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    Can a New Regulation Fix China’s ‘Big Data Backstabbing’ Problem?

    Users are tired of being exploited by digital platforms, but for all their recent aggressive posturing, regulators remain cautious.

    Earlier this month, just a day before the annual “Double Eleven” Nov. 11 shopping extravaganza, China’s State Administration for Market Supervision quietly dropped a bombshell on the country’s e-commerce industry. The draft “New Anti-Monopoly Guidelines for the Platform Economy,” not only confirms the applicability of basic anti-monopoly systems and analytical frameworks to e-commerce and digital platforms, it also takes aim at certain business practices that have raised regulator and consumer hackles — including the much-maligned phenomenon known as “big data backstabbing.”

    “Big data backstabbing” — dashuju shashu — refers to the increasingly common practice of using big data to compile user profiles, then leveraging that information to market the same product at various prices to different users, usually in the form of charging higher prices to older users. This behavior is hardly new or unique to China: As early as 2000, Amazon was offering DVDs at a lower price to new users more than older ones; in 2012, office supply store Staples came under fire for offering consumers different prices based on their unique IP addresses.

    But as big data technology has matured, China’s internet platforms have become increasingly cutthroat, trying to maximize their profit on every transaction. Unsurprisingly, consumers tend to be particularly sensitive to these practices. In March 2019, a survey published by the Beijing Consumer Association found that 88% of respondents thought big data backstabbing was widespread or common, while 57% said they had personally been “backstabbed.” Online, netizens joke that, “Old users are treated worse than dogs” and, “It’s the people who know you best who hurt you the most.”

    Despite its high-tech trappings, big data backstabbing really isn’t all that different from simple price discrimination or personalized pricing, whereby companies charge differing prices to different users for identical goods or services within the same time frame. In his classic work from 1920, “The Economics of Welfare,” British economist Arthur Pigou divided price discrimination into three degrees of severity, charging different consumers various prices for the same products was classified as the most serious type. Yet economists long agreed it was also the most difficult form to carry out, in part due to the need for detailed information on what different consumers might be willing to pay.

    The age of big data has changed all that. Large internet platforms possess both the will and technology needed to create increasingly accurate consumer profiles and pull off personalized pricing. Even compared with just 20 years ago, today’s big data backstabbing methods are far more precise than crude differential pricing systems that only distinguished between old and new users, or which were based on IP addresses.

    The “New Guidelines” represent China’s attempt to grapple with this phenomenon. Article 17 of the guidelines states that regulators may consider any usage of big data applied to establish differentiated pricing or other transaction terms according to users’ purchasing power, consumption preferences, usage habits, or anything else not judged to have a “substantial impact” on a transaction when determining if an act constitutes “discriminatory treatment.”

    The key words here are “may consider.” The guidelines are still being reviewed and revised, and in their current state provide only general guidance, rather than explicit terms. Regulators do not have any obligation to use Article 17 as a basis when determining whether a platform has engaged in discriminatory treatment. Even if they do decide to use it, the platform can find leeway in the “safe harbor clause” at the end of the article, which allows for discriminatory treatment under certain vaguely defined conditions, such as carve-outs for “trading habit and industry practice” or “promotions for first-time users in a reasonable time frame.”

    There is also a gray zone surrounding what constitutes identical transaction conditions, as well as overlap between what regulators can consider when checking for discriminatory practices and acts potentially protected under the safe harbor clause. For example, platforms cannot offer different prices based on consumers’ transaction histories or shopping preferences, but their vast troves of data make it easy for them to extrapolate a user’s credit status — which can then be used to justify discriminatory treatment under the “substantial impact” exemption. Users, meanwhile, have no way of knowing if a platform has provided them with a different price based on the former or the latter.

    This is all the more problematic within the current context of anti-monopoly law enforcement in China. There are only a handful of instances of an online retail platform being penalized by an anti-monopoly authority or successfully sued by a user — the main reasons being the insidiousness of these platforms’ monopolistic behavior, the revolving door between companies and regulatory bodies, and the prohibitively high burdens of proof imposed on consumers.

    Much of this can be attributed to the inherent complexity of regulating tech. Previous attempts to address big data backstabbing include 2019’s “E-Commerce Law,” which placed limits on the use of big data to set differential prices “based on characteristics such as consumers’ interests, habits, and more.” Last October, the Ministry of Culture and Tourism issued new regulations to curb the practice in the online travel industry, while a draft Personal Information Protection Act promises individual users greater control over their personal information in an effort to cut big data backstabbing at the source.

    At the same time, the vagueness of the “New Guidelines” underscores an important point: Counter to the raft of headlines about runaway regulation, Chinese regulators continue to prefer a prudent, “tolerant” approach to regulating the platform economy.

    One reason for this approach is that the exploitation of big data has become a key part of the economy in countries like China and the United States. Thus, while overly lax regulation is not conducive to consumer protection or fair competition, overzealous regulation would be detrimental to the competitiveness of the country’s internet industry. Article 17 thus provides regulators with a degree of flexibility: They can use it to punish cases of a particularly egregious nature and social impact, while at the same time excusing more benign practices such as group purchases, first-order promotions, and preferential treatment for new customers.

    China’s fast-growing platform economy has in many respects been a boon to the country: increasing economic efficiency and making consumers’ lives more convenient, even if it’s come at the cost of unethical practices like big data backstabbing. In this context, the “New Guidelines” are a meaningful attempt to rein in monopolistic and anti-competitive practices, but we can’t expect the guidelines to eradicate these behaviors on their own. That will take not only greater cooperation between different spheres of the law, but also regulators willing and able to keep up with new business models and technological advancements. Only then will consumers be able to stop watching their backs every time they make a purchase.

    Translator: Lewis Wright; editors: Cai Yineng and Kilian O’Donnell.

    (Header image: Fansatic Graphics/People Visual, re-edited by Sixth Tone)