The end of the bargain? And should we worry?

Price elasticity. And vice versa.
Price elasticity. And vice versa.

In the run-up to an OECD Competition Committee roundtable discussion on 30 November 2016, Chris Pike of the OECD Directorate for Financial and Enterprise Affairs looks at the concerns and the opportunities created by the increased scope for personalised pricing in the digital economy.

We’ve all felt it – the rush you get when you find a great bargain at a price way less than you would happily have paid. But will these moments continue in the digital world as shopping moves online and the scope for firms to charge different prices to different customers increases?

In the era of big data and automated pricing algorithms, firms, using increasingly sophisticated analytical tools, can – with increasing accuracy – use huge volumes of information to model and predict our willingness to pay for a product. This helps them identify how much extra profit they might have extracted from any given transaction, and if they wish, to adjust and personalise their prices accordingly to extract that profit.

Reports suggest that more than 500 sellers on Amazon Marketplace have been identified as using algorithmic pricing and that at some retailers prices can differ by 10-30% for identical products based on the customer’s location, the device they’re using, proximity to a rival’s brick-and-mortar store (paywall) and characteristics including browser configuration.

An early example is Safeway, a grocery store with a mobile app that sends personalised promotional offers on specific products to shopper’s mobile devices as they walk around the store. These could be based on the current or forecast weather, products previously purchased, regularity and history of purchasing that product, complementary products purchased that day, and whether the location of their mobile device suggests they have already passed the aisle. Whether the customer uses the offer or not, the firm can use that information in its pricing algorithm to predict the discount required for like-minded consumers.

Moreover, this information might be added to in future, particularly as data is generated by the ‘internet of things’ (online devices in cars, kitchen devices, and health devices). For example, what were they doing prior to shopping? When did they last eat? Is there an appointment they are late for? Which rival stores do they use? Which rival stores are located on their route home? What’s in their calendar for the next week? What’s on their digital shopping list (or that of other family members)? Have they ordered takeaway that week? Do they have family visiting?

So this could be really bad news for consumers that would in any case buy the product; what if Amazon now realises that I’m willing to pay a frankly irrational amount of money for the new Hot Chip album? Are they seriously going to charge me 300 euro for it? Well, if they had an exclusive on it, then they might do. Would that be exploitative? Price gouging? Should competition agencies investigate? Maybe.

But, it could also mean they set a price of 3 euro to my dad who would never dream of paying 15 euro for it, since in the digital world reproduction is effectively costless so this is all profit. It would also increase the incentive for the band to make another album, which would be great, though at some point we might wonder whether this incentivises the right things.

Moreover, if I can get it elsewhere then the information that I value the product at 300 euro has little value. So big data should be less valuable in more competitive markets? Might its value even help us identify uncompetitive markets? Again, perhaps. But, if I can’t get it elsewhere, that is if they have market power, then what?

Well firstly we should recognise that consumers might not be defenceless, they might react by withholding information and services have been developed to provide anonymity (paywall). By making it more difficult for firms to estimate a consumer’s valuation these can disrupt discriminatory pricing schemes. Alternatively, consumers might start to demand compensation for providing the information.

And even if consumers are individually vulnerable, there might be small things we can do to help empower them. For example while personalised pricing is not yet widespread, a more common approach is to send personalised coupons. These change the effective price without changing the list price and this framing can make it more acceptable to consumers who would otherwise resent being charged personalised prices. The risk of offending a sense of fairness is perhaps key, and may lead to firms facing boycotts. This may explain the proposal that firms using personalised pricing schemes should have to declare transparently to consumers that they are doing so. They might for example be required to specify whether the personalised price is higher than average or the range of prices that they are charging for the product in question.

So, the effects of personalised pricing could go either way, and they could often benefit consumers, particularly those on small budgets, so we can’t go blundering in and prohibit all price discrimination. What to do then? Traditionally competition agencies would look at price discrimination where it was used to exclude a rival, and perhaps if it threatened to distort competition in a downstream market. They have been understandably reluctant to challenge price discrimination in final consumer markets. As a default position, a presumption that price discrimination is often competitive and good for consumers is entirely sensible. However, as this discrimination becomes near perfect in its execution, and the scale of potential harm to consumers increases, the risk calculus changes and so agencies and lawmakers may need to become more open to complaints and more active in requiring transparency on pricing policy.


This post is based on an OECD Secretariat paper on price discrimination

OECD Roundtable on Price Discrimination (30 November 2016)

Big Data: Bringing competition policy to the digital era

Data-driven innovation: Big data for growth and well-being

OECD Competition Committee Best Practice Roundtables

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