Dynamic pricing is the practice of offering different prices to consumers based on various factors designed to maximize sales and profits, which may include the retailer’s perception of the willingness of a particular consumer to pay at a given price point, often in connection with other factors such as a given point in time. Airlines use dynamic pricing based on complex data analyses involving a myriad of factors, including time of day and week, fare class and availability. The ride share service Uber’s surge pricing dynamically bases fares on supply and demand at a given moment. Making projections of whether consumers will pay more or less under different circumstances is an evolving art that can be aided by data analytics, including, now that the data is increasingly available, consumer profiling based on historical consumer behavior and even A/B testing — the practice of testing for different reactions by the same subject based on variables. This can be the basis for personalized pricing, also known as first-degree or primary price differentiation, the “holy grail” of which is to develop a methodology for “perfect price discrimination” that maximizes the amount each individual consumer is willing to pay.
Perfect price discrimination is only theoretically possible since the seller must know each buyer’s reservation price (the maximum they will pay) and individualize an offer to them at that price, thus not leaving any potential revenues uncaptured. Beyond the difficulty in ascertaining that information, the market prevents perfect price discrimination through competition and real-time data enables real-time competitive offerings. However, since less price conscious consumers may be less inclined to shop the competition, and more cost conscious consumers are more inclined to look for lower equivalent offers, e-tailers have an incentive to try to identify which consumers are which when they are in front of them and offer them personalized pricing based on their price sensitivity.
Personalized Pricing and Privacy
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