Manitoba Moves Against Retailers Charging Different Prices for the Same Goods

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Manitoba Moves Against Retailers Charging Different Prices for the Same Goods

The bill would outlaw using personal data to determine what customers pay—a first in Canada

Vass Bednar writes sharp, accessible articles about how markets actually work—who benefits, who doesn’t, and why competition laws (when they exist) shape everyday life more than we realize. She has a knack for making boring topics—like interprovincial trade—surprising and urgent, and unlike a lot of policy experts, she isn’t afraid to let her frustration show (“How fit for purpose is government labour data today? Not very. The data sucks”).

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Bednar is also managing director at a think tank called the Canadian Shield Institute, where she and her team spend a lot of time thinking about how to defend our economic sovereignty in an era of shifting global power (their newsletter is well worth subscribing to).

What ties all of this together is Bednar’s instinct for where regulatory debates are heading before they break into the open. And so it was interesting to see, weeks after her article for us on “algorithmic” or personalized pricing, the Manitoba government propose legislation banning retailers from using customers’ personal data to charge them different prices for the same goods.

I wanted to get Bednar’s thoughts on Manitoba’s move and if this signals a shift in how governments are starting to understand the hidden mechanics of digital markets.

Tell me why this is a big deal. Provincial governments introduce legislation all the time.

Manitoba is the first government in Canada to recognize and address the potential issues with pricing algorithms that use your data against you. A few US states have started moving, but this is a wide-open policy space in Canada. Manitoba is setting the terms of this debate early, instead of waiting until personalized pricing is everywhere and politically untouchable.

Normalization is a real risk here. Once firms get consumers used to being sorted, profiled, and priced differently, the practice starts to feel inevitable. But it is not. It is a choice about what kind of business practices we expect. Personalized algorithmic pricing pulls together affordability, privacy, competition, consumer protection, and data extraction all at once. It asks whether companies should be allowed to use increasingly intimate signals about our behaviour to decide what we see, what we pay, and what discounts we never even knew existed.

In a cost-of-living crisis, that really matters. Because the affordability crisis is also about whether companies are building systems designed to squeeze each consumer a little harder.

Explain algorithmic pricing, and explain why it concerns you so much.

Personalized algorithmic pricing is essentially the use of algorithms that change prices based on information about consumers or markets; it can be on an individualized level, and the changes can happen on a minute-by-minute basis. The potential is that companies may move away from fixed, knowable sticker prices that consumers use for comparison shopping and toward individualized prices that use data about you to try to extract the highest possible price.

The use of pricing algorithms isn’t new. What’s newer is just how much personal data and information these companies are able to access, and advancements in AI and machine learning have made it possible to extract and analyze it at massive scales.

How common is dynamic or algorithmic pricing in retail today compared with industries like airlines and hotels?

Consumers already know dynamic pricing from airlines, hotels, and rideshare. But retail is where it gets murkier. The US Federal Trade Commission found that companies selling these pricing tools were working with at least 250 clients, including grocery stores and online marketplaces, which shows how widely this model is spreading beyond travel. So, the core concern is the normalization of opaque, algorithmic pricing across everyday consumer markets.

What kinds of data signals are companies actually using today to adjust prices—location, browsing history, past purchases, device type, or something else? Can algorithmic systems infer traits like income level or neighbourhood wealth?

Location, browser and device data, language settings, past purchases, and browsing history are all part of the picture. But increasingly, firms can also track real-time behaviour; things like how long you linger on a page, what you click on, even the items you abandon in a cart. They combine those signals with outside data from brokers and other commercial platforms to build detailed consumer profiles. That lets them infer things like purchasing intent, financial sensitivity, or neighbourhood wealth and use those inferences to shape the price you see, the discounts you get, or the offers you never see at all.

The FTC has confirmed that detailed personal and behavioural data are being used in these systems. What is harder to prove case-by-case is the most invasive edge of it (for example, whether firms are inferring payday timing or emotional vulnerability) but those concerns flow directly from the capabilities these systems already have.

Why prioritize this policy now?

This is landing now because affordability is already raw. Canadians feel nickelled-and-dimed from every direction, and algorithmic price discrimination adds another layer of opacity to that experience. It is not just that so many things cost more; it is that people can no longer tell why they are seeing a certain price, whether someone else is being offered something different, or whether the market is being calibrated against their perceived vulnerability.

It also reflects a broader unease about how much of everyday life is now mediated by systems we cannot meaningfully see or contest. Pricing is becoming part of a bigger digital sovereignty question: Who governs the algorithms shaping our economic lives, on whose terms, and with whose interests in mind? Once people realize these systems can sort, profile, and price them in ways they do not understand, concern moves quickly from consumer frustration to a deeper question of control. If we can’t govern the behaviour of Amazon or Uber or Hotels.com when they’re selling to Manitobans, then what does that say about the state of our sovereignty?

Is personalized pricing actually happening in Manitoba today, or is the government legislating against a hypothetical problem?

I would not call this a hypothetical problem. It’s not made up or theoretical. We know personalized pricing is a real problem thanks to the proactive work of the US states I mentioned. Manitoba is being proactive in getting ahead of a potential issue, which is what we should ask more of our governments to do.

The Competition Bureau here in Canada has looked at it in a couple of different contexts, but they haven’t taken further action. The question is whether governments should wait for a fully documented local scandal before acting.

So no, Manitoba is not performatively legislating against an imaginary threat.

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In a related question, what evidence do we have that Canadian retailers are currently using personal data to charge different consumers different prices?

We don’t yet have a Canadian smoking gun showing exactly which retailers are using personal data to charge different people different prices for the same item. But we do know three important things: first, Canadian authorities are already worried enough about algorithmic pricing to study it; second, the tools and data practices needed to do this are well documented; and third, adjacent evidence from housing and online grocery shows this is not some far-off conjecture. In other words, the burden is no longer on consumers to prove the capability exists. It does. The policy question is how much of it is already happening here and why we are only finding out after the fact.

Do we even have the technical expertise to audit algorithmic pricing systems used by major retailers or platforms?

Yes, we can audit these systems. The myth that algorithms are too technical to inspect mostly serves the firms deploying them. Canada’s real problem is courage: giving regulators the mandate and access rights to look under the hood.

Why introduce a ban without specifying penalties or enforcement mechanisms? Who exactly will enforce the rule if retailers ignore it?

It makes sense to start with a clear prohibition. The bill is drawing a line around a practice the government sees as being discriminatory, even if the enforcement architecture is not fully built out yet. In this case, Manitoba believes that charging people different prices based on their personal data is unacceptable. Eventually, we expect to see this backed by clear penalties and a named enforcement body. This approach sets the tone for future policy action and creates a foundation the province can build on later.

Some have pushed back. Josh Dehaas on X said, “If some people pay more because they’re willing to pay more, that benefits the rest of us. The most inefficient thing a business can do is charge everyone the same price.” What’s your answer to that?

My answer is that this gives algorithmic pricing far too much credit. Businesses are not charging some people more in order to generously give the rest of us a courtesy discount. They are using these tools to squeeze as much value from each consumer as possible. That is the point. These systems are explicitly designed and marketed to help firms find the maximum price a given person is willing to pay (it’s not about ability to pay).

In practice, algorithmic pricing is unlikely to lower the price of something you were already going to buy anyway. More often, it is used to trigger extra spending: to push a discount when it might prompt an impulse purchase or to steer you toward buying more than you planned.

So the effect is the opposite of a textbook-tidy redistribution that benefits everyone: it is the erosion of consumer surplus and goes against the principle of competition in capitalism. More of the value that used to stay with the consumer is captured by the company instead.

What do you want to happen next?

A national strategy on personalized algorithmic pricing. We also need to update Canada’s privacy framework. When we talk about personalized algorithmic pricing, we’re often talking about the end result of a much bigger problem: companies are able to collect, infer, and act on massive amounts of information about us with very few meaningful limits.

And at a minimum, there should be mandated transparency requirements. If companies operating in Canada are using algorithmic personalized pricing, they should have to disclose it clearly. Consumers should know when prices are being tailored, on what basis, and with what kind of oversight. Right now, too much of this happens in a black box.

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