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Media Multiplexing Behavior: Implications for Targeting and Media Planning

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  • Chen Lin

    (Eli Broad College of Business, Michigan State University, East Lansing, Michigan 48824)

  • Sriram Venkataraman

    (Kenan-Flagler Business School, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599)

  • Sandy D. Jap

    (Goizueta Business School, Emory University, Atlanta, Georgia 30322)

Abstract

There is a growing trend among consumers to serially consume small, incomplete “chunks” of multiple media types---television, radio, Internet, and print---within a short time period. We refer to this behavior as media multiplexing and note that key challenges for integrated marketing communications media planners are (1) predicting which media or combination of media their target audience is likely to consume at any given time and (2) understanding potential substitutions and complementarities in their joint consumption. We propose a forecasting model that incorporates media-multiplexing behavior of both traditional and new media, their interdependencies, and consumer heterogeneity, and we calibrate the model using a rich database of individual-specific media activity diaries. The results suggest that accounting for media synergies within a single utility specification significantly improves model forecasts. We also introduce a utility function that directly models cross-channel media complementarities via interactive effects of the satiation parameters of own and joint consumption of various media types. Finally, our individual-level analyses generate unique insights on consumer-level media switching, multiplexing, and individual heterogeneity often ignored in aggregate data.

Suggested Citation

  • Chen Lin & Sriram Venkataraman & Sandy D. Jap, 2013. "Media Multiplexing Behavior: Implications for Targeting and Media Planning," Marketing Science, INFORMS, vol. 32(2), pages 310-324, March.
  • Handle: RePEc:inm:ormksc:v:32:y:2013:i:2:p:310-324
    DOI: 10.1287/mksc.1120.0759
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    1. Jeongwen Chiang, 1991. "A Simultaneous Approach to the Whether, What and How Much to Buy Questions," Marketing Science, INFORMS, vol. 10(4), pages 297-315.
    2. Hanemann, W Michael, 1984. "Discrete-Continuous Models of Consumer Demand," Econometrica, Econometric Society, vol. 52(3), pages 541-561, May.
    3. Andres Perea & Hans Peters & Tim Schulteis & Dries Vermeulen, 2006. "Stochastic dominance equilibria in two-person noncooperative games," International Journal of Game Theory, Springer;Game Theory Society, vol. 34(4), pages 457-473, November.
    4. Bhat, Chandra R., 2005. "A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 39(8), pages 679-707, September.
    5. Nitin Mehta & Xinlei (Jack) Chen & Om Narasimhan, 2010. "Examining Demand Elasticities in Hanemann's Framework: A Theoretical and Empirical Analysis," Marketing Science, INFORMS, vol. 29(3), pages 422-437, 05-06.
    6. Wendel, S. & Dellaert, B.G.C., 2005. "Situation variation in consumers' media channel consideration," Research Memorandum 006, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    7. Nitin Mehta, 2007. "Investigating Consumers' Purchase Incidence and Brand Choice Decisions Across Multiple Product Categories: A Theoretical and Empirical Analysis," Marketing Science, INFORMS, vol. 26(2), pages 196-217, 03-04.
    8. Karsten Hansen & Vishal Singh & Pradeep Chintagunta, 2006. "Understanding Store-Brand Purchase Behavior Across Categories," Marketing Science, INFORMS, vol. 25(1), pages 75-90, 01-02.
    9. Kenneth C. Wilbur, 2008. "A Two-Sided, Empirical Model of Television Advertising and Viewing Markets," Marketing Science, INFORMS, vol. 27(3), pages 356-378, 05-06.
    10. Roland T. Rust & Mark I. Alpert, 1984. "An Audience Flow Model of Television Viewing Choice," Marketing Science, INFORMS, vol. 3(2), pages 113-124.
    11. Danaher, Peter J. & Rust, Roland T., 1996. "Determining the optimal return on investment for an advertising campaign," European Journal of Operational Research, Elsevier, vol. 95(3), pages 511-521, December.
    12. David A. Schweidel & Eric T. Bradlow & Peter S. Fader, 2011. "Portfolio Dynamics for Customers of a Multiservice Provider," Management Science, INFORMS, vol. 57(3), pages 471-486, March.
    13. Pilotta, Joseph J. & Schultz, Don, 2005. "Simultaneous Media Experience and Synesthesia," Journal of Advertising Research, Cambridge University Press, vol. 45(1), pages 19-26, March.
    14. Prasad A. Naik & Kalyan Raman & Russell S. Winer, 2005. "Planning Marketing-Mix Strategies in the Presence of Interaction Effects," Marketing Science, INFORMS, vol. 24(1), pages 25-34, June.
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