Online price discrimination: Conspicuous by its absence
The mystery about online price discrimination is why so little of it seems to be happening.
Consumer advocates and journalists among others have been trying to find smoking gun evidence of price discrimination — the overt kind where different customers are charged different prices for identical products based on how much they are willing to pay. (By contrast, examples of covert or concealed price discrimination abound; see, for example, my 2011 article.) Back in 2000 Amazon tried a short-lived experiment where prices of DVDs for new and for regular users were different. But that remains essentially the only example.
This should be surprising. Tailoring prices to individuals is far more technically feasible online than offline, since shoppers are either identified or at least have loads of behavioral data associated with their pseudonymous cookies. The online advertising industry claims that this is highly effective for targeting ads; estimating consumers’ willingness to pay shouldn’t be much harder. Clearly, price discrimination has benefits to firms engaging in it by allowing them to capture more of the “consumer surplus.” (Whether or not it is beneficial to consumers is a more controversial question that I will defer to a future post.) In fact, based on technical feasibility and economic benefits, one might expect the practice to be pervasive.
The evidence (or lack thereof)
A study out of Spain last year took a comprehensive look at online merchants, by far the most thorough analysis of its kind. They created two “personas” with different browsing histories — one of which visited discount sites and the other visited sites for luxury products. Each persona then browsed 200 e-commerce sites as well as search engines to see if they were treated differently. Here’s what the authors found:
- There is evidence for search discrimination or steering where the high- and low-income personas are shown ads for high-end and low-end products respectively. In my opinion, the line between this practice and plain old behavioral advertising is very, very slim. 
- There is no evidence for price discrimination based on personas/browsing histories.
- Three of the 200 retailers including Staples varied prices based on the user’s location, but necessarily not in a way that can’t be explained by costs of doing business.
- Visitors coming from one particular deals site (nextag.com) saw lower prices at various retailers. (Discounting and “deals” are very common forms of concealed price discrimination.)
A new investigation by the Wall Street Journal analyzes Staples in more detail. While the Spain study found geographic variation in prices, the WSJ study goes further and shows a strong correlation between lower prices and consumers’ ability to drive to competitors’ stores, which is an indicator of willingness to pay. I’m not 100% convinced that they’ve ruled out alternative hypotheses, but it does seem plausible that Staples’ behavior constitutes actual price discrimination, even though geography is a far cry from utilizing behavioral data about individuals.
Other findings in the WSJ piece are websites that offer discounts for mobile users and location-dependent pricing on Lowe’s and Home Depot’s websites but with little evidence of being based on anything but costs of doing business.
So there we have it. Both studies are very thorough, and I commend the authors, but I consider their results to be mostly negative — very few companies are varying prices at all and none are utilizing anywhere near the full extent of data available about users. Other price discrimination controversies include steering by Orbitz and a hastily-retracted announcement by Coca Cola for vending machines that would tailor prices to demand. Neither company charged or planned to charge different prices for the same product based on who the consumer was.
In short, despite all the hubbub, I find overt price discrimination conspicuous by its absence. In a follow-up post I will propose an explanation for the mystery and see what we can learn from it.
 This is an automatic consequence of collaborative recommendation that suggests products to users based on what similar users have clicked on/purchased in the past. It does not require that any explicit inference of the consumer’s level of affluence be made by the system. In other words, steering, bubbling etc. are inherent features of collaborative filtering algorithms which drive personalization, recommendation and information retrieval on the Internet. This fact greatly complicates attempts to define, detect or regulate unfair discrimination online.
Thanks to Aleecia McDonald for reviewing a draft.