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6 Safari File Edit View History Bookmarks Window Help 0 \"a? I1 84%-- Sun3212PM Q 9 EE O ('3 E v e I) # (5 :33 m + W16 10. LAST WORD Companies cannot use Big Data to set personalized prices with 100 percent precision. What will happen if personalized prices are set higher than customers' reservation prices? Will this outcome reduce the incentive to set the highest possible personalized prices? How can consumers protect themselves from personalized prices? "JO-@697? 1E$0 p Aa 6 LAST WORD W D Personalized Pricing The Potential Perils of Online Price Discrimination Internet retailers collect vast amounts of data about their customers. They know what you buy, who your friends are, what posts you like on Facebook, what websites you visit, andby connecting that information with other datahow old you are, where you live, your credit history, and so on. This huge collection of Big Data can be used by retailers with monopoly power to engage in an individually tailored form of price discrimination known as personalized pricing. As discussed in this chapter, price discrimination involves selling the same product to different buyers at different prices when the price differences are not justied by cost differences. Traditionally, price discrimination has operated at the group level with, for example, senior citizens getting discounts at restaurants and children under 12 getting discounts on movie tickets. Groups with relatively inelastic demand get charged higher prices while groups with relatively elastic demand get charged lower prices. The trick behind personalized pricing is that online retailers now have the ability to set individualized prices for most patrons. When that is possible, a rm with monopoly power can set the very lowest price for the person with the most elastic demand, a slightly higher price for the person with the next-most elastic demand, and so on all the way up to the highest price being presented to the person with the most inelastic demand. By proceeding in this manner, the monopoly seller can attempt to set a price for each individual that is just below his or her reservation price. Doing so allows the monopoly seller to squeeze out as much revenue as possible from each customer while still leaving him or her with a bit of consumer surplus (and thus with a motive to still buy the product). \"IO-(9%)? ZVI$0 'IIGMRIWWEEI'EWEU G\" E]? W I' Safari File Edit View History Bookmarks Window Help 4 o '5? 1 83%-r Sun3217PM Q 9 EE (3 . . 'i > E V e U _E Q prod,readerui.prod.mheducation.com [:1 333 a Content g Class Professor Marotti Economics in Connect m Regulated Monopoly + E p Aa e For an offline example of how personalized pricing works, think about car dealerships, where the nal price paid by a buyer is usually 4)) the result of a negotiation. In particular, keep in mind that a car salesman will negotiate much more aggressively if you drive up in a fancy BMW rather than an average Hyundai. The salesman will try to tailor the nal price to your perceived ability to pay. Big Data allows the same tactic to be used online. The more online retailers know about your buying habits, background, and preferences, the more they can attempt to set a price tailored to maximize how much they think they can get from you. But personalized pricing has its limits. Most importantly, very few rms actually have any substantial monopoly power. As just one example, car dealers have to compete with each other. So a dealership that is driving too hard at bargain will drive customers toward rival dealers. That competitive pressure is probably even stronger online, where, for many items, search engines and vast digital retail sites like eBay and Amazon often list multiple competing sellers. mg a no "3 W 6 Safari File Edit View History Bookmarks Window Help 4 v '6? 1 83%-' Sun3217PM Q 6 EE 0 . . E V e U _E H prod,readerui.prod,mheducation.com [:1 333 a Content :7 Class Professor Marotti Economics in Connect m Regulated Monopoly + E ,0 Aa e 7'5\" ' WC 0' V' 9 W \\\\ \\\\\\\\\\:\\\\"' v8\" '11)) A A'c Miss Ty/Shutterstock But even if a seller were to have monopoly power online, it would still confront another problem. Even with Big Data, it is hard for sellers to gure out what each potential buyer's reservation price is. So even if a rm with monopoly power wished to attempt personalized pricing, it would have diculty setting a customized price for each individual. On the other hand, online shoppers give up all their advantages if they fail to comparison shop. If they treat the rst seller that they deal with as the only seller, they are implicitly giving that seller monopoly power over themi In those situations, online merchants can run wild with personalized pricing. In 2014, for example, Allstate Insurance admitted to Wisconsin insurance regulators that its personalized pricing computer algorithms had presented essentially identical online customers with insurance prices that varied massively, in some cases by more than 800 percent. In many instances, the differences were driven by factors as trivial as one customer having a birthdate of January 12, 1968, and another having a birthdate of April 9, 1968. Allstate customers who failed to shop around left themselves open to paying much more than they had to. Online shopping is speedy and convenientbut it still pays to shop around. 1mm)? z$0 illwla7vme