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The modeling and analysis of the word-of-mouth marketing

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  • Li, Pengdeng
  • Yang, Xiaofan
  • Yang, Lu-Xing
  • Xiong, Qingyu
  • Wu, Yingbo
  • Tang, Yuan Yan

Abstract

As compared to the traditional advertising, word-of-mouth (WOM) communications have striking advantages such as significantly lower cost and much faster propagation, and this is especially the case with the popularity of online social networks. This paper focuses on the modeling and analysis of the WOM marketing. A dynamic model, known as the SIPNS model, capturing the WOM marketing processes with both positive and negative comments is established. On this basis, a measure of the overall profit of a WOM marketing campaign is proposed. The SIPNS model is shown to admit a unique equilibrium, and the equilibrium is determined. The impact of different factors on the equilibrium of the SIPNS model is illuminated through theoretical analysis. Extensive experimental results suggest that the equilibrium is much likely to be globally attracting. Finally, the influence of different factors on the expected overall profit of a WOM marketing campaign is ascertained both theoretically and experimentally. Thereby, some promotion strategies are recommended. To our knowledge, this is the first time the WOM marketing is treated in this way.

Suggested Citation

  • Li, Pengdeng & Yang, Xiaofan & Yang, Lu-Xing & Xiong, Qingyu & Wu, Yingbo & Tang, Yuan Yan, 2018. "The modeling and analysis of the word-of-mouth marketing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 1-16.
    • Handle: RePEc:eee:phsmap:v:493:y:2018:i:c:p:1-16
    DOI: 10.1016/j.physa.2017.10.050
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    References listed on IDEAS

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    1. Herr, Paul M & Kardes, Frank R & Kim, John, 1991. "Effects of Word-of-Mouth and Product-Attribute Information on Persuasion: An Accessibility-Diagnosticity Perspective," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 17(4), pages 454-462, March.
    2. Samadi, Mohammadreza & Nikolaev, Alexander & Nagi, Rakesh, 2016. "A subjective evidence model for influence maximization in social networks," Omega, Elsevier, vol. 59(PB), pages 263-278.
    3. Vijay Mahajan & Eitan Muller & Roger A. Kerin, 1984. "Introduction Strategy for New Products with Positive and Negative Word-of-Mouth," Management Science, INFORMS, vol. 30(12), pages 1389-1404, December.
    4. Yang, Lu-Xing & Yang, Xiaofan, 2014. "The spread of computer viruses over a reduced scale-free network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 173-184.
    5. Hinz, Oliver & Skiera, Bernd & Barrot, Christian & Becker, Jan, 2011. "Seeding Strategies for Viral Marketing: An Empirical Comparison," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 56543, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    6. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    7. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    8. Yang, Lu-Xing & Draief, Moez & Yang, Xiaofan, 2016. "The optimal dynamic immunization under a controlled heterogeneous node-based SIRS model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 403-415.
    9. Mauro Bampo & Michael T. Ewing & Dineli R. Mather & David Stewart & Mark Wallace, 2008. "The Effects of the Social Structure of Digital Networks on Viral Marketing Performance," Information Systems Research, INFORMS, vol. 19(3), pages 273-290, September.
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    3. Feng Xu & Wenxia Niu & Shuaishuai Li & Yuli Bai, 2020. "The Mechanism of Word-of-Mouth for Tourist Destinations in Crisis," SAGE Open, , vol. 10(2), pages 21582440209, May.
    4. Li, Pengdeng & Yang, Xiaofan & Wu, Yingbo & He, Weiyi & Zhao, Pengpeng, 2018. "Discount pricing in word-of-mouth marketing: An optimal control approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 512-522.
    5. Qiang Yan & Simin Zhou & Xiaoyan Zhang & Ye Li, 2019. "A System Dynamics Model of Online Stores’ Sales: Positive and Negative E-WOM and Promotion Perspective," Sustainability, MDPI, vol. 11(21), pages 1-13, October.

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