Posts tagged ‘price discrimination’
Given the pervasive tracking and profiling of our shopping and browsing habits, one would expect that retailers would be very good at individualized price discrimination — figuring out what you or I would be willing to pay for an item using data mining, and tailoring prices accordingly. But this doesn’t seem to be happening. Why not?
This mystery isn’t new. Mathematician Andrew Odlyzko predicted a decade ago that data-driven price discrimination would become much more common and effective (paper, interview). Back then, he was far ahead of his time. But today, behavioral advertising at least has gotten good enough that it’s often creepy. The technology works; the impediment to price discrimination lies elsewhere. 
It looks like consumers’ perception of unfairness of price discrimination is surprisingly strong, which is why firms balk at overt price discrimination, even though covert price discrimination is all too common. But the covert form of price discrimination is not only less efficient, it also (ironically) has significant social costs — see #3 below for an example. Is there a form of pricing that allows for perfect discrimination (i.e., complete tailoring to individuals), in a way that consumers find acceptable? That would be the holy grail.
In this post, I will argue that the humble coupon, reborn in a high-tech form, could be the solution. Here’s why.
1. Coupons tap into shopper psychology. Customers love them.
Coupons, like sales, introduce unpredictability and rewards into shopping, which provides a tiny dopamine spike that gets us hooked. JC Penney’s recent misadventure in trying to eliminate sales and coupons provides an object lesson:
“It may be a decent deal to buy that item for $5. But for someone like me, who’s always looking for a sale or a coupon — seeing that something is marked down 20 percent off, then being able to hand over the coupon to save, it just entices me. It’s a rush.”
Some startups have exploited this to the hilt, introducing “gamification” into commerce. Shopkick is a prime example. I see this as a very important trend.
2. Coupons aren’t perceived as unfair.
Given the above, shoppers have at best a dim perception of coupons as a price discrimination mechanism. Even when they do, however, coupons aren’t perceived as unfair to nearly the same degree as listing different prices for different consumers, even if the result in either case is identical. 
3. Traditional coupons are not personalized.
While customers may have different reasons for liking coupons, from firms’ perspective the way in which traditional coupons aid price discrimination is pretty simple: by forcing customers to waste their time. Econ texts tend to lay it out bluntly. For example, R. Preston McAfee:
Individuals generally value their time at approximately their wages, so that people with low wages, who tend to be the most price-sensitive, also have the lowest value of time. … A thrifty shopper may be able to spend an hour sorting through the coupons in the newspaper and save $20 on a $200 shopping expedition … This is a good deal for a consumer who values time at less than $20 per hour, and a bad deal for the consumer that values time in excess of $20 per hour. Thus, relatively poor consumers choose to use coupons, which permits the seller to have a price cut that is approximately targeted at the more price-sensitive group.
Clearly, for this to be effective, coupon redemption must be deliberately made time-consuming.
To the extent that there is coupon personalization, it seems to be for changing shopper behavior (e.g., getting them to try out a new product) rather than a pricing mechanism. The NYT story from last year about Target targeting pregnant women falls into this category. That said, these different forms of personalization aren’t entirely distinct, which is a point I will return to in a later article.
4. The traditional model doesn’t work well any more.
Paper coupons have a limited future. As for digital coupons, there is a natural progression toward interfaces that make it easier to acquire and redeem them. In particular, as more shoppers start to pay using their phones in stores, I anticipate coupon redemption being integrated into payment apps, thus becoming almost frictionless.
An interesting side-effect of smartphone-based coupon redemption is that it gives the shopper more privacy, avoiding the awkwardness of pulling out coupons from a purse or wallet. This will further open up coupons to a wealthier demographic, making them even less effective at discriminating between wealthier shoppers and less affluent ones.
5. The coupon is being reborn in a data-driven, personalized form.
With behavioral profiling, companies can determine how much a consumer will pay for a product, and deliver coupons selectively so that each customer’s discount reflects what they are willing to pay. They key difference is what while in the past, customers decided whether or not to look for, collect, and use a coupon, in the new model companies will determine who gets which coupons.
In the extreme, coupons will be available for all purchases, and smart shopping software on our phones or browsers will automatically search, aggregate, manage, and redeem these coupons, showing coupon-adjusted prices when browsing for products. More realistically, the process won’t be completely frictionless, since that would lose the psychological benefit. Coupons will probably also merge with “rewards,” “points,” discounts, and various other incentives.
There have been rumblings of this shift here and there for a few years now, and it seems to be happening gradually. Google’s acquisition of Incentive Targeting a few months ago seems significant, and at the very least demonstrates that tech companies are eyeing this space as well, and not just retailers. As digital feudalism takes root, it could accelerate the trend of individualized shopping experiences.
In summary, personalized coupons offer a vehicle for realizing the full potential of data mining for commerce by tailoring prices in a way that consumers seem to find acceptable. Neither coupons nor price discrimination should be viewed in isolation — together with rewards and various other incentive schemes, they are part of the trend of individualized, data mining-driven commerce that’s here to stay.
 Since I’m eschewing some academic terminology in this post, here are a few references and points of clarification. My interest is in first-degree price discrimination. Any price discrimination requires market power; my assumption is that is the case in practice because competition is always imperfect, and we should expect quite a bit of first-degree price discrimination. The observed level is puzzlingly low.
The impact of technology on the ability to personalize prices is complex, and behavioral profiling is only one aspect. Technology also makes competition less perfect by allowing firms to customize products to a greater degree, so that there are no exact substitutes. Finally, technology hinders first-degree price discrimination to an extent by allowing consumers to compare prices between different retailers more easily. The interaction between these effects is analyzed in this paper.
Technology also increases the incentive to price discriminate. As production becomes more and more automated, marginal costs drop relative to fixed costs. In the extreme, digital goods have essentially zero marginal cost. When marginal production costs are low, firms will try to tailor prices since any sale above marginal cost increases profits.
My use of the terms overt and covert is rooted in the theory of price fairness in psychology and behavioral economics, and relates to the presentation of the transaction. While it is somewhat related to first- vs. second/third-degree price discrimination, it is better understood as a separate axis, one that is not captured by theories of rational firms and consumers.
 An exception is when non-coupon customers are made aware that others are getting a better deal. This happens, for example, when there is a prominent coupon-code form field in an online shopping checkout flow. See here for a study.
Thanks to Sebastian Gold for reviewing a draft, and to Justin Brickell for interesting conversations that led me to this line of thinking.
In my previous article I pointed out that online price discrimination is suspiciously absent in directly observable form, even though covert price discrimination is everywhere. Now let’s talk about why that might be.
By “covert” I don’t mean that the firm is trying to keep price discrimination a secret. Rather, I mean that the differential treatment isn’t made explicit — e.g., by not basing it directly on a customer attribute — and thereby avoiding triggering the perception of unfairness or discrimination. A common example is selective distribution of coupons instead of listing different prices. Such discounting may be publicized, but it is still covert.
The perception of fairness
The perception of fairness or unfairness, then, is at the heart of what’s going on. Going back to the WSJ piece, I found it interesting to see the reaction of the customer to whom Staples quoted $1.50 more for a stapler based on her ZIP code: “How can they get away with that?” she asks. To which my initial reaction was, “Get away with what, exactly? Supply and demand? Econ 101?”
Even though some of us might not feel the same outrage, I think all of us share at least a vague sense of unease about overt price discrimination. So I decided to dig deeper into the literature in psychology, marketing, and behavioral economics on the topic of price fairness and understand where this perception comes from. What I found surprised me.
First, the fairness heuristic is quite elaborate and complex. In a vast literature spanning several decades, early work such as the “principle of dual entitlement” by Kahneman and coauthors established some basics. Quoting Anderson and Simester: “This theory argues that customers’ have perceived fairness levels for both ﬁrm proﬁts and retail prices. Although ﬁrms are entitled to earn a fair proﬁt, customers are also entitled to a fair price. Deviations from a fair price can be justiﬁed only by the ﬁrm’s need to maintain a fair proﬁt. According to this argument, it is fair for retailers to raise the price of snow shovels if the wholesale price increases, but it is not fair to do so if a snowstorm leads to excess demand.”
Much later work has added to and refined that model. A particularly impressive and highly cited 2004 paper reviews the literature and proposes an elaborate framework with four different classes inputs to explain how people decide if pricing is fair or unfair in various situations. Some of the findings are quite surprising. For example: in case of differential pricing to the buyer’s disadvantage, “trust in the seller has a U-shaped effect on price fairness perceptions.”
The illusion of fairness
Sounds like we have a well-honed and sophisticated decision procedure, then? Quite the opposite, actually. The fairness heuristic seems to be rather fragile, even if complex.
Let’s start with an example. Andrew Odlyzko, in his brilliant essay on price discrimination — all the more for the fact that it was published back in 2003  — has this to say about Coca Cola’s ill-fated plans for price-adjusting vending machines: “In retrospect, Coca Cola’s main problem was that news coverage always referred to its work as leading to vending machines that would raise prices in warm weather. Had it managed to control publicity and present its work as leading to machines that would lower prices in cold weather, it might have avoided the entire controversy.”
We know how to explain the public’s reaction to the Coca Cola announcement using behavioral economics — the way it was presented (or framed), customers take the lower price as the “reference price,” and the price increase seems unfair, whereas the Odlyzko’s suggested framing would anchor the higher price as the reference price. Of course, just because we can explain how the fairness heuristic works doesn’t make it logical or consistent, let alone properly grounded in social justice.
More generally, every aspect of our mental price fairness assessment heuristic seems similarly vulnerable to hijacking by tweaking the presentation of the transaction without changing the essence of price discrimination. Companies have of course gotten wise to this; there’s even academic literature on it. One of the techniques proposed in this paper is “reference group signaling” — getting a customer to change the set of other customers to whom they mentally compare themselves. 
The perception of fairness, then, can be more properly called the illusion of fairness.
The fragility of the fairness heuristic becomes less surprising considering that we apparently share it with other primates. This hilarious clip from a TED talk shows a capuchin monkey reacting poorly, to put it mildly, to differential treatment in a monkey-commerce setting (although the jury may still be out on the significance of this experiment). If our reaction to pricing schemes is partly or largely due to brain circuitry that evolved millions of years ago, we shouldn’t expect it to fare well when faced with the complexities of modern business.
Given that the prime impediment to pervasive online price discrimination is a moral principle that is fickle and easily circumventable, one can expect that companies to do exactly that, since they can reap most of the benefits of price discrimination without the negative PR. Indeed, it is my belief that more covert price discrimination is going on than is generally recognized, and that it is accelerating due to some technological developments.
This is a problem because price discrimination does raise ethical concerns, and these concerns are every bit as significant when it is covert.  However, since it is much less transparent, there’s less of an opportunity for public debate.
There are two directions in which I want to take this series of articles from this point: first a look at how new technology is enabling powerful forms of tailoring and covert price discrimination, and second, a discussion of what can be done to make price discrimination more transparent and how to have an informed policy discussion about its benefits and dangers.
 I had the pleasure of sitting next to Professor Odlyzko at a conference dinner once, and I expressed my admiration of the prescience of his article. He replied that he’d worked it all out in his head circa 1996 but took a few years to put it down on paper. I could only stare at him wordlessly.
 I’m struck by the similarities between price fairness perceptions and privacy perceptions. The aforementioned 2004 price fairness framework can be seen as serving a roughly analogous function to contextual integrity, which is (in part) a theory of consumer privacy expectations. Both these theories are the result of “reverse engineering,” if you will, of the complex mental models in their respective domains using empirical behavioral evidence. Continuing the analogy, privacy expectations are also fragile, highly susceptible to framing, and liable to be exploited by companies. Acquisti and Grossklags, among others, have done some excellent empirical work on this.
 In fact, crude ways of making customers reveal their price sensitivity lead to a much higher social cost than overt price discrimination. I will take this up in more detail in a future post.
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.
This is the first in a series of articles that will show how we’re at a turning point in the history of price discrimination and discuss the consequences. This article presents numerous examples of traditional price discrimination that you see today, many of which are funny, sad, or downright devious.
Price discrimination, more euphemistically known as differential pricing and dynamic pricing, exploits the fact that in any transaction each customer has a different “willingness to pay.”
What is “willingness to pay,” and how does the seller determine it? To illustrate, let me quote a hilarious story by Steve Blank on selling enterprise software. The protagonist is one Sandy Kurtzig.
Since it was the first non-IBM enterprise software on IBM mainframes, [when] she got her first potential order, she didn’t know how to price it. It must have been back in the mid-’70s. She’s [with] this buyer, has a P.O. on his desk, negotiating pricing with Sandy.
So, Sandy said she goes into the buyer who says, “How much is it?”
And Sandy gulped and picked the biggest number she thought anybody would ever rationally pay. And said, “$75,000″. And she said all the buyer did was write down $75,000.
And she realized, shit, she left money on the table. … And she said, “Per year.”
And the buyer wrote down, “Per year.”
And she went, oh, crap what else? She said, “There’s maintenance.”
He said, “How much?”
“25 percent per year.”
And he said, “That’s too much.”
She said, “15 percent.”
And he said, “OK.”
Sadly, not all transactions are as much fun as pricing enterprise software ;-) The price usually has to be determined without meeting the buyer face to face. There are three types of price discrimination based on how the price is determined:
- Each buyer is charged a custom price. (Traditionally, there has never been enough data to do this.)
- Price depends on an attribute of the buyer such as age or gender.
- Different price for different categories of buyers, with the seller somehow getting the buyer to reveal which category they fall into. As we’ll see, hilarity frequently ensues.
Additionally, each buyer may be sold the same product, or it could be customized to each segment—in the extreme case, to each buyer. This is called product differentiation.
Alright. Time to dive into some examples.
1. Student discounts at movies, museums, etc. are one of the simplest types of price discrimination. Students are generally poorer and more price sensitive, so the business hopes to attract more of them by making it cheaper.
Why museums and movies, and not say grocery stores? Two reasons: first, if the grocery store tried it, they’d quickly run into the problem of resale by the group that qualifies for the lower price. (It could manifest as parents sending their kids to get groceries.) The museum doesn’t have this problem because they ask for a student ID.
Second, grocery stores set prices pretty close to their marginal cost anyway, so there’s not as much of a scope for variable pricing. With museums, on the other hand, it costs them next to nothing to admit an extra visitor. All of their costs are fixed costs.
2. Ladies’ night at bars is another simple example of price discrimination based on an attribute (gender). Rather than women having a lower willingness to pay, it is perhaps more accurate to say that men are more desperate to get in :-)
Interestingly, this is one of the few examples whose legality is questionable. Wikipedia has a good survey. Also, it is not a “pure” example since the point of ladies’ night is not just to get more women through the door but also, indirectly, to get more men through the door.
3. A less obvious example is the variation of gas prices (and other commodities) within the same chain across locations. This is because people in richer ZIP codes are willing to pay more on average.
An important caveat: some of the variation is typically explainable by differences in marginal cost (such as rent) between different locations, but not all of it.
4. Financial aid at universities is a rather complex case of price discrimination. Instead of charging different rates to different students, the seller has a base rate and gives discounts (aid) to qualifying students.
You can see aid programs in humanitarian/political terms or in economic terms; the two paradigms are not in conflict with each other. In the economic view, students with higher scores receive aid because they have more college options and are therefore more price-sensitive. Poorer students and minorities receive aid because they are less able/willing to pay.
In the examples so far, the attribute(s) that factor into discrimination are either obvious (gender, race, location) or it is in the buyer’s interest to disclose them to the seller (student status, financial need). Now let’s look at examples where the seller has to be crafty in getting the buyer to disclose it.
The same principle applies to numerous other product categories like wine and coffee. But at least you’re getting at least a nominally superior product for a higher price. Let’s look at examples where buyers voluntarily pay more for the same product.
6. Dell.com used to ask customers if they were home users, small businesses, or other categories. The prices for the same products varied according to the category you declared. There was no legally binding reason to be honest about your disclosure, and no enforcement mechanism.
Now for a more devious example.
7. “Staples brazenly sends out different office supply catalogs with different prices to the same customers. The price-sensitive buyers know which to buy from. The inattentive ones pay extra.” [source]
A similar example: restaurants with long menus sometimes highlight some popular choices on the first page. The same items are available in the long-form menu for cheaper, if only you knew where they’re buried.
These examples illustrate an extremely common form of price discrimination:
This theme is so fundamental that it has been practiced for thousands of years in the form of haggling.
8. The jumping-through-hoops principle suggests that it makes economic sense for the seller to make discounts hard to get. Nowhere is this more apparent than with Black Friday deals—stand in ridiculously long lines all night to get fabulous discounts. Wealthier customers who don’t bother doing so will get much less of a discount during regular store hours, even on Black Friday.
9. More examples of hard-to-get discounts: woot.com, mailing-list deals and Southwest Airlines DING. Many of these involve artificial scarcity and time-limitations to make them more difficult to get, thus ensuring that those who take advantage are buyers who might otherwise not buy at all.
10. Perhaps the most extreme example of roping in buyers who might otherwise not buy is deliberately crippling your own product, known in economics as damaged goods.
IBM did this with its popular LaserPrinter by adding chips that slowed down the printing to about half the speed of the regular printer. The slowed printer sold for about half the price, under the IBM LaserPrinter E name.
It is not because of the few thousand francs which would have to be spent to put a roof over the third-class carriages or to upholster the third-class seats that some company or other has open carriages with wooden benches. What the company is trying to do is to prevent the passengers who can pay the second class fare from traveling third class; it hits the poor, not because it wants to hurt them, but to frighten the rich. And it is again for the same reason that the companies, having proved almost cruel to the third-class passengers and mean to the second-class ones, become lavish in dealing with first-class passengers. Having refused the poor what is necessary, they give the rich what is superfluous.
These examples should make clear that:
11. There are endless examples of clever tricks to learn the customer’s price-sensitivity in the airline industry. The price for the same seat can vary greatly depending on a variety of factors. The most well-known one is that you get lower prices if your trip spans a weekend, because it probably means you’re not a business traveler.
12. First class and business class seating on airlines is also price discrimination, but of a very different kind. Here it’s not different prices for the same product but different prices for slightly different products. Buyers segment themselves due to product differentiation, a phenomenon we’ve seen before with cars.
The first class/economy price spread can often be as high as 10x, which illustrates the wide range of customers’ willingness to pay. For a variety of reasons, most other markets haven’t managed to attain such a high price spread.
Aaaaaand we’re done with the examples!
Note that this is far from a complete list—I haven’t covered clearance sales, loyalty programs and frequent flyer miles, hi-lo pricing, drug prices that vary by country, and so forth, but I hope I’ve convinced you that price discrimination in some form already happens in nearly every market.
But here’s the kicker: I’ve deliberately left out what I consider the most important class of examples, because I’m going to devote a whole article to it. I will argue that this emerging form of price discrimination is going to explode in popularity and dwarf anything we’ve seen so far. Feel free to guess what I’m thinking about in the comments, and stay tuned!
Many thanks to Justin Brickell, Alejandro Molnar and Adam Bossy for useful discussions and comments. Thanks also to my Twitter followers for putting up with my ‘tweetathon’ on this topic two months ago and providing feedback.