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Pricing Optimization Using R

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  • Alexandru CONSTÃNGIOARÃ
  • Gyula-Laszlo FLORIAN

Abstract

The proposed empirical research uses a sample of 199 records from automotive industry to analyze the characteristics of pricing optimization in this industry. R software is being employed to estimate an OLS model. The coefficient estimates from our OLS estimations were used to generate a linear demand function, with errors normally distributed and standard deviation given by historic data available for the analysis. Finally, we employ GGPLOT2 package to generate the visualization of revenues and profits corresponding to different price levels. Our approach provides management with insights into the measures and steps necessary to achieve the full potential of pricing optimization across products and customers. Besides policy implications for management, our research underlines the benefits of using a quantitative approach to offer management relevant information necessary to fundament an efficient price policy.

Suggested Citation

  • Alexandru CONSTÃNGIOARÃ & Gyula-Laszlo FLORIAN, 2019. "Pricing Optimization Using R," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 13(1), pages 142-149, November.
    • Handle: RePEc:rom:mancon:v:13:y:2019:i:1:p:142-149
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    References listed on IDEAS

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    1. Vohra,Rakesh V. & Krishnamurthi,Lakshman, 2012. "Principles of Pricing," Cambridge Books, Cambridge University Press, number 9781107010659, October.
    2. Omar Besbes & Assaf Zeevi, 2015. "On the (Surprising) Sufficiency of Linear Models for Dynamic Pricing with Demand Learning," Management Science, INFORMS, vol. 61(4), pages 723-739, April.
    3. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 225-238.
    4. Claudiu VINTE & Titus Felix FURTUNA & Marian DARDALA, 2017. "R Spatial and GIS Interoperability for Ethnic, Linguistic and Religious Diversity Analysis in Romania," Romanian Statistical Review, Romanian Statistical Review, vol. 65(4), pages 85-97, December.
    5. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    6. E. Andrew Boyd, 2007. "The Future of Pricing," Palgrave Macmillan Books, Palgrave Macmillan, number 978-0-230-60690-6, December.
    7. William L. Cooper & Tito Homem-de-Mello & Anton J. Kleywegt, 2015. "Learning and Pricing with Models That Do Not Explicitly Incorporate Competition," Operations Research, INFORMS, vol. 63(1), pages 86-103, February.
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    Keywords

    price policy; pricing optimization; R; GGPLOT2;
    All these keywords.

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