[Ezrachi and Stucke] argue that online behavioural discrimination will differ from the price discrimination we have seen in the retail world in three important respects:
- Big data allow the shift from third-degree, imperfect price discrimination to near perfect price discrimination;
- Sellers can use big data to target consumers with the right “emotional pitch” to increase overall consumption (the demand curve shifts to the right)
- As more online retailers personalise pricing and product offerings, it will be harder for consumers to discover a general market price and to assess their outside options, thus implying that behavioural discrimination becomes more durable.
“New dynamics that reduce our welfare? (…) Our article explores how e-commerce and the personalisation of our online environment can give rise to behavioural discrimination, a durable, more pernicious form of price discrimination.”
I. Near perfect price discrimination
Third-degree price discrimination, which involves the charging of different prices to different groups. The price can depend, among other things, on your location (i.e. where you live), your age, or your sex. Cinemas, bus services, and restaurants, for example, may charge adults higher prices than children, students or senior citizens.
By contrast, in this article, our focus is on the possible shift to perfect, or first-degree, price discrimination—where firms can identify and charge for each individual the most he or she is willing to pay, i.e. the reservation price.
“Big Data, learning by doing, and the scale of experiments come into play to better approximate your reservation price.”
“In this data-driven economy, the algorithm—to maximise profitability—will estimate the likelihood of our shopping elsewhere or being aware of better deals and accordingly provide us with a convincing sales pitch.” (e.g. coupons and promotion codes for customers more sensitive to outside options, i.e. more price-sensitive customers who are likeley to compare option, more sophisticated consumers. Naieve consumers can be exploited more efficiently).
II. Shifting the demand curve to the right
Sellers using our personal data to induce us to buy more products or services than
we otherwise would have purchased.
A few consumer biases, which firms may exploit to promote consumption:
- Use of decoys
- Price steering, e.g. On Orbitz, Mac Users Steered to Pricier Hotels
- Increasing complexity; facilitate consumer error or bias and manipulate consumer demand to their advantage (…) companies can, by designing the number and types of options they offer, better exploit consumers’ cognitive overload. In increasing complexity, the firms can also increase consumers’ search and switching costs, thereby reducing the visibility (and attraction) of outside options, and giving them more latitude to exploit consumers.
- Imperfect willpower “framing effects” (how the issue is worded or framed) do matter. Credit cards are one example. Here they cite a Dutch study The abolition of the No-discrimination Rule from 2000 (!) with N=150 consumers (!) surveyed. Dutch
merchants could impose surcharges or offer discounts based on how the customer was going to pay. Of the consumers surveyed, 74% thought it (very) bad if a merchant asked for a surcharge for using a credit card. But when asked about a merchant offering a cash discount, only 49% thought it (very) bad. A weak spot in an excellent paper.
The road to near-perfect behavioural discrimination will be paved with personalised coupons and promotions: the less price-sensitive online customers may not care as much if others are getting promotional codes, coupons, and so on, as long as the list price does not increase. (p.488)
Another way to frame behavioural discrimination in a palatable manner is to ascribe the pricing deviations to shifting market forces. Few people pay the same price for corporate stock. They accept that the pricing differences are responsive to market changes in supply and demand (dynamic pricing) rather than price discrimination (differential pricing). So once consumers accept that prices change rapidly (such as airfare, hotels, etc.), they have lower expectations of price uniformity among competitors. One hotel may be charging a higher price because of its supply of rooms (rather than discriminating against that particular user). (…) Thus, we may not know when pricing is dynamic, discriminatory, or both.
III. The durability of behavioural discrimination
it will be harder to know what others see. (…) As personalised offerings increase, search costs will also increase for consumers seeking to identify the “true” market price.
Behavioural discrimination—while not always possible—could occur more often than we expect. Furthermore, as we shift more of our activities to a controlled online ecosystem, it is likely to intensify.
The power to discriminate may be curtailed by possible pushback from consumers (I personally doubt it).
Price comparison websites may foster, rather than foil, behavioural discrimination and switching costs may be higher than one assumes, despite perceived competition being only a click away. (from the footnotes related to this quote: As more consumers rely (and trust) an intermediary to deliver the best results (whether relevant results to a search query or array of goods and services), the less interested they become in multi-homing—that is, from checking the availability of products and prices elsewhere. And: many users who indicated that when a search result is fails to meet their expectations they will “try to change the search query—not the search engine.”
IV. The welfare effects of behavioural discrimination
sellers can manipulate our environment to increase overall consumption, without necessarily increasing our welfare.
Once one accounts the consumer perspective, the social welfare perspective, and the limited likelihood of total welfare increasing, behavioural discrimination is likely a toxic combination. Moreover, behavioural discrimination may blur into actual discrimination due to the limits and costs of refined aggregation.
The worrying thing is that we (and the enforcers) may not even know that we are being discriminated against. Under the old competitive paradigm, one might suspect one was discriminated against if access was inexplicably denied (e.g. restaurants for “whites only”) or was charged a higher price based on this single variable. Under the new paradigm, users may not detect the small but statistically significant change in targeted advertisements (or advertised rates).
As pricing norms change, price and behavioural discrimination eventually may be accepted as the new normal. Just as we have accepted (or become resigned to) the quality degradation of air travel, and the rise of airline fees—from luggage to printing boarding passes—our future norms may well include online segmentation and price discrimination.
The costs can be significant. The new paradigm of behavioural discrimination affects not only our pocketbook but our social environment, trust in firms and the marketplace, personal autonomy, privacy and well-being.
Some other relevant links:
- The Economics of Big Data and Differential Pricing (White House, 2015)
- Work by Frederik Borgesius (UvA)
- OECD on digital economy
- The EU will examine banks’ use of customer data for profiling and marketing campaigns (2015)