In October 2016 the US Federal Communications Commission (FCC), then under Democratic majority, imposed a set of privacy rules on Internet service providers (ISPs), requiring them to get opt-in consent from consumers before using, sharing, or selling their Web browsing data, app usage history and other private information. On April 3 2017, President Donald Trump signed a repeal of these rules, following actions taken by both houses of the US Congress. ISPs are thus free to continue using their customers’ surfing data without seeking their prior consent. What does it imply for consumers?
More personalized prices
ISPs (and the firms to which they sell data) use primarily our Web browsing history to deliver personalized advertisements (Athey (2016) nicely summarizes the ins and outs of improved ad targeting). Our focus here is on another important, though less apparent, use of customers’ data, namely targeted prices. Shiller (2014) provides empirical evidence that Web browsing data gives firms more information on consumer willingness to pay compared to “old school” demographic data. Similarly, a report of McKinsey&Company (2016) shows that finer data analytics has improved customer segmentation and firm’s profitability.
Clearly, if a seller acquires a better knowledge of its consumers, it is in a position to charge prices that come closer to the maximum price that each consumer would accept to pay. Hence, in the case of price targeting, a more widespread use of Web data is likely should hurt consumers, especially when a seller faces no competition. However, the increased availability of data challenges this intuition in two important ways: first, digital technologies also enable customers to protect their privacy; second, when competing firms access detailed customer information, they may target the same consumers with personalized prices.
Our recent research contributes to shed some light on these two issues. Our starting point is that firms are able to identify any consumer’s willingness to pay, but only imperfectly: there is always a positive probability that any particular consumer will remain anonymous. Firms then charge a personalized price to consumers they are able to “profile”, while charging a “uniform” price to all consumers who remain “anonymous”.
The hidden cost of hiding
In Belleflamme and Vergote (2016), we analyze a situation where a monopoly seller is able to (imperfectly) profile consumers, while consumers may decide to protect their privacy (by, e.g., clearing cookies from their browsers, adopting proxy servers, or even polluting their Web history). One expects that such move would make consumers better off. However, this intuition proves incorrect: adding insult to injury, privacy-protecting strategies may decrease the collective well-being of consumers even further. The reason is that when consumers can hide, the seller reacts by raising the uniform price of its product. Why? Because a higher uniform price discourages hiding, and because consumers who decide to hide are identified as consumers with a high willingness to pay (who can thus be charged a higher price). As a result, what some consumers gain by protecting their privacy is often more than offset by what the other consumers lose by paying a higher price.
Consumer profiling may relax price competition
According to the previous analysis, President Trump’s recent decision would inevitably hurt consumers. Competition among sellers should, however, be brought into the picture, as we do Belleflamme, Lam and Vergote (2017). Take two identical sellers producing the same product and having both the possibility to profile consumers with some probability. In particular, they are assumed to have correlated and potentially different abilities to profile consumers: the firm with the better technology can profile any consumer that the firm with the inferior technology can profile, but the reverse is not true. This assumption makes perfect sense if, e.g., both firms obtained Web history data from the same ISP, but differ in their expertise in applying differential pricing solutions.
Such different and imperfect profiling technologies allow firms to regain market power (which would be nil were profiling not possible). This is because consumer profiling enables differential pricing, but its imperfect nature makes firms uncertain about the competitor’s pricing strategy, which triggers strategic random pricing. As a result, there may be three simultaneous explanations as to why two consumers end up paying different prices: firms may charge them either different personalized prizes (differential pricing), or randomized uniform prices (price dispersion), or randomized personalized prices (differential pricing and price dispersion).
Our second analysis suggests that making privacy rules less protective (as just decided in the US) has ambiguous impacts on consumers: on the one hand, some consumers will pay higher prices because firms can profile them more easily; but on the other hand, some consumers who used to be anonymous will now be profiled and among them, those with a low valuation will start purchasing and will thus enjoy a larger surplus. Yet, we also demonstrate that all consumers should be better off if firms had the obligation to make their uniform prices public. Finally, we also show that exclusivity contracts offered by data brokers do not necessarily harm consumers. Indeed, in our model exclusivity leads to more and not to less competition.
In sum, our research suggests that, globally, consumers have reasons to worry when more lenient Web browsing privacy rules allow firms to refine differential pricing strategies (even if some consumers may be better off when profiling improves). Consumers may then take privacy in their own hands by obfuscating their presence on the Web, but the cure may be worse than the disease because firms are likely to react by raising their prices.