E commerce, big data and personalized pricing

Posted by on October 21, 2017  /  0 Comments

In the course of our policy work related to big data, we discussed first-degree price discrimination:

At a more abstract level, the problem is one of first-degree price discrimination. First-degree price discrimination, or person-specific pricing, has not been practiced or observed because it was not possible to discern reservation values. This constraint may be in the process of being overcome now that capabilities exist to analyze individual behavior as recorded in multiple transaction-generated data sets (Shiller, 2014). Big data and electronic commerce have reduced the costs of targeting and first-degree price discrimination. It is argued that the increased availability of behavioral data may encourage a shift from third-degree price discrimination towards personalized pricing (Executive Office of the President of the United States, 2015).

But now the Harvard Business Review is speculating that awareness of price discrimination on the part of consumers could lead to a complicated negotiation process that could nullify the reduction of transaction costs that is the principal advantage of e commerce:

Whether personalized pricing catches on with web retailers is now up to consumers. Will shoppers be comfortable knowing that the prices they are offered may be higher than those presented to others? Will buyers relish “electronically bargaining” to outwit sellers? Retailers first “negotiate” with each customer by personalizing prices based on their profile. In response, savvy shoppers will “bargain” by checking prices on different devices, clearing caches, using the app, conducting multiple searches, asking friends in different cities to see what price they’re quoted, and so on. Or will they become fed up and steer clear of web retailers that price profile? Amazon is on the record as stating that all of its customers see the same prices — will other retailers be so clear-cut?

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