Future research directions in demand management
- 4 Downloads
Pricing and revenue management faces new research challenges against the background of new markets for trading of personal data, new regulations on data privacy, opportunities for personalised pricing, demand learning and many more emerging trends and developments. In order to explore these challenges, the British Engineering and Physical Sciences Research Council funded an interdisciplinary workshop to identify future research directions in demand management. The workshop (led by the authors Strauss and Currie) took place in September 2017 in London, and brought together 33 academics and practitioners in demand management and related disciplines, including law, computer science, digital marketing and operational research.
KeywordsDemand management Future research directions Pricing
- Ban, Gah‐Yi, and Keskin, N. Bora. 2017. Personalized Dynamic Pricing with Machine Learning. http://dx.doi.org/10.2139/ssrn.2972985Accessed 23 May 2017.
- Chen, X., Z. Owen, C. Pixton, and D. Simchi-Levi. 2015. A Statistical Learning Approach to Personalization in Revenue Management. http://dx.doi.org/10.2139/ssrn.2579462 Accessed 15 Mar 2015.
- DMA. 2015. Data privacy: what the consumer really thinks. https://dma.org.uk/uploads/ckeditor/Data-privacy-2015-what-consumers-really-thinks_final.pdf. Accessed June 2015.
- Kallus, N., and M. Udell. 2016. Dynamic Assortment Personalization in High Dimensions. https://arxiv.org/abs/1610.05604. Accessed Sept 2017.
- Strauss, A.K., R. Klein, and C. Steinhardt. 2018. A Review of Choice-based Revenue Management: Theory and Methods. Forthcoming in European Journal of Operational Research. Google Scholar