Dynamic Pricing Task

A Dynamic Pricing Task is a price determination task that is a dynamic task.

• AKA: Surge Pricing, Demand Pricing.
• Context:
  • It can (typically) be required where there are predictable changes in Item Demand.
  • It can be solved by a Dynamic Pricing System.
  • It can be supported by Demand Forecasting.
  • …
• Example(s):
  • Uber's surge pricing.
  • …
• Counter-Example(s):
  • Offline Pricing, such as annual pricing.
  • Price Negotiation.
• See: Pricing Strategy, Auction, Auctioning.

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References

2016

• (Wikipedia, 2016) ⇒ https://en.wikipedia.org/wiki/dynamic_pricing Retrieved:2016-7-17.
  • Dynamic pricing, also referred to as surge pricing or demand pricing, is a pricing strategy in which businesses set flexible prices for products or services based on current market demands. Business are able to change prices based on algorithms that take into account competitor pricing, supply and demand, and other external factors in the market. Dynamic pricing is a common practice in several industries such as hospitality, travel, entertainment, and retail. Each industry takes a slightly different approach to repricing based on its needs and the demand for the product.

2015

• (Boer, 2015) ⇒ Arnoud V. den Boer. (2015). “Dynamic Pricing and Learning: Historical Origins, Current Research, and New Directions.” In: Surveys in operations research and management science, 20(1). doi:10.1016/j.sorms.2015.03.001
  • QUOTE: Dynamic pricing is the study of determining optimal selling prices of products or services, in a setting where prices can easily and frequently be adjusted. …

    … This consideration is a main driver of research on dynamic pricing and learning: the study of optimal dynamic pricing in an uncertain environment where characteristics of consumer behavior can be learned from accumulating sales data.

2010

• (Farias & Roy, 2010) ⇒ Vivek F. Farias, and Benjamin Van Roy. (2010). “Dynamic Pricing with a Prior on Market Response." Operations Research 58, no. 1
  • QUOTE: We study a problem of dynamic pricing faced by a vendor with limited inventory, uncertain about demand, aiming to maximize expected discounted revenue over an infinite time horizon. The vendor learns from purchase data, so his strategy must take into account the impact of price on both revenue and future observations. We focus on a model in which customers arrive according to a Poisson process of uncertain rate, each with an independent, identically distributed reservation price. Upon arrival, a customer purchases a unit of inventory if and only if his reservation price equals or exceeds the vendor’s prevailing price. We propose a simple heuristic approach to pricing in this context, which we refer to as decay balancing. Computational results demonstrate that decay balancing offers significant revenue gains over recently studied certainty equivalent and greedy heuristics. We also establish that changes in inventory and uncertainty in the arrival rate bear appropriate directional impacts on decay balancing prices in contrast to these alternatives, and we derive worst-case bounds on performance loss. We extend the three aforementioned heuristics to address a model involving multiple customer segments and stores and provide experimental results demonstrating similar relative merits in this context.

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