First-degree price discrimination has been around ever since people began bartering and exchanging goods. 1 It is simply an attempt to charge different prices to different customers for the same product. Figure 2.1 presents an example of an aggregate demand curve for a cord of wood in a small town. In an ideal world, from the producer’s perspective, one producer could identify each consumer’s willingness-to-pay function and set prices accordingly (cf. Varian 1996). Let us assume that one company owns all of the timber in the area and is therefore a monopoly. Instead of charging $40 to each consumer, the monopolist charges a different price to each consumer depending on their ability and willingness to pay for the cord of wood. This is essentially personalized pricing, where the selling price is customized for each buyer. This is a good strategy for a monopolist because they can generate more revenue than just picking a single price point. Each consumer is thus charged a different price for the same product.
This strategy is also known as perfect price discrimination. Personalized pricing is very difficult to implement in practice for four reasons. First, it is difficult to identify the willingness-to-pay functions for each consumer. Second, customers often get upset when they find out that another consumer has paid less for a product or service than they have paid. The third reason that personalized pricing causes problems is that perfect price discrimination can lead to arbitrage, where opportunistic buyers purchase the product at a discounted price in one market and then sell it at a profit in another market. The fourth and final reason that it is difficult to implement is that, in certain instances, it is illegal. This issue will be dealt with at the end of the chapter.
Though personalized pricing is difficult to implement, it can be accomplished and is in fact embraced by some companies. Amazon, for example, presents their customers with personalized product recommendations using past search and buying behavior, and large supermarkets use their scanner data to configure promotions tailored to their customers.
Personalized pricing requires the effective measurement of consumer preferences. The supplier must in some way conduct market research to determine individualized pricing strategies. This can be accomplished by using technology to analyze historical buying patterns. Online retailers, such as Amazon, can very easily analyze transactions using historical data. Offline retailers have to collect and sort the data from a variety of sources unless their customers participate in a rewards program or a customer discount program that incorporates a mechanism for gathering customer transaction information. Amazon has participated in many of types of personalized marketing and pricing schemes because they have the infrastructure in place to gather and analyze behavior. Companies such as Amazon use some form of collaborative filtering to determine product recommendations for books, videos, and many other products.