In most retail environments, the price is on or at the product. 

In a growing number of online marketplaces, the price may be in your profile. 

The practice known as surveillance pricing or algorithmic personalized pricing uses personal data for each potential buyer to estimate how much they might be willing to pay for an item, then adjusts the price accordingly. It uses the purchasers’ own past buying habits, browsing behaviour, location, and other information to establish a unique price for each buyer. 

These price-setting methods have been primarily directed at online shoppers, but a similar outcome can be achieved by conventional retailers by using customized coupons or discount offers for in-store purchases. 

Policymakers and consumers express a general distaste for the practice when it is explained to them. One of the first acts of new NDP leader Avi Lewis was to initiate a motion to prohibit it, which failed to gain the unanimous consent necessary in Parliament for it to move forward. Manitoba has proposed amending its Business Practices Act to prevent retailers from using consumers’ personal data to increase the price of goods for a specific consumer.

Canadians have become accustomed to different forms of differential pricing: event ticket prices that vary based on the days of the week, ride-sharing rates increasing at peak times, loyalty and club member discounts, fluctuating online airline and hotel prices. 

But prices that respond to market conditions are not the same as prices that draw from an individual’s purchasing history, location or type of device used to transact. The premise of two different consumers purchasing the same product from the same retailer at the same time, but paying different prices just feels wrong. 

Research published earlier this year from Consumer Reports and Groundwork found that leading online grocery service Instacart offered significant pricing variations among shoppers purchasing identical items from identical stores at identical times, suggesting Instacart used technology to set personal prices. Researchers found the overall grocery basket prices varied by an average of about 7%. (With the resulting backlash, Instacart announced it would discontinue its price experimentation, but insisted it was testing different pricing algorithms, not implementing surveillance pricing.)

Canada’s Competition Bureau completed and published a consultation on algorithmic pricing earlier this year. The top issue from respondents was the potential for price discrimination based on these factors. Consumers wanted to know if the price they saw was the same for everyone. 

But in preparation for that, the Bureau described the issue as a “challenge for competition” and considered how the practice fit against current legislation that covered competitor collaborations, anti-competitive acts and deceptive marketing practices. If it were a clearcut violation of existing rules, one might expect the Bureau to take action, not float consultations. 

Practices from other jurisdictions suggest that if regulators and competition authorities can’t easily tackle the practice under current legislation, as an interim measure, they can mandate disclosure. Since 2019, the European Union has required disclosure of personalized pricing. Currently, 33 different states have laws requiring retailers to at least inform consumers when real-time data or artificial intelligence are used to help set prices. Some states have also introduced measures to protect consumers by limiting the use of personal information to set prices, as the Manitoba and federal NDP initiatives have attempted.