\”Price discrimination\” has a specific technical meaning for economists. It\’s not about sellers charging more to certain groups because of biased attitudes about gender, race/ethnicity, religion, or sexual orientation. Instead, it\’s about setting up a varying set of prices in order to charge more to those who are willing to pay more–unlike the standard situation in a plain vanilla market in which everyone pays the same price.

There are lots of examples of price discrimination. When a movie is first released, the ticket prices are typically higher in \”first-run\” theaters than when the movie arrives at \”second-run\” theaters a few months later. Books are often released first in more-expensive hard-cover editions, and later in less-expensive paperbacks. Those who aren\’t sure about going out for dinner are enticed by happy hour and early-bird specials, while those willing to pay more arrive later in the evening. There are discounts for students or senior citizens. There are volume discounts for buying a larger quantity of a good. Such arrangements often seem potentially beneficial to both buyers and sellers.

But there\’s one more kind of price discrimination called \”personalized pricing,\” in which prices would vary across individuals so that everyone would be charged as much as they were willing to pay. This seems more problematic, and a combination of big data and online retail may be bringing it our way. Ariel Ezrachi and Maurice E. Stucke write about \”The rise of behavioural discrimination,\” in the European Competition Law Review (2016, 12: 485-492; not freely available online). They also refer to an Executive Office of the President report from February 2015, \”Big Data and Differential Pricing.\” That report sets up the issue in this way:

\”Economics textbooks usually define three types of differential pricing. Personalized pricing, or first-degree price discrimination, occurs when a seller charges a different price to every buyer. Individually negotiated prices, such as those charged by a car dealer, are an example of personalized pricing. Quantity discounts, or second-degree price discrimination, occur when the per-unit price falls with the amount purchased, as with popcorn at the movie theater. Finally, third-degree price discrimination occurs when sellers charge different prices to different demographic groups, as with discounts for senior citizens.

Big data has lowered the costs of collecting customer-level information, making it easier for sellers to identify new customer segments and to target those populations with customized marketing and pricing plans. The increased availability of behavioral data has also encouraged a shift from third-degree price discrimination based on broad demographic categories towards personalized pricing. Nevertheless, differential pricing still presents several practical challenges. First, sellers must figure out what customers are willing to pay. This can be a complex problem, even for companies with lots of data and computing power. A second challenge is competition, which limits a company’s ability to raise prices, even if it knows that one customer might be willing to pay more than another. Third, companies need to prevent resale by customers seeking to exploit price differences. And finally, if a company does succeed in charging personalized prices, it must be careful not to alienate customers who may view this pricing tactic as inherently unfair. …

Ultimately, whether differential pricing helps or harms the average consumer depends on how and where it is used. In a competitive market with transparent pricing, the benefits are likely to outweigh the costs.  …  Ultimately, differential pricing seems most likely to be harmful when implemented through complex or opaque pricing schemes designed to screen out unsophisticated buyers. For example, companies may obfuscate by bundling a low product price with costly warranties or shipping fees, using “bait and switch” techniques to attract unwary customers with low advertised prices and then upselling them on different merchandise, or burying important details in the small print of complex contracts.When these tactics work, the economic intuition that differential pricing allows firms to serve more price-sensitive customers at a lower price-point may even be overturned. If price-sensitive customers also tend to be less experienced, or less knowledgeable about potential pitfalls, they might more readily accept offers that appear fine on the surface but are actually full of hidden charges. ….\”

Ezrachi and Stucke point out a number of ways in which these issues are becoming a practical reality. The collection and interconnection of big data from a wide variety of sources creates the possibility that when you shop on-line, the seller may already know quite a lot about you. They write:

\”As the volume, variety and value of personal data increases, self-learning pricing algorithms can use the data collected on you and other people to identify subgroups of like-minded, like-price-sensitive individuals, who share common biases and levels of willpower. Pricing algorithms can use data on how other people within your grouping react, to predict how you will likely react under similar circumstances. This then enables the self-learning algorithm to more accurately approximate the user\’s reservation price, observe behaviour, and adjust. The more time we spend online–chatting, surfing, and purchasing–the more times the algorithm can observe what you and others within your grouping do under various circumstances; the more experiments it can run; the more it can learn through trial and error what your group\’s reservation price is under different situations; and, the more it can recalibrate and refine (including shifting you to another group). 

\”To better train their algorithms and categorize even smaller groups of individuals, firms will need personal data. Among other things, this trend will accelerate the \”Internet of Things\”, as firms compete to collect data on consumers\’ activities at home, work, and outside. Smart appliances, cars, utensils, and watches can help firms refine their consumer profiles and gain a competitive edge. Thus in making use of our demographics, physical location (via our phones), browser and search history, friends and links on social networks, and online reviews and blog posts, firms can target us with personalised advertisements with ever increasing proficiency. Also, at the point of sale, the categorisation can help sellers approximate our price sensitivity.\”

It used to be said that when you go to a website, you are like a person with a name-tag at a convention: that is, you could be identified, but others didn\’t necessarily know much about you. But in the future, when you go to a website, certain sellers at least will already know a great deal about you. With this information, the seller will be able to customize your retail experience by manipulating the information presented about products, choices, prices, and deals in ways that makes someone with your specific characteristics more likely to buy and to pay higher prices.

This could be done in literally dozens of ways. One example from Ezrachi and Stucky is that the first item presented in an online list of possibilities will both be a decoy designed with your characteristics in mind: it will also be higher-priced, and perhaps lacking in some features.  When you scroll down the list, you will find other items that have lower prices or more features. Compared to the decoy item, these look like good deals. A standard example in regular retailing is that many restaurants report that the second most-expensive bottle of wine and the second least-expensive bottle of wine are among their top seller, because those who want to splurge can feel they are being a little thrifty, and those who want inexpensive can feel they aren\’t being totally cheap. \”So we may have originally intended to purchase a cheaper item, but chose a more expensive item with perhaps a few more attributes, as it was relatively more attractive than the personalised decoy option.\”

Another option is \”price-steering,\” where a website makes it easier to find more expensive options. Or firms can make strategic use of complexity: \”To better discriminate, companies can take advantage of consumers\’ difficulty in processing many complex options. Companies may deliberately increase the complexity by adding price and quality parameters, with the intent to facilitate consumer
error or bias and manipulate consumer demand to their advantage. By increasing their products\’ complexity, firms can also make it difficult to appraise quality and compare products, increase the consumers\’ search and evaluation costs, and nudge consumers to rely on basic signalling that benefits the firms. Once the customer is snagged, the complexity in contract terms can increase
the customers\’ switching costs and increase the likelihood of customers retaining the personalised default option.  This enables firms to inch closer to perfect behavioural discrimination.\”

Notice that none of these strategies involve the seller actually lying. In fact, one can easily think of circumstances where these options could benefit consumers, by providing them with the selection of products and information that they actually find most attractive. But it\’s also easy to think of ways in which people can be manipulated. Ezrachi and Stucke write:

The road to near-perfect behavioural discrimination will be paved with personalised coupons and promotions: the less price-sensitive online customers may not care as much if others are getting promotional codes, coupons, and so on, as long as the list price does not increase. Online sellers will increasingly offer consumers with a lower reservation price a timely coupon-ostensibly for being a valued customer, a new customer, a returning customer, or a customer who won the discount. The coupon may appear randomly assigned, but only customers with a lower reservation price are targeted. Indeed, the price discrimination can happen on other, less salient aspects of the purchase. Retailers can offer the same price, but provide greater discounts on shipping (or faster delivery), offer complimentary customer service, or better warranty terms to attract customers with lower reservation prices, greater willpower, or more outside options.

In the brave new world of big data and online purchases. buyers really do need to be wary. And one suspects that the Federal Trade Commission and other consumer protection agencies are going to become active participants in determining what tools sellers can use.

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