Nowadays, with firms getting access to more and more consumer data, they are able to use this abundant data to access consumers’ willingness-to-pay (WTP) to price discriminate, and at the same time to offer personalized discounts. Moreover, the data also contributes hugely to the advertising area. Firms are capable to target particular consumers for personalized ads and marketing campaign programs. For example, if Greg is an avid hiker, then his browser could be occupied by the ads for hiking outfits and equipment, such as the cool brochure of Patagonia and selected coupons. In the current data ecosystem, consumers generate a lot of data when interacting with platforms—daily shopping website Amazon, social media platforms such as Facebook and Twitter, the search engine Google, or credit card companies. Some of these platforms may sell the data to third-party data integrators (i.e. data brokers). These data integrators collect and package the data, and send them to advertisers such as Patagonia in the above anecdote so that they could send potential consumers targeted offers.
In this ecosystem, one would ask, how much control a consumer should have over this entire process? Therefore, the motivation behind Ali and his coauthors’ work lies in the equilibrium implication of a consumer’s control of data. Just as Acquisti et al. (2016) emphasize in their survey paper on the economics of privacy— “Privacy is not the opposite of sharing—rather, it is control over sharing.”
The current privacy protection features grant consumers more control over their data disclosure. In the public policy and regulatory regime, the General Data Protection Regulation (GDPR), carried out by the European Union (EU) and the European Economic Area (EEA), has highlighted the importance of giving consumers more control over their personal data by the right of opting out. Similarly, from the firm practice setting, Apple, in its IOS 14 updates, specifically allows consumers to set the privacy setting on each app they download whether they “allow the app to track them across other apps”. We could imagine an alternative data ecosystem, where consumers have full control of the data. The firms are only able to send the individual personalized offer when he/she sends a verifiable message to the firm. To understand whether this alternative data ecosystem is better or not, the first-order importance is to model what consumer control means; and what implications it brings. This leads to the main research question of the paper— can consumers benefit from the combination of consumer control and personalized pricing? Can data be used to improve market efficiency without the additional surplus all going to data owners, brokers, and firms?
Figure: How consumer's control of data is enhanced in the alternative data ecosystem
Source: Nageeb's slide @ Luohan Webinar, Jul 2022
In the literature, it has been broadly questioned whether giving consumers control over data could indeed protect consumers and improve consumer welfare. A classical intuition from the late 70s(Grossman(1981), Milgrom (1981)) suggested the unraveling equilibria, where consumers could be self-defeating by the information they both disclosed and did not disclose. In other words, firms draw inferences from what you hide in a game-theoretic context. Ali and his coauthors’ model speaks contrary to this classical intuition. They suggest that granting consumers control under personalized pricing can be beneficial, in both monopolistic and competitive markets. This is because consumers can selectively disclose data to amplify competition, or even induce a monopolist to lower prices. Ultimately, welfare depends on the sophistication of the technology by which consumers control their data and the degree of market competitiveness.
More specifically, he proposed two ways to think about consumer control: a) simple evidence— consumers can reveal all or nothing (eg. GDPR’s track-or-do-not-track regulation); b) rich evidence (partial disclosure)— consumers can reveal some information without having to reveal them all. The brief results show that, in the monopolistic markets, simple evidence is ineffectual, but partial disclosure yields benefit to consumers; while in the competitive markets, both simple and rich evidence increase consumers’ welfare. Consider the model as a classical pricing problem, and for simplicity, imagine consumer’s value for a product is drawn from [0,1] with a uniformed distribution. In the monopolistic setting, the claim says, “with simple evidence, for every equilibrium, and for every type of consumer, consumers can never do better than the benchmark of uniformed pricing.” In other words, consumer control in the form of current popular track-or-do-not-track regulations does not benefit consumers in a monopolistic setting. Moving on, the claim says, “with rich evidence, no consumer type will be worse off relative to the uniformed pricing; while some consumer types are strictly better off.” In the competitive setting (here in the case of two firms with horizontal differentiation), the claim says, “a simple track-or-do-not-track technology can make consumers better off with competition and horizontal differentiation. While with more sophisticated evidence technology, consumers can do even better.”
The key idea of the work provides the answer to the research question we brought up at the beginning— when consumers can control the flow of information, personalized pricing along, even without the add-on welfare coming from customized products and services, can benefit consumers. This brings up interesting evidence that the price a firm charges is not strictly monotone in its beliefs. It also highlights the importance of technology solutions to enable voluntary (partial) disclosure in complementary to the regulations. This work contributes to the literature streams in competitive markets and targeting, privacy and dynamic choices, disclosure games, and information design.
At the end of the seminar, participants raised interesting questions that welcome future discussions and research work as well. For example, if we consider consumer disclosure choices in a dynamic way— if I expect a firm to do something in the future, how would I choose whether and what information to disclose so as to induce a discount? In another question, the platform’s role was brought up. In current industry practice, the platform might be doing these disclosure decisions on behalf of consumers. The distinction is within the concept of commitment. Consumers can “not commit” along the way. Therefore, it could be the case that platform can do even better with its ability to commit.
Paper: https://dl.acm.org/doi/abs/10.1145/3391403.3399457
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