Pricing and Timing Strategies for New Product Using Agent-Based Simulation of Behavioural Consumers

Article OPEN
Keeheon Lee ; Hoyeop Lee ; Chang Ouk Kim (2014)
  • Subject: Product Diffusion, Pricing and Time Strategies, Korean Mobile Phone Market, Sensitivity Analysis

In this study, we are interested in the problem of determining the pricing and timing strategies of a new product by developing an agent-based product diffusion simulation. In the proposed simulation model, agents imitate behavioural consumers, who are reference dependent and risk averse in the evaluation of new products and whose interactions create word-of-mouth regarding new products. Pricing and timing strategies involve the timing of a new product release, the timing of providing a discount on a new product, and the relative rates of discounts. We conduct two experiments in this study. In both experiments, we consider the urban young person segment in the mobile phone market in Korea, in which three major new products - two smartphones and one convergence product - compete with one another. The first experiment is sensitivity analysis on the product life cycle and social influence. The objective is to observe how consumer agents behave as the product life cycle and the degree of sensitivity on social influence change. The second experiment is sensitivity analysis on time-to-market, time-to-discount, and amount-to-discount. The marketing strategy that maximises the sales volume (or revenue) of the new convergence product is sought from the sensitivity analysis. Based on the result, we provide pricing and timing implications for firms pursuing sales volumes (or revenue) increase.
  • References (17)
    17 references, page 1 of 2

    ABDELLAOUI, M., Bleichrodt, H., & Paraschiv, C. (2007). Loss aversion under prospect theory: a parameter-free measurement. Management Science, 53, 1659-1674. [doi:10.1287/mnsc.1070.0711] BARBERIS, N., Huang, M., & Santos, T. (2001). Prospect theory and asset prices.Quarterly Journal of Economics, 116(1), 1-53.

    [doi:10.1162/003355301556310] BASS, F.M. (1969). A new product growth model for consumer durables. Management Science, 15(5), 215-227. [doi:10.1287/mnsc.15.5.215] BEGGS, A., & Graddy, K. (2009). Anchoring effects: evidence from art auctions.American Economic Review, 99(3), 1027-1039.

    [doi:10.1257/aer.99.3.1027] BENTZ, Y., Merunka, D. (2000). Neural Networks and the Multinomial Logit for Brand Choice Modelling: a Hybrid Approach.Journal of Forecasting, 19, 177-200. [doi:10.1002/(SICI)1099-131X(200004)19:3<177::AID-FOR738>3.0.CO;2-6] BLEICHRODT, H., Pinto, J.L., Wakker, P.P. (2001). Making Descriptive Use of Prospect Theory to Improve the Prescriptive Use of Expected Utility. Management Science, 47(11), 1498-1514. [doi:10.1287/mnsc.47.11.1498.10248] BROMILEY, P. (2009) A Prospect theory model of resource allocation. Decision Analysis, 6(3), 124-138. [doi:10.1287/deca.1090.0142] CAMERER, C., & Thaler, R. H. (1995). Anomalies: ultimatums, dictators and manners. The Journal of Economic Perspectives, 9(2), 209-219.

    [doi:10.1257/jep.9.2.209] GEYER, R., & Blass, V.D. (2010). The economics of cell phone reuse and recycling.International Advanced Manufacturing Technology, 47, 515- 525. [doi:10.1287/mksc.1040.0071] GUARDAGNI, P., & J. Little. 1983. A logit model of brand choice calibrated on scanner data.Marketing Science, 2, 203-238.

    [doi:10.1057/jors.2009.170] GUNTHER, M., Stummer, C., Wakolbinger, LM, & Wildpaner, M. (2011). An agent-based simulation approach for the new product diffusion of a novel biomass fuel. Journal of the Operational Research Society, 62, 12-20. [doi:10.1287/mksc.12.4.378] HARDIE, B.G.S., Johnson, E.J., & Fader, P.S.(1993). Modeling loss aversion and reference dependence effects on brand choice. Marketing Science, 12(4), 378-394. [doi:10.1007/s10683-008-9203-7] HARRISON, G.W., & Rutström, E.E. (2009). Expected utility theory and prospect theory: one wedding and a decent funeral. Experimental Economics, 12, 133-158. [doi:10.1287/mnsc.1100.1269] HE, X.D., & Zhou, X.Y. (2011). Portfolio choice under cumulative prospect theory: an analytical treatment, Management Science, 57(2), 315-331.

    [doi:10.1016/0165-0114(95)00305-3] HONG, T-P., & Lee, C-Y. (1996). Induction of fuzzy rules and membership functions from training examples.Fuzzy Sets and Systems, 84, 33-47.

    [doi:10.1016/j.jbusres.2007.02.003] JAGER, W. (2007). The four P's in social simulation, a perspective on how marketing could benefit from the use of social simulation, Journal of Business Research , 60, 868-875. [doi:10.1162/106454603322694807] JANSSEN, M.A., & Jager, W. (2003). Simulating market dynamics interactions between consumer psychology and social networks. Artificial Life, 9, 343-356. [doi:10.2307/1914185] KAHNEMAN, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk,Econometrica, 47(2), 263-292.

    [doi:10.1145/956750.956769] KIM, S., Lee, K., Cho, J. K., & Kim, C. O. (2011). Agent-based diffusion model for an automobile market with fuzzy TOPSIS-based product adoption process. Expert Systems with Applications, 38(6), 7270-7276.

    LEE, K., Kim, S., Kim, C. O., & Park, TH. (2013). An agent-based competitive product diffusion model for the estimation and sensitivity analysis of social network structure and purchase time distribution. Journal of Artificial Societies and Social Simulation, 16(1), 3 http://jasss.soc.surrey.ac.uk/16/1/3.html. [doi:10.1109/ICPPW.2010.70] LI, X., Ortiz, P.J., Browne, J., Franklin, D., Oliver, J.Y., Geyer, R., Zhou, Y., & Chong, F.T. (2010). Smartphone Evolution and Reuse: Establishing a more Sustainable Model. 2010 39th International Conference on Parallel Processing Workshops. [doi:10.2307/1252170] MAHAJAN, V., Muller, E., & Bass, F.M. (1990). New product diffusion models in marketing: a review and directions for research.Journal of Marketing, 54, 1-26.

    MARDIA, K.V., Kent, J.T., & Bibby, J.M. (1979). Multivariate Analysis. Academic Press.

  • Metrics
    No metrics available
Share - Bookmark