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Social Dynamics of Consumer Response: A Unified Framework Integrating Statistical Physics and Marketing Dynamics

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  • Javier Marin

Abstract

Comprehending how consumers react to advertising inputs is essential for marketers aiming to optimize advertising strategies and improve campaign effectiveness. This study examines the complex nature of consumer behaviour by applying theoretical frameworks derived from physics and social psychology. We present an innovative equation that captures the relation between spending on advertising and consumer response, using concepts such as symmetries, scaling laws, and phase transitions. By validating our equation against well-known models such as the Michaelis-Menten and Hill equations, we prove its effectiveness in accurately representing the complexity of consumer response dynamics. The analysis emphasizes the importance of key model parameters, such as marketing effectiveness, response sensitivity, and behavioural sensitivity, in influencing consumer behaviour. The work explores the practical implications for advertisers and marketers, as well as discussing the limitations and future research directions. In summary, this study provides a thorough framework for comprehending and forecasting consumer reactions to advertising, which has implications for optimizing advertising strategies and allocating resources.

Suggested Citation

  • Javier Marin, 2024. "Social Dynamics of Consumer Response: A Unified Framework Integrating Statistical Physics and Marketing Dynamics," Papers 2404.02175, arXiv.org.
  • Handle: RePEc:arx:papers:2404.02175
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    References listed on IDEAS

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    1. John D. C. Little & Leonard M. Lodish, 1969. "A Media Planning Calculus," Operations Research, INFORMS, vol. 17(1), pages 1-35, February.
    2. Gary Charness & Yan Chen, 2020. "Social Identity, Group Behavior, and Teams," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 691-713, August.
    3. Fred M. Feinberg, 2001. "On Continuous-Time Optimal Advertising Under S-Shaped Response," Management Science, INFORMS, vol. 47(11), pages 1476-1487, November.
    4. Nelson, Phillip J, 1975. "The Economic Consequences of Advertising," The Journal of Business, University of Chicago Press, vol. 48(2), pages 213-241, April.
    5. John R. Hauser & Kenneth J. Wisniewski, 1982. "Dynamic Analysis of Consumer Response to Marketing Strategies," Management Science, INFORMS, vol. 28(5), pages 455-486, May.
    6. John D. C. Little, 1979. "Feature Article—Aggregate Advertising Models: The State of the Art," Operations Research, INFORMS, vol. 27(4), pages 629-667, August.
    7. 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.
    8. M. L. Vidale & H. B. Wolfe, 1957. "An Operations-Research Study of Sales Response to Advertising," Operations Research, INFORMS, vol. 5(3), pages 370-381, June.
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