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Marketing percolation

Author

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  • Goldenberg, J
  • Libai, B
  • Solomon, S
  • Jan, N
  • Stauffer, D

Abstract

A percolation model is presented, with computer simulations for illustrations, to show how the sales of a new product may penetrate the consumer market. We review the traditional approach in the marketing literature, which is based on differential or difference equations similar to the logistic equation (Bass, Manage. Sci. 15 (1969) 215). This mean-field approach is contrasted with the discrete percolation on a lattice, with simulations of “social percolation” (Solomon et al., Physica A 277 (2000) 239) in two to five dimensions giving power laws instead of exponential growth, and strong fluctuations right at the percolation threshold.

Suggested Citation

  • Goldenberg, J & Libai, B & Solomon, S & Jan, N & Stauffer, D, 2000. "Marketing percolation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 284(1), pages 335-347.
  • Handle: RePEc:eee:phsmap:v:284:y:2000:i:1:p:335-347
    DOI: 10.1016/S0378-4371(00)00260-0
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    References listed on IDEAS

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    1. Parker, Philip M., 1994. "Aggregate diffusion forecasting models in marketing: A critical review," International Journal of Forecasting, Elsevier, vol. 10(2), pages 353-380, September.
    2. Frank M. Bass & Trichy V. Krishnan & Dipak C. Jain, 1994. "Why the Bass Model Fits without Decision Variables," Marketing Science, INFORMS, vol. 13(3), pages 203-223.
    3. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    4. Kelvin J. Lancaster, 1966. "A New Approach to Consumer Theory," Journal of Political Economy, University of Chicago Press, vol. 74(2), pages 132-132.
    5. Rabik Ar Chatterjee & Jehoshua Eliashberg, 1990. "The Innovation Diffusion Process in a Heterogeneous Population: A Micromodeling Approach," Management Science, INFORMS, vol. 36(9), pages 1057-1079, September.
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