Personalized coupons as a vehicle for perfect 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.