Planning Marketing Mix Strategies In The Presence Of Interaction Effects: Empirical And Equilibrium Analyses

Planning a mixed marketing strategy in the face of interactions: empirical and balanced analysis

Abstract
Companies spend millions of dollars on advertising to enhance their brand image, and at the same time they spend millions on advertising that draws attention to price and undermines brand heritage. This is because advertising and promotions are necessary to compete effectively in a dynamic market. Therefore, brand managers need to consider the interaction between marketing activities and competing brands when determining the right amount of budget and focus on marketing activities. By knowing the implications of interactions between activities, managers can weigh the advantages and disadvantages of interactions when planning a marketing mix strategy. On the other hand, by considering interactions with competitors, managers can incorporate strategic foresight into their planning, which requires a front and back approach to make optimal decisions. Looking ahead means that each brand manager anticipates how other competing brands will make decisions in the future and then, in retrospect, makes the best decisions in response to the best decisions of all the other brands. Considering the interaction implications and strategic foresight when planning a marketing mix strategy is a challenging and open-ended marketing issue that drives this article.
In this article, we develop a methodology for planning the optimal marketing mix in a dynamic competitive market, taking into account the implications of forecasting and strategic interactions. To calculate and infer the interaction effect, we designed the Kalman filter using what is available
Split-time market data to calibrate ongoing marketing patterns for dynamic competition.
To develop an optimal marketing mix plan, we build a computational algorithm to solve the non-linear two-point boundary associated with the equilibrium strategy.
We demonstrate the application of this dual methodology by analyzing the dynamic competition between five brands in Lanchester in the detergent market, using advertising and promotion of each brand to influence market share and actions of competing brands. Experimentally, we have seen that advertising and promotions not only affect brand engagement (personal and proprietary), but also affect the interaction effect, i.e. that each activity strengthens or weakens the effectiveness of the other. In addition, when managers ignore this interaction effect, they may think that advertising and promotions are more effective than they really are. Typically, big brands like Tide and Wisk spend less on advertising, and spend more on advertising. Thus, we accept that there may be “promotional escalation” in some markets, and Leeflang and Wittink (2001) attribute this to a lack of strategic foresight on the part of managers.
Finally, common estimation procedures and computational algorithms allow managers to apply the proposed methodology to other market response models that reflect their company's specific marketing environment.

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