we design a Kalman filter that uses readily available
discrete-time market data to calibrate continuous-time marketing models of dynamic competition.
To develop optimal marketing-mix plans, we construct a computational algorithm for solving the nonlinear two-point boundary value problem associated with the derivation of equilibrium strategies.
We illustrate the application of this dual methodology by studying the dynamic Lanchester competition across five brands in the detergents markets, where each brand uses advertising and promotion to influence its own market share and the shares of competing brands. Empirically, we find that advertising and promotion not only affect the brand shares (own and competitors’), but also exert interaction effects, i.e., each activity amplifies or attenuates the effectiveness of the other activity. Moreover, if managers ignore these interaction effects, they are likely to believe that advertising and promotion are less effective than they actually are. Normatively, we find that large brands such as Tide and Wisk not only under-advertise, but also substantially over-spend on promotion. Thus, we provide support for the view that “escalation of promotion” may exist in some markets, a finding that Leeflang and Wittink (2001) attribute to managers’ lack of strategic foresight.
Finally, the generality of both the estimation method and computational algorithm enables managers to apply the proposed methodology to other market response models that reflect the marketing environment specific to their companies.