Personalized pricing has often been defined as the ability to charge different prices to different consumers. The price offered to a consumer whose valuation for a product or service is known may be higher or lower than the posted uniform price charged by firms that lack the sophistication to target individual consumers. Various technologies exist today that allow firms to identify and track individual customers. For example, the online data provider Lexis-Nexis sells to virtually every user at a different price. Personalized pricing, as the name suggests, involves tailoring prices completely to the individual customer. It is easy to see how this can increase profits. For example, suppose there are four potential customers (Andrew, Carl, Dale and Hal), and each person just wants one subscription to an online financial information service, suppose that information sharing among these four is impossible, or simply too costly. The seller has some fixed cost of gathering the information, but the marginal cost of delivering it to a customer is negligible. Suppose the seller knows the maximum each customer is willing to pay (Reservation prices). These reservation prices are: Andrew $200, Carl $160, Dale $140, Hal $110. Given the fact that marginal cost is (close to) zero, if the information service has to announce a price at which it will serve all customers who come to it, it maximizes its profits by charging $110, the most that Hal, with the lowest valuation, is willing to pay. This yields the firm $440.
Now if the information service can charge each of the four potential customers his maximum willingness to pay, it does much better. Its revenue in this case is $610. This maximizes the firm’s profit from these four customers.
The concept of structuring and presenting offerings and prices in an easy-to-understand, transparent format can be readily adapted to almost any