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

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Contents Executive Summary 2 Introduction .. 3 I. The Economics of Differential P ricing .. 4 II. Big Data and Perso nalized Pricing . 8 What Sellers are Doing? .. 10 Exploring the Demand Curve .. 10 Steering and Third -Degree Discrimination .. 11 Behavioral Targeting and Personalized Pricing .. 12 What Buyers are Doing? . 13 E-Commerce . 13 Consumer Technolog y 14 Privacy Tools 15 III. Toward a Policy Framework . 16 Antidiscrimination .. 16 Consumer Protection 17 Conclusi ons . 19 References .. 20 1

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Executive Summary Big data refers to the ability to gather large volumes of data, often from multiple sources, and with it produce new kinds of observations, measurements and predictions. Commercial applications of big data deserve ongoing scrutiny given the speed at which both the technology and business practices are evolving. One of the many questions raised by big data is whether companies will use the information they harvest to more effectively charge different prices to different customers, a practice that economists call price discrimination . Economics suggests that many forms of differential pricing , such as senior citizen discounts at the box office or tiered pricing for air travel, can be good for both businesses and consumers. However, the combination of different ial pricing and big data raises concerns that some consumers can be made worse off, and have very little knowledge why. This report finds that many companies already use big data for targeted marketing, and some are experimenting with personalized pricing, though examples of personalized pricing remain fairly limited. While substantive concerns about differential pricing in the age of big data remain, many of them can be addressed by enforcing existing antidiscrimination, privacy , and consumer protection la ws. In addition, providing consumers with increased transparency into how companies use and trade their data would promote more competition and better informed consumer choice. 2

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Introduction Understanding the customer is a core principle of good marketing , and over time companies have developed a wide variety of tools for doing so . Th ese tools range from surveys to focus groups , reward s programs and the quarterly sales meeting . For many companies , big data and consumer analytics are an increasingly important part of this tool kit. In the marketing context, big data refers to the ability to gather large volumes of data, often from multiple sources, and use it to produce new kinds of observations, measurements and predictions about individual customers. Much of what companies learn through big data is used to desig n products and services that deliver more value to the individual consumer . At the same time, if sellers can accurately predict what a customer is willing to pay , they may set prices so as to capture much of th e value in a given transaction , especially when the y face little competition . This report considers the implications of big data and customer analytics for the American consumer, with a particular emphasis on how these tools might be used for differential pricing . The report strives to provide an economic perspective on th ese issues , focusing on the underlying technology, how it is used, the potential costs and benefits for both buyers and sellers , and the kinds of policies that can best promote efficiency , equity , and innovation in this space . Big data clearly hold s both promise and peril for the individual consumer . As the Executive Office of the President™s 2014 report Big Data: Seizing Opportunities, Preserving Values recently observed , fiIt is one thing for big data to segment consumers for marketing purposes, thereby providing more tailored opportunities to purchase goods and services. It is another, arguably far more serious, matter if this information comes to figure in decisions about a consumer™s eligibility for– employment, housing, health care, credit or ed ucation fl (Podesta et al. 2014). This report explores these issues, beginning with an overview of the economics of differential pricing . It goes on to describe how sellers are using big data to create personalized marketing campaig ns and pricing strategies, and how buyers are responding. The concluding section considers how big data and personalized pricing fit into our existing framework of antidiscrimination and consumer protection laws. 3

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I. The Economics of Differential Pricing Differential pricing , or what economists call fiprice discrimination,fl is the practice of charging customers differ ent prices for the same product. While this sounds unfair, many forms of differential pricing generate few objection s. For example , venues like movie theaters that charge a price of admission may offer discounts to particular groups, such as children, senior citizens , or members of the military. Business travelers often pay a higher price for the same plane ticket or hotel room if they purchase closer to the date of travel . And a variety of big -ticket items Œ products ranging from a new car to a university education Œ have a list price, but offer individualized discounts that vary from one customer to the next. In each of these examples, the seller ™s goal is to raise pri ces for those willing to pay more , without losing another group of more price -sensitive customers. That is the general idea behind differential pricing Œ to set prices based on demand , or what customers are willing to pay , rather than costs . Economics t extbooks 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 s econd -degree pric e discrimination , occur when the per -unit price falls with the amount purchased , as with popcorn at the m ovie 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 cost s of collect ing 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 encourage d 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 f inally, 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. When di fferential pricing is possible, e conomic theory suggests that it can produce both costs and bene fits. The main benefit is that when sellers have some market power, differential pricing allows them to expand the size of the market. For example, m atinee prices encourage large families and music lovers on a tight budget to take in a show. If a theater were prohibited from using this type of differential pricing , it might decide to keep prices high and leave some seats empty. This would mean less pro fit for the theater and fewer people getting entertained . 4

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Similarly, financial aid packages help universities bring in more tuition by charging the list price to those who can afford it , while educating more student s who might be excluded if need or merit -based financial aid were prohibited . These forms of differential pricing typically generate few objections because they appeal to customers™ sense of fairness Œ companies charg e a bit more to the least price -sensitive customers, who can proba bly afford it , and a lower price to those who cannot. On the other hand, one cost of differential pricing is that it can produce incentive s to inefficiently degrade product quality. Early railroad operators, for example, sought to increase profits by charging wealthy customers more for passage but did not have a good way of determining which customers were willing to pay more. As a response, some railroads chose to provide no roof on third -class carriages in order to increase the difference in quality (and price) between a first – and third -class ticket. The modern equivalent might be disabling features built into a car or a smartphone, or degrading the speed of an Internet connection, in order to create high and low -end versions of the same product (see box on versioning). In each case, sellers can profit at the expense of high -end customers by degrading the low -end product, even if it is costly to reduce overall quality . WHAT IS VERSIONING ? Firms often produce multiple versions of a product to encourage consumers to self -select into groups that pay different prices, even when it would be more cost efficient to sell a single design. For example, at one time IBM sold two versions of its LaserPrinter Series E, where the only difference between them was a chip that made the low -pric ed version print more slowly (Deneckere and McAffee 1996). While intentionally disabling some features of a product to facilitate price -discrimination seems perverse, it can nevertheless increase welfare for both firms and consumers if it allows the seller to reach a larger number of customers who would not otherwise be served. Versioning is especially prevalent with information goods such as books, films, or software because the costs of reproduction are typically small relative to the price. For these products, companies often release multiple versions over time (e.g. hardcover, paperback , and e-book) or add and remove features (e.g. bonus tracks or concurrent user limitations) as part of their product -line strategy. It is difficult to predict how big data will influence the prevalence of versioning. If it becomes easier to predict individual customers™ willingness to pay and charge different prices for an identical product, versioning may be replaced by personalized pricing. On the other hand, versioning has the benefit of reducing concerns about inequity that arise with personalized pricing, and big data may facilitate versioning strategies based on fimass customization,fl particularly for information goods that can be customized at relatively little incre mental cost. 5

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is what most economists mean by differential pricing . However, s eller s may also charge prices that reflect differences in the cost of servi ng different groups of buyers . This type of firisk -basedfl pric ing arises most commonly in insurance markets, where prices reflect the risk that an individual will experience the outcome covered by an insurance policy . Big data encourages risk -based pricing by enabling more fine -grained measurement of various risks, for example through tracking individual driving behaviors. 4 Risk -based pricing can improve economic efficiency by discouraging risky behavior, such as when individuals with a history of traffic accidents are charged more for auto insurance . It can also make insurance more widely available by reducing adverse s election, which occurs when only high -risk individuals enroll at a given (uniform ) price . At the same time , differential pricing in insurance markets can raise serious fairness concerns , particularly whe n major risk factors are outside an individual customer ™s control, with health insurance an obvious example. In general, risk -based pricing favors less risky customers, whereas value -based pricing favors those who are more price -sensitive. Nondiscrimination policies are one way to promote fairness for high -risk buyers . However, those policies can re -introduce adverse selection problem s unless they are accompanied by rules or subsidies that encourage low -risk buyers to remain in the market. 5 The remainder of this report is primarily focused on value -based pricing , which has different economic motivations and implications from risk -based pricin g. However, the impact of big data on risk -based pricing remains an important area for further discussion. 4 See, for example, fiSo You™re a Good Driver? Let™s Go To t he Monitor,fl Randall Stross, New York Times , November 24, 2012. 7

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II. Big Data and Personalized Pricing To understand how big data enables personalized pricing, it is useful to start with an overview of the technology. Computers have long been used to collect sales data, organize customer lists , and identify market segments. The features of big data that promise to make it more informative, however, are the increased scale of the underlying databases, the increasing variety of customer -level observations and measures, and the speed with which data are now harvested, traded , and deployed. Two broad trends are driv ing the increas ed applicatio n of big data to marketing and consumer analytics . The first trend is the widespread adoption of new information technolog y platforms , of which the Internet and the smartphone are the most important . These platforms provide access to a wide variety of applications such as search engines, maps, blogs, and music or video streaming services . The se new applications , in turn, create new ways for businesses to interact with consumers that produce new sources and types of data . For example, it is now possible to track : a user™ s location via mapping software their browser and search history whom and what they filikefl on social networks like Facebook the songs and videos they have streamed their retail purchase history the contents of their online reviews and blog posts . Sellers can also utilize these new types of information to make an educated guess about consumer characteristics. For example, some web sites use a computer™s Internet Protocol ( IP) address to guess the user™s location. 6 Others might use the items in a virtual shopping basket to infer a buyer™s gender , or th e history of web sites that a user has browsed to guess at their income or health status .7 To some extent, th is new ability to measure a consumer™s digital footprint depends upon a company™s relationship with that consumer. For example, w hen an Internet user visits a web site, the owner of the site may place a file called a ficookiefl onto the user™s computer , enabling the site to keep track of information about the user™s interactions with the site . Over time, cookies can be used to build a long -term picture of an individual™s Internet browsing history, and that information can be shared across sites (see box: What is a Cookie?) . However, it is considerably easie r to track customer behaviors on web sites or mobile applications that require user s to create an account and log into that account with each use . In addition to simplifying online tracking, account holders typically provide personal information that a sit e can use to link them with other external information sources. 6 IP is an acronym for Internet Protocol, and an IP address is the numeric identifier used to locate an individual computer on the Internet. 7 fiThe Web™s New Gold Mine: Your Secrets,fl Julia A ngwin, The Wall Street Journal . July 30, 2010. 8

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The second trend driving the application of big data to marketing is the growth of the ad -supported business model , and the creation of a secondary market in consumer information . Companies like Google and Facebook , both of which earn much of their revenue by selling targeted marketing opportunities , demonstrate the commercial potential of ad-supported Internet platform s.8 The ability to place ads that will be targeted to a specific audience based on their personal characteristics makes consumer information increasingly valuable to businesses. This has fostered a growing industry of data b rokers and information intermediaries that buy and sell custo mer lists and other data used by marketers to assemble a digital profile of individual consumers . Given sufficient data , sellers can try to predict how buyers will behave in response to different prices and pricing schemes . While r andomized experiments are one approach , n on-experimental tools and techniques for predict ing consumer behavior are also evolving rapidly . Predictive modeling is not a simple problem . However , companies have large incentives to refine these tools , since even small improvements can have a large impact on profitability , particularly for companies with a large customer base . For example, a 2014 recent study by Benjamin Shiller est imates the increase in profits if Netflix were to use behavioral data for personalized pricing . He finds that differential pricing based on demographic s (whereby Netflix would adjust prices based on a customer™s race, age, income, geographic location, and family size) could increase profit by 0.8 percent, while using 5,000 web browsing variables (such as the amount of time a user typically spends online or whether she has recently visited Wikipedia or IMDB) could increase profits by as much as 12.2 percent. 8 fiProgrammatic Bidding: Buy, Buy, Baby.fl The Economist . September 13, 2014. WHAT IS A COOKIE ? A cookie is a small text file that a web site can place on a user™s computer. Each time a user loads a particular web site, the cookie is sent to that site. This allows web sites to firememberfl certain information, such as what pages a user has already visited, or whether they are currently logged in to the site. Internet browsers generally allow users to set various permissions that control whether cookies are allowed on their computer. Cookies were created by programmers working for Netscape in 1994, and the name is a reference to fimagic cookiesfl Œ a term used to describe a piece of data that a program receives and then retransmits unchanged. Today, cookies are used for a wide variety of purposes, most notably tracking customers across multiple sites in order to send them behaviorally targeted advertising. Cookies are not regulated in the United States. However, in 2009 the European Union modified its e-Privacy directive to regulate cookies. In particular, the Directive told EU member states to pass laws requiring users to fiopt infl or provide consent before placing a cookie on their computer. 9

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The potentially large benefits of personalized pricing lead naturally to the question of whether many companies are actually engaging in the practice . The remainder of this section examines how both sellers and buyers are adapting to the rapid diffusion of big data in the context of personalized marketing. What Seller s are Doing ? Although a few studies have tried to detect differential pricing online , current know ledge is mainly anecdot al. Companies naturally protect information about pricing strategies for competitive reasons, and perhaps also for fear of a customer backlash. Nevertheless, the anecdotes suggest that we have not yet entered an era of widespread personalized pricing. Rather, sellers are using online and offline pricing practices that fall into three broad categories: (1) exploring the demand curve, (2) steering and differential pricing based on demographics , and (3) behavioral targeting and personalized pricing . Exploring the Demand C urve Experiments are a powerful way of learni ng about demand and consumer behavior, even in the absence of big data. As a consequence , marketers often conduct fiA/B Tests fl that randomly assign customers to one of two possible price conditions . These experiments are technically a form of differential pricing , since they result in different prices for different customers , even if they are finondiscriminatoryfl in the sense that all customers are equally likely to face the higher price . Offline businesses have long been able to explore the demand curve by testing prices in different stores or randomizing offers via direct mail. The Internet, h owever, provides a much better platform for running demand experiments quickly and effectively. For exa mple, a recent study identified hun dreds of thousands of fiseller experimentsfl on eBay, where an identical item was listed multiple times by the same seller at different prices or with different auction parameters , presumably t o learn how those variables influence demand for the underlying product (Einav et al 2011) . While such price experiments are common, they can still become controversial. For example, in 2000 users discovered that was conducting price tests and complained about paying different prices for the same DVD. Amazon ™s CEO Jeff Bezos apologized in a news release that same year indicat ing that the tests were random and promis ed that fiWe’ve never tested and we never will test prices based on customer demographics.fl 9 Even if sellers do not wish to randomize prices across potential buyers at a point in time, it is often possible to collect similar data by raising and lowering prices for all customers over very brief time intervals. For example , if customers who arrive a t a web site at 10 am face a lower price than those who arrive at 10:15 am, and buy correspondingly more of a given product , the seller has discovered valuable information about the demand curve without technically offering different prices to its customers . To provide an example indicating that this phenomenon 9 “Bezos Calls Amazon Experiment ‘a Mistake.'” Puget Sound Business Journal . September 28, 2000. 10

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