There are smarter ways that Canada can address surveillance pricing, writes Vass Bednar.Liam Richards/The Globe and Mail
Vass Bednar is the managing director of the Canadian Shield Institute and co-author of The Big Fix.
In 2008, on The Price Is Right, retired meteorologist Terry Kniess gave exactly the correct answer – right down to the dollar! – on two grand prizes. Everyone assumed he had cheated, but he said he hadn’t – instead, he memorized the show’s predictable set of prices.
That mind-blowing feat couldn’t happen today. Companies have made it almost impossible to know when the price is “right,” thanks to endless data collection that changes how much things cost across both time and place. Businesses now commonly use computational systems to track behaviour and traits to figure out what consumers might tolerate paying, to extract the maximum possible amount of money from them. This goes by many names: Algorithmic, personalized, surveillant, or even “snitch” pricing (though some economists see it as the holy grail of market efficiency, since everyone is paying what they are willing to pay). Some companies even claim, weakly, that this data collection helps them to deliver personalized and optimized discounts. But no matter what you call it, the result is discriminatory – even as this variance becomes a dominant feature of the modern economy.
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This all began decades ago with geographic consumer pricing, adjusting a product’s price using information about an area, such as income, shipping costs, and taxes. Today, digital marketplaces have supercharged this exploitative logic. Retailers and platforms increasingly have the technical ability to show different people different prices for the same goods, based on factors such as a person’s exact location, browsing behaviour, purchase history, device type, or even how frequently one redeems loyalty points.
Corporate pricing has become so fluid, so fast, that researchers haven’t really had a chance to explore when or why it occurs. The best indicators come from recent research that documents how Amazon, Walmart, and Instacart subject American customers to “pricing experiments” based on their data. There are also a handful of patents associated with the practice – a helpful reminder that some firms want to go so far as to make gouging consumers proprietary.
In the U.S., many states have proposed or adopted legislation to curb surveillant pricing, ranging from disclosure rules in New York and prohibitions on individualized price- and wage-setting in Colorado, to proposed restrictions on electronic shelf labels and surveillance pricing in Nebraska. But the regulatory playbook remains underdeveloped. Maryland, for instance, bungled a ban by allowing such pricing to occur if someone consents to it, and exempting promotional offers and temporary discounts. Under this structure, sellers could effectively create illusory higher prices that no one pays, and then “discount” every price from there.
In Canada, the policy debate has been blunt and binary: Ban it entirely, as the federal NDP wishes to do, or let ‘er rip. But there are smarter ways we could attack the problem.
Canada has weak federal private-sector privacy laws, and Ottawa could immediately amend PIPEDA to expressly include “unfair and deceptive business practices” like personalized pricing as an unreasonable purpose for data collection, even if the consumer provides consent.
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We can also call on governments to better enforce existing laws. Quebec’s Law 25 specifically defines profiling as the collection and use of personal information to assess characteristics such as economic situation, personal preferences, or behaviour. Ostensibly, the law prohibits personalized pricing without clear opt-in consent, though this hasn’t been rigorously enforced.
Consent-based privacy laws might not be enough, either, when simple data-anonymization tactics can take harmful pricing practices out of the scope of “personal information.” The complication is that privacy laws are meant to affirm and provide individual rights, but online pricing often harms collectively, by sorting consumers into commercially useful cohorts.
The Competition Bureau has angles on this, too. Section 74.05 of the Competition Act prohibits retailers from selling a product at a price higher than advertised, which could apply in situations where personalized pricing results in a higher price being advertised to someone. Section 54 prohibits double-ticketing, which makes it an offence for a retailer to charge a higher price between two or more prices clearly displayed in-store, though this needs to be updated for today’s online context.
Manitoba’s approach is encouraging, framing algorithmic discrimination as a consumer-protection issue whereby data-driven pricing – whether calibrated to the individual level or to a group from a specific postal code – counts as an unfair business practice.
We don’t need to reinvent the regulatory wheel with complex new laws, nor dignify gouging with the language of innovation. Just don’t buy the claims of companies saying that surveillance pricing is in consumers’ best interest. The price of that is too high.