Pricing Analytics: Modeling, Theory and Algorithms

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The objective of this award is to develop novel dynamic pricing models that take into account pertinent, empirically validated consumer behaviors. Building upon empirical data from industrial partners and open databases, we plan to construct accurate and tractable demand models that are amenable to pricing optimization. Specifically, we will explore demand models that incorporate consumers' memory-based reference prices under dynamic pricing. We will then use these demand models to build pricing optimization models that take into account a variety of complex practical operation constraints. The project's demand models and decision models range from single products to multiple products, from durable products to perishable products, and from deterministic settings to stochastic settings. Developing advanced analytical techniques and efficient algorithms by exploiting the special structures of these models will be essential to this project. If successful, this research will lead both to novel and comprehensive analytical models and to advanced methodologies and efficient algorithms that may be used to attack the resulting challenging non-smooth, non-convex optimization problems. These models, methodologies, and algorithms will be critical for the development of the much needed decision support systems to improve companies' competitive advantages. Preliminary research demonstrates that memory-based reference price models result in complex price dynamics and raise a host of challenging yet practical research questions that the existing literature does not yet address. The novel theory and techniques we expect to develop will not only successfully address these questions, but will also be useful in other research areas such as dynamic systems.