A Study on Optimizing Price Using Business Rule as a Prior
DOI:
https://doi.org/10.61841/2g9j3887Keywords:
Price Optimization, Business rules, prior information, Monte Carlo algorithm, Bayes theoremAbstract
This study tries to propose the new model to have more accurate price optimization for a specific business. Usually, the optimal pricing solutions which come from the estimated sales models suggest prices that are unfairly inaccurate, which leads the manager to come up with some decision rules and constraints on the shelf price as a more appropriate solution. Using this information as a post hoc instead of prior information leads to inefficient pricing decisions. This study tries to use manager’s constraints on the price solution as a prior information about the model. The study argues that the model and its prior (Business Rules about optimal prices) can be translated into informative prior distributions. These prior distributions appropriately weight the managerial knowledge against the data unlike the traditional approach. Moreover, this study considers situations in which the analyst may not know either the business rule or model with complete certainty of demand and illustrate the impact of this uncertainty on the optimal pricing solution using Bayes theorem by conducting Monte Carlo algorithm.
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References
1. Davis, S.M., Huckabay, G.L. and Smith, J.S., REVIONICS Inc, 2010. Market-based price optimization system. U.S. Patent 7,853,473.
2. DegliEsposti, S., 2014. When big data meets dataveillance: The hidden side of analytics. Surveillance & Society, 12(2), p.209.
3. Hilton, R.W. and Platt, D.E., 2013. Managerial accounting: creating value in a dynamic business environment. McGraw-Hill Education.
4. Horngren, C.T., Foster, G., Datar, S.M., Rajan, M., Ittner, C. and Baldwin, A.A., 2010. Cost accounting: A managerial emphasis. Issues in Accounting Education, 25(4), pp.789-790.
5. Love, D.E.P., Gharavi, H. and Merchant, V., 2008. CRM technology and bulding material suppliers. In European and mediterranean conference on information systems.
6. McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D.J. and Barton, D., 2012. Big data: the management revolution. Harvard business review, 90(10), pp.60-68.
7. Oladepo, O.I. and Abimbola, O.S., 2015. The influence of brand image and promotional mix on consumer buying decision-a study of beverage consumers in Lagos State, Nigeria. British journal of marketing studies, 3(4), pp.97-109.
8. Patrick Schneidau, 2017. TOP 8 MYTHS OF Price OPTiMizaTiOn. Pros Education United states.
9. Phillips, R.L., 2005. Pricing and revenue optimization. Stanford University Press.
10. Shiller, B.R., 2014. First-degree price discrimination using big data. Available at from Ben Shiller’s hompage at http://benjaminshiller. com/images/First_Degree_PD_Using_ Big_Data_Apr_8, _2014. pdf.
11. Taylor, L.D. and Houthakker, H.S., 2009. Consumer demand in the United States: Prices, income, and consumption behavior. Springer Science & Business Media.
12. Wildavsky, A., 2017. Speaking truth to power: Art and craft of policy analysis. Routledge.
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