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Planning Marketing-Mix Strategies in the Presence of Interaction Effects


Author Info

  • Prasad A. Naik

    (University of California Davis, 1 Shields Avenue, Davis, California 95616)

  • Kalyan Raman

    (Loughborough Business School, Loughborough University, Ashby Road, Loughborough, Leicestershire, United Kingdom LE11 3TU)

  • Russell S. Winer

    (Stern School of Business, New York University, 44 West 4th Street, New York, New York 10012)

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    Companies spend millions of dollars on advertising to boost a brand's image and simultaneously spend millions of dollars on promotion that many believe calls attention to price and erodes brand equity. We believe this paradoxical situation exists 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. By recognizing interaction effects between activities, managers can consider interactivity trade-offs 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 backward 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 backward 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 unsolved marketing problem, which motivates this paper. This paper investigates the problem of planning marketing mix in dynamic competitive markets. We extend the Lanchester model by incorporating interaction effects, constructing the marketing-mix algorithm that yields marketing-mix plans with strategic foresight, and developing the continuous-discrete estimation method to calibrate dynamic models of oligopoly using market data. Both the marketing-mix algorithm and the estimation method are general, so they can be applied to any other alternative model specifications for dynamic oligopoly markets. Thus, this dual methodology augments the decision-making toolkit of managers, empowering them to tackle realistic marketing problems in dynamic oligopoly markets. We illustrate the application of this dual methodology by studying the dynamic Lanchester competition across five brands in the detergents market, 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. Normatively, we find that large brands underadvertise and overspend on promotion, while small brands underadvertise and underpromote. Finally, comparative statics reveal managerial insights into how a specific brand should respond optimally to the changes in a competing brand's situation; more generally, we find evidence that competitive responsiveness is asymmetric.

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    Bibliographic Info

    Article provided by INFORMS in its journal Marketing Science.

    Volume (Year): 24 (2005)
    Issue (Month): 1 (June)
    Pages: 25-34

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    Handle: RePEc:inm:ormksc:v:24:y:2005:i:1:p:25-34

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    Keywords: continuous-discrete estimation; dynamic competition; interaction effects; marketing-mix planning; strategic foresight; two-point boundary value problem;


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    Cited by:
    1. Sigitas Urbonavičius & Vytautas Dikčius, 2009. "Perceived role of marketing activities in the context of transitional economy," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 21(2), pages 167-182.
    2. Annamaria Sorato & Bruno Viscolani, 2008. "Modelling smoothly the joint effect of several advertising media on sales in a homogeneous market," Working Papers 176, Department of Applied Mathematics, Università Ca' Foscari Venezia.
    3. Huang, Jian & Leng, Mingming & Liang, Liping, 2012. "Recent developments in dynamic advertising research," European Journal of Operational Research, Elsevier, vol. 220(3), pages 591-609.
    4. Singfat Chu & Hean Keh, 2006. "Brand value creation: Analysis of the Interbrand-Business Week brand value rankings," Marketing Letters, Springer, vol. 17(4), pages 323-331, December.
    5. Ramanathan, Usha & Muyldermans, Luc, 2010. "Identifying demand factors for promotional planning and forecasting: A case of a soft drink company in the UK," International Journal of Production Economics, Elsevier, vol. 128(2), pages 538-545, December.
    6. Nobuhiko Terui & Masataka Ban, 2008. "Modeling heterogeneous effective advertising stock using single-source data," Quantitative Marketing and Economics, Springer, vol. 6(4), pages 415-438, December.
    7. Beltran-Royo, C. & Zhang, H. & Blanco, L.A. & Almagro, J., 2013. "Multistage multiproduct advertising budgeting," European Journal of Operational Research, Elsevier, vol. 225(1), pages 179-188.
    8. Blanco Callejo, M, 2007. "LA VENTA DE LA ILUSIÓN Y “LA BRUJA DE ORO”: EL EMBRUJO DE UN CÍRCULO VIRTUOSOi /," Investigaciones Europeas de Dirección y Economía de la Empresa (IEDEE), Academia Europea de Dirección y Economía de la Empresa (AEDEM), vol. 13(3), pages 33-56.


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