Apr 3, 2018 - data he leaves on the Net when surfing, buying, looking ⦠hence describing his live and dreams .... strange new type of privacy violation.
2018
Personalized pricing
A fashionable method of profit’s optimization which may become dangerous!
Personalized pricing is now a very classical method of profit optimization for many firms with a power of market. Some also argue that it may be in the interest of the consumer. Nevertheless, combine with artificial intelligence and big data, it may be a very dangerous technique. By the way, questions surge about the legality of the technique in regard to the consumers protection laws. By hugely offending the sense of fairness of many people, personalized pricing may also be violently rejected.
Jean-François ROUGÉ
Cercle Interdisciplinaire de Réflexion stratégique https://www.linkedin.com/groups/13562797 4/3/2018
Sommaire Key words: ...................................................................................................................................... 2 Table of illustrations ....................................................................................................................... 2 Introduction ..................................................................................................................................... 3 1. Personalized pricing: An efficient way to maximize firm’s profits ........................................ 5 1.1 The traditional approach of personalized pricing ................................................................. 6 1.2 The revolution Big Data & of AI in personalized pricing .................................................... 7 2. Personalized pricing: A dangerous way to consolidate sustainable profits ........................... 10 2.1
Risks of outraging the laws ............................................................................................ 10
2.2
Risk of damaging the customer’s relationship ............................................................... 13
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Conclusion ............................................................................................................................. 15
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References ............................................................................................................................. 16
Key words: Pricing Personalized pricing Profit optimization Risk Willingness to pay Price fairness
Table of illustrations Figure 1: Price discrimination methods .......................................................................................... 3 Figure 2 Precondition to be able to discriminate prices .................................................................. 4 Figure 3: Repartition du surplus de production with personalized pricing ..................................... 7 Figure 4: Impact of big data on Personalized Price Discrimination ............................................... 8 Figure 5 Personalized pricing and potential law infringement ..................................................... 11
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Introduction As mainstream economy postulates the market as dominated by pure and perfect competition, most economists still consider price discrimination1 as a “market failure”. Nevertheless, after the seminal work of Baumol & Bradford2 in 1970, the book of Phlips (1981) and the paper of Nayle (1984), it is obvious that price discrimination is a fundamental of any industrial strategy. Since, the first scientific paper about this topic3, price management has become “the third business skill uncomfortably positioned between art and science”4. The fact is that the imagination of decisions makers (and marketers) is fertile finding original ways to discriminate prices5, as exemplified below:
coupons personnalized pricing
time based pricing
sliding scale fees
direct segmentation
premium product version
indirecte segmentation
first degree discrimination
loyalty pricing incentive discounts
Figure 1: Price discrimination methods
The mater is critical. This diversity has to be explained regarding the stakes of pricing for firms: 1
Price discrimination may be defined as strategies that aim to improve revenues of the firm by charging a higher price for their products to the less price sensitive customers. 2
BAUMOL & BRADFORD (1970), Optimal departure from marginal cost pricing, American Economic Review, N°60, pp 265-283 3 Machlup (1955) 4 At least according to Bouter (2013) 5 Varian (1989)
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“It’s hard to overstate the importance of getting pricing right. On average, a 1 percent price increase translates into an 8.7 percent increase in operating profits (assuming no loss of volume, of course). Yet we estimate that up to 30 percent of the thousands of pricing decisions companies make every year fail to deliver the best price. That’s a lot of lost revenue.”6 In a market of intense competition, very few firms are supposed to be able to become “price maker” and be given the opportunity to discriminate their prices. Strict preconditions theoretically limit the availability of such price discriminations:
Preconditions to be able to discriminate prices Getting a sufficient market power
Be able to identify different market segments • Price elasticity of demand
Be able to limit customer's arbitrages • Prevent resale • prevent customer switshing
Figure 2 Precondition to be able to discriminate prices
Nevertheless, fulfilling those conditions is not so uncommon in most of the economic sectors. First, apart from the GAFA, it is quite easy, mainly when the capitalistic intensity of the activity is high, to find examples of firms dominating their market. Second, even modest firms are able to identify market segment in order to create a niche market. On the other hand, identifying the price elasticity of the demand is not such a big deal; even for somebody who never heard about this notion. Market dealers used to do that instinctively every Sunday! Then, the most difficult precondition to fulfill in a world of Hypercompetition, even for a powerful firm, is the third condition: preventing customers’ arbitrages. Whenever those conditions are completed, price discrimination is a powerful mean to maximize the firm’s surplus; mainly if the first-degree price discrimination is available. And it is exactly what the combination of big data and artificial intelligence permit. “In the online world, our anonymity and ability to identify a single competitive price are becoming a thing of the past. Virtual competition heralds the age of personalization with its benefits and possible pitfall.”7 Hence the utilization of personalized pricing is rising not without huge risks. First, it may hurt the law. Actually, more and more regulations are created in the developed countries to protect both the integrity of the market (competition & antitrust laws) and the customers. Second, and 6 7
Backer, Kiewell & Winkler (2014) Ezrachi & Stucke (2017)
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not the least, personalized pricing is fundamentally unfair: “some people pay more just because of who they are”8 and may hurt customers inducing violent reactions.
1. Personalized pricing: An efficient way to maximize firm’s profits “Discriminatory prices will be required for an optimal allocation of resources in real life situation.” Philps (1981,1)
As Philps insists, the real word is a world of prices discriminations. By the way, being positives or negatives, discriminations are something that seems quite natural and very human9, to sociologists and anthropologists. Since they appeared, some profession is pricing discriminatorily: For example, barristers and practitioners. Those professionals are hence pay through “emoluments”, in French “honoraires” 10 that, even if contemporary laws may prohibit this practice, used to be fixed according to the capacity11 and the willingness12 of the client to pay. More generally, price discrimination may be linked to the fact that whatever the field of activity; production costs may differ substantially according many criteria: geography; time; scarcity, intensity of the competition, taxes’ level… and justify economically a different pricing for a same good or service. Traditional economic theory distinguishes three type of price discrimination: “First degree price discrimination involves charging every individual customer a price based on their individual willingness to pay. Second-degree price discrimination does not charge based on customer characteristics, but based on the amount of the good purchased, e.g. quantity discounts. Third degree price discrimination relies on putting customers into groups and charging different rates based on willingness to pay within those groups, e.g. senior discounts at the movies13.”14. Many contemporary authors used to complete this list. They add a fourth category: selling to customer at a unique price, but cost of providing the service or the product to different customers is
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Krugman (2000) Becker (1971) first tested the economics effects of discriminations in 1955. 10 In France, this term is important because it means that the client/patient, « honor » the professional for its services; he does not “pay” him, which will be considered as crude, plebeien… 11 Often today, some doctors or lawyers work for free when their clients are unable to pay. 12 The importance of the rewarding is often proportional to the benefit of the help received… 13 Most of the advocacies in favor of personal pricing are factually sustained not by an analysis of the real situation of first-degree price discrimination, but on the true advantages for some groups of the third-degree price discrimination. At least it is a regrettable confusion; more probably, it should be a kind of manipulation! 14 Ozimec (2013) 9
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different; or even a fifth category: the hurdle method of price discrimination: The seller offers a lower price coupled with an inconvenience the consumer may prefer to avoid… Only the first-degree price discrimination will interest us. Therefore, the following section will begin reminding the classical approach of personalized pricing. Then the second one will enter the core of our topic: the impact of big data and AI, on the capability of firms to improve their ability to use it.
1.1 The traditional approach of personalized pricing
While the term “personalized pricing” is the most successful to describe the situation at the very core of this analyze, economists also use two other locutions to describe this situation. “Firstdegree price discrimination, also sometimes known as perfect price discrimination, involves making price per unit of output depend on the identity of purchaser and on the number of units purchased.”15 … More generally, the availability of “perfect price discrimination is based on the ability of the producer to identify the willingness to pay, of each of its client. It was traditionally considered as impossible except in very few situations mainly of oligopoly.
The third definition should particularly be noted: “perfect price discrimination”, considering the fact, that if nothing stops the practice, this will be the one best way to maximize the profit of the producer. Why? Because it allows charging each and every consumer his own reservation price until this reservation price16 equals the marginal cost. Then, the firm able to do that may extract a “an even higher abnormal profit that the monopolist who charge a uniform price. (…) The policy of first degree price discrimination allows the monopolist to convert all of the consumer surplus that exist in the non-discriminating case, into producer surplus and to eliminate deadweight loses”17 … Very far away from perfect theoretical world18, of the pure and perfect competition: a sub-optimal, situation consisting in pricing according to the marginal cost of production. A producer’s paradise enlightened by the difference in the area of the green zone (profit) in each situation19:
15
Lipczynski, Wilson & Goddard (2009, 321) This is the maximum price a consumer is willing to pay for a good or a service. 17 Lipczynski, Wilson & Goddard (2009, 324) 18 In fact in the very world of the classical and neo-classical theory. 19 The curve on the left represent the “situation of pure and perfect competition”; the ones on the right (source : http://econowaugh.blogspot.bg/2014/11/monopoly-8-price-discrimination.html) highlight the difference of revenue between a “single” monopoly situation (1) and perfect pricing (2) 16
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Figure 3: Repartition du surplus de production with personalized pricing
No further comments are necessary… Personalized pricing is the perfect tools to optimize the bottom line of any firm that may afford it. One huge problem remains until now: it is very difficult to implement. To be effective, personalized pricing requires the effective measurement of consumer preference, of its ability and, at the time of the sale, of its real willingness to pay. In most case all those indispensable information were mostly unaffordable even for the wealthiest companies. There, the “producer paradise”, used to became a nightmare or a utopia! Then two technical revolutions: the big data and the artificial intelligence (AI); mostly if combined may permit to reopen the hope to access the paradise of personalized pricing.
1.2 The revolution Big Data & of AI in personalized pricing
According to Louis-David Benayer: “Value of data is not due to their rarity, but to their abundance. Data need to be reused.”20 So are doing big data and artificial intelligence. Once limited by the accessibility of primary data, due to the progress of Information and Communication Technologies, the world is now flooded under data about consumers privacy, tests, sensitivities and curiosities21… Manly, tanks to the GAFA that collect most of them and kindly resale that information to everybody able to pay! The customer, who does not pay its services on the Internet, becomes the good. Or, at least the data he leaves on the Net when surfing, buying, looking … hence describing his live and dreams 20 21
Cited in Clayet (2018) ; 2/4 Artun & Levin (2015).
to everyone in position to catch and analyze them. “Sellers are now using big data and digital technology to explore consumer demand, to steer consumers toward particular products, to create targeted advertising and marketing offers, and in a more limited and experimental fashion, to set personalized prices”22 Whereas first-degree price discrimination has long been considered as somewhat utopist; nowadays, big data and the forthcoming revolution of Artificial Intelligence are actually making first-degree price discrimination more and more technically affordable; at least for some firms! ; And nothing will slow down or stop the process …but the Law23. As of now, it is possible to consider that bid data allows a very fine intrusions in the psychology of the consumer24, that transform both the face of the competition on the targeted market and accurate the potential to design truly personalized pricing (even if most firms deny vigorously to use them): Near perfect price discrimination
Big data
Targeting "the right emotonal pitch"
Shadow perception of the "maket price"
Increase overall consumtion
Harder for consumer to know the price of the market
Reinforce price discrimination Figure 4: Impact of big data on Personalized Price Discrimination25
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Slaughter & May in Ezrachi & Stucke (2017) Even Mark Zuckerberg, implicitly recognized during its auditions (April 2018, 11&12) in from of the US congress, that nothing but law will restraint the use of personal data by technological firms. On April 12, we also learn that Facebook is now the company which spends the most money in lobbying in the European Commission... 24 « Sentiment analysis has its hands in numerous areas. For instance, sentiment analysis can be performed on social media to determine sentiment of customers on a trending topic. (…) Marketers can obtain information from blogs, reviews, social media posts, to nanalyse customer attitude toward a particular topic or product » https://www.linkedin.com/pulse/5-reason-why-you-must-care-customer-sentiment-anaysis-naven-joshi/ Artun & Levin (2015) 23
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Inspired by Ezrachi & Stucke (2016)
Obviously, as powerful big data may be, it will be much more efficient to combine it with AI. The combination of both techniques allows, with “very few” resources to permanently scrutinize customer’s change in behavior and price sensitivity and then to learn… then pricing accordingly, in real time. So “The secret to increasing profit margins is to harness big data to find the best price at the product—not category—level, rather than drown in the numbers flood.”26 Great! At least as long as you are the producers.
* * * This section demonstrated how, technological innovation, through big data combined with artificial intelligence, transformed personalized pricing from an economist’s utopia to an affordable tool allowing some producer to monopolize the integrality of the rent of production27. On a technical point of view, nothing may slow down or stop the use of those techniques to increase the bottom line of price making firms; certainly not the greed of financial capitalism28! According to Paul Krugman (2000), “the only thing that is likely to stop it is government action”. But given the economic and social dangers that generalized personal pricing may constitute; it seems to be a very dangerous way to consolidate sustainable profits.
26
Backer, Kiewell & Winkler (2014) Walker (2015) 28 Stiglitz (2013) ; Généreux (2016) 27
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2. Personalized pricing: A dangerous way to consolidate sustainable profits Despite it obvious advantages on the bottom line, personalized pricing represent risks in the middle and long term. Actually, those dangers may be analyzed based on two main ideas: -
Personalized pricing are practiced by firms with a (huge) market power. This one may be too important in regard of the regulation. Then the risk is to outrage competition or consumer laws that aim to keep the fairness of the market.
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Personalized pricing are fundamentally unfair29. So, they hurt the sense of fairness of consumers30 who may react violently31, jeopardizing both the reputation and the sales of the firm.
Let the following paragraphs examine those two points.
2.1 Risks of outraging the laws
The extreme advantage that personalized pricing may give to the producer32 go far beyond the “simple” information asymmetry described, in 1970, by Akerlof. Now, not only the producer is the only one knowing everything on the product it sells, but also he may knows everything (or so) on the consumer it sells to. Hence expunging the latest residual randomness of the transaction to extract the maximum of profit… Ultimately reinforcing the unsustainability of the current repartition of the revenue33. Generally speaking, the law is far from being perfect. Worst, since a long time: the beginning of the 1980’? It is often manipulated34 and seems to be biased to foster specific interests35. Anyhow, at least in the advanced democracies, it endeavors to protect, among other things, first the integrity of the market and second, the populations against the abuses of this market. And by the fact, more and more regulations actually do it. Then, without pretending to the completeness, let
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Krugman (2000) Encarnacion, Gomez-Borja & Mondéjar-Jiménez (2013) for an extansive definition of price fairness 31 Valentino & Devier, (2012) 32 Remind that this producer is a price maker in position of domination (monopolistic or oligopolistic) 33 Even if it is not our main preocupation here, that very problem may be the source of new regulations that may concern our topic; for instance in the purpose of « moralizing » the economic life. 34 Rougé (2017), especially « The cynical game of many global enterprises », pp 25-30. 35 Généreux (2016), Reich (2007) 30
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us try to draw up a scheme that enlightens the main sources violation of law that personalize pricing may induce:
Figure 5 Personalized pricing and potential law infringement
A- Personalized pricing & laws relative to protection of the market Whatever its’ actual relevance, the dogma of free market is THE pillar of the economy at the present time. As such, it deserves the protection of the law. Unsurprisingly, many regulations around the world, national or even international36, aim to protect its core concept: the free competition. Then appear problems concerning personalized pricing. As seen below, their core area of profitability is based on the power of market. The legal impacts of price discrimination have been researched and documented both by economists37 and by lawyers38. On a technical point of view, they may resort of three penal qualifications: The violation of antitrust (concentration) law39; the abuse of their power of market40; and the conspiracies about price fixing. Those possible incriminations have already been documented, but the third one. The possible link between “price fixing conspiracy” and “personalized pricing” it is far from evident and, as long as we know it, has not been studied. We have not the place to do it here, and will just ask the question and let it for further researches41.
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Mostly the regulation of the World Trade Organization & European Union ; but also the one of OECD which is particularly up to date in fiscal matters. 37 Connor (2008) 38 Especially competition lawyers. 39 Woodkock (2018) 40 Wodkcock (2017) 41 In the particular case of personalized pricing, it may, for instance, result of a voluntary retention of information; on the quality and completion of information sold between monopolists…
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If it is obvious that personalized pricing methods may infringe the rules protecting a market of free competition, they also ask questions relative to the protection of the consumer. B- Personalized pricing & laws relative to the protection of the consumers Although mainstream economy considers the consumer as a “hommo oeconomicus”; somebody to be considered philosophically and economically on the same base of equality that a global firm, law is more pragmatic and recognize the huge difference between the two. So it grants him its protection. In fact, most countries putted in place a juridical arsenal concerning consumers’ protection. This legal shield begins with the importance of the information given by the professional during the negotiations (that may become an obligation to give a clever advise related to the true needs of the consumer), to finish with obligations of warranty (which may last a decade). Between those to “limits” the particular point of the price and its eventual fairness42 is central. Let us bet, that if spreading like it may be anticipated, personalized pricing will become a huge source of litigations. In some cases, it may become a societal question: for instance when they concern the personalized risk-based-pricing: customer may be priced or even exclude, out of the market for factors they cannot control, or ignore in good faith43. By the way, the importance of the matter is such that, when Amazon has been busted, in 2000, “testing the technique” under the presidency of M. Obama, the White House published a special report44! Following the scandal of “Cambridge Analytica”, the auditions by the American congress, on April 2018, 11 & 12, of Mark Zuckerberg, putted in light, in case of need, the many ways big data, then personalized pricing, may infringe privacy laws. At least, civil laws used to protect the weaker citizens; disabled first; but not only. This civil protection may also protect the weakest hence entering in convergence with the consumer law notion of “abuse of consumer weakness”. 42
Glielissen & Graafland (2008)
43
Mary Bleiberg & Judith West at Brooking university : «Big data could also allow companies to uncover medical conditions that a consumer might not even know about themselves, and higher prices in this case could constitute a strange new type of privacy violation. The worst-case scenario with big data pricing, however, would involve the treatment of protected classes. Federal law does not allow companies to treat people differently based on race, religion, and several other characteristics but big data pricing could inadvertently lead to higher prices for certain groups in a way that would violate American values and laws.» 44
A 2015 report from the White House Office of Economic Advisers found that companies are presently experimenting with three broad pricing strategies: (1) experiments that randomly manipulate prices to learn about demand; (2) efforts to steer consumers towards particular products without altering their prices; and (3) using big data to customize prices to individual buyers. Research on the prevalence of these pricing practices suggests that experiments and steering are common on some web sites, while cases of personalized pricing remain limited.
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Once again, it is clear that personalized pricing present many ways of breaking laws concerning the protection of the consumers. Very few things about the legal future of personalized pricing are sure, but the fact that this technique may infringe severely legislations concerning as market that people protection. Nevertheless, in the short term, one of the main risks incurred with the use personalized pricing concern the integrity of the customers’ relationship.
2.2 Risk of damaging the customer’s relationship
According to Monroe (2003), it is natural for consumers to compare price. Hence, there is very few chances that personalized price discriminations stay secret for a long time; especially with the development of comparison tools. So, « From our point of view, there are two main risks to be further discussed and analyzed. First, even though the economic effect of personalized dynamic pricing has theoretically been proven, it has to be questioned whether personal pricing is meaningful to customers or not. Second, provided that an estimation of the willingness to pay in real time would be technically possible, it is still questionable if the short-term increase in revenue outweighs the long-term risk of deteriorating the customer relationship. »45 Let us remember that inherently, personalized pricing is unfair46. Hence, even if some economist may justify its utility for (some) consumers, most of them are worrying about the damage they may cause to firms ‘reputation or client fidelity. The first occurrence of personalized price discrimination occurred in the 2000’s when Amazon, used it fantastic base of personal data to “randomly test” the willingness to pay videotapes of some of its client. It constituted the first scandal about personalized pricing. “Even if Amazon insisted that “the price differentials where random; a way of testing the market” and reimburse most of the clients for this differential, the warm was done.” 47: clients where “infuriated”. This situation seems to answer the first argument of Mohammed (2017): personalized pricing is meaningful to customers; they may be very sensitive to them. If nothing sustain the price differentiation, even a very small difference: like to serve Champagne or a better meal in business class of short flights; the unfairness of charging different prices for exactly the same product or service is badly experienced by customers. It may causes “emotions of anger and indignation”48. “Setting prices according to individual valuations, however, generates adverse consumer reaction unless consumers are invited to participate in the price-formation process. Consumer perceptions of price fairness are key to the sustainability of any discriminatory 45
Mohamed (2017) Krugman (2000); Reavey & Suri (2015), Glielissen & Graafland (2008) 47 Valentino & Devier, (2012): 48 Monroe & Xia (2006) 46
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pricing regime”49. In fact perception of value and perception of fairness are strongly interrelated50. As a result to fight what is considered as unfair personalized pricing, two strategies may be highlighted: the first is the development of a new business: the price comparators; the second is a violent consumers’ reaction: the boycott. -
The development of price comparators and the huge advertising campaign they used to make in the media is certainly a factor which reinforces customer’s aversion to personalized pricing. When someone sees every day, on each of its screens and radio stations, that the chamber or the airline ticket he just bought 100 is available at 72 or below; how not to feel fooled and swindled? Hence: how long will the wealthiest customers tolerate the situation in the case of a perfectly standard product or service? Very clever will be the one who will give the answer!
-
The first Amazon’s personalized price scandal putted in light the tendency of consumers to take revenge when fooled: first many of them decided to boycott the firm and a bit of them launch smear campaigns. The recent « Cambridge Analytica » scandal is here to remind it. Exploiting personal data of hundred of thousand net surfers, the firms is supposed to have influenced the willingness to vote of it targets. In the day following the scandal a great deal of Faceboock’s members, network from which those data where extracted, closed their account as a sanction for the lake of protection of their private data. Consequently to this scandal, the capitalization of the firm loosed 60 milliards of dollars in a few days51.
Both of those reactions have already proved to be very damaging for some companies and may spread in the future all the more since consumers are more and more informed and organized to fight producers’ abuses.
49
Richards, Liaukonyte & Streletskaya (2015): “Perceptions of price fairness, in turn, are hypothesized to be shaped by "self-interested inequity aversion" in which prices tend to be regarded as unfair, and purchase probabilities fall, if others are perceived to pay a lower price, while prices tend to be regarded as more fair, and consumers more likely to purchase, if inequity is in the buyers favor” 50 Martin & Rondan (2007) 51 Debes (2018)
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3 Conclusion
After remembering the general theory of price discrimination, this study enlightened the stakes at using personalized pricing combined with big data and artificial Intelligence to optimize the producers' profits. It concludes that the financial incentives of the practice are huge. Hence, the risk of a generalization of personalized pricing exists, in many areas of activities. At least, according to Richards & Alii "in order to be viable, any system of discriminatory pricing for consumer goods should invite consumers to have a stake in the price they pay." , in order to preserve the consumers' felling of price fairness. But no one can actually bet on this kind of wisdom. 52
Some authorized authors are reassuring: "After talking to researchers and others using big data The Council of Economic Advisers report says the use of first-degree price discrimination doesn’t seem to have caught on. Internet companies appear to have held off on personalizing prices for now. But the report does note that companies do seem to be using tests on websites to figure out the demand curves for different goods. While not exactly price discrimination, this process does provide companies with information on what prices the market can bare. So big-data-fueled price discrimination of the first degree doesn’t look like a reality in the present. But we are just at the beginning of the use of these kinds of large data sets for business use. In the meantime, as the authors suggest, policy may be better focused on enforcing existing regulations concerning privacy, consumer protection, and discrimination. Given the speed of technology, policy makers should keep an eye on this area." 53
Nevertheless, hearing Mark Zuckerberg confessing in from of the American Congress that the American legislator should probably take a larger concern about private data let us perplexed! As Krugman (2000) pointed out only governments may counter the greed of global firms. Europe did it in the beginning of 2018: what about the rest of the world?
52 53
Richards, Liaukonyte & Streletskaya (2015) https://www.weforum.org/agenda/2015/02/will-big-data-lead-to-price-discrimination/
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