Imagine walking into your local supermarket to buy a two-litre bottle of milk. You pay $3, but the person ahead of you pays $3.50, and the next shopper pays only $2. While this might sound strange, it reflects a growing practice known as surveillance pricing, where companies use personal data and artificial intelligence (AI) to determine how much each customer should pay. It is a regular practice and we must comprehend the ins and outs since we are directly subjected to it.
What is surveillance pricing?
Surveillance pricing refers to the use of digital tracking and AI to set individualised prices based on consumer behaviour. By analysing a person’s online activity, shopping habits, and even technical details like their device or location, retailers estimate each customer’s “pain point”, the maximum amount they are likely to pay for a product or service.
A recent report from the U.S. Federal Trade Commission (FTC) highlighted that businesses can collect such information through website pixels, cookies, account registrations, or email sign-ups. These tools allow them to observe browsing time, clicks, scrolling speed, and even mouse movements. Together, these insights reveal how interested a shopper is in a product, how urgent their need may be, and how much they can be charged without hesitation.
Growing concerns about fairness
In mid-2024, Delta Air Lines disclosed that a small percentage of its domestic ticket pricing was already determined using AI, with plans to expand this method to more routes. The revelation led U.S. lawmakers to question whether customer data was being used to charge certain passengers higher fares. Although Delta stated that it does not use AI for “predatory or discriminatory” pricing, the issue drew attention to how such technology could reshape consumer costs.
Former FTC Chair Lina Khan has also warned that some businesses can predict each consumer’s willingness to pay by analysing their digital patterns. This ability, she said, could allow companies to push prices to the upper limit of what individuals can afford, often without their knowledge.
How does it work?
AI-driven pricing systems use vast amounts of data, including login details, purchase history, device type, and location to classify shoppers by “price sensitivity.” The software then tests different price levels to see which one yields the highest profit.
The FTC’s surveillance pricing study revealed several real-world examples of this practice:
- Encouraging hesitant users: A betting website might detect when a visitor is about to leave and display new offers to convince them to stay.
- Targeting new buyers: A car dealership might identify first-time buyers and offer them different financing options or deals.
- Detecting urgency: A parent choosing fast delivery for baby products may be deemed less price-sensitive and offered fewer discounts.
- Withholding offers from loyal customers: Regular shoppers might be excluded from promotions because the system expects them to buy anyway.
- Monitoring engagement: If a user watches a product video for longer, the system might interpret it as a sign they are willing to pay more.
Real-world examples and evidence
Ride-hailing platforms have long faced questions about this kind of data-driven pricing. In 2016, Uber’s former head of economic research noted that users with low battery life were more likely to accept surge pricing. A 2023 Belgian newspaper investigation later reported small differences in Uber fares depending on a phone’s battery level. Uber denied that battery status affects fares, saying its prices depend only on driver supply and ride demand.
Is this new?
The concept itself isn’t new. Dynamic pricing has existed for decades, but digital surveillance has made it far more sophisticated. In the early 2000s, Amazon experimented with varying prices for DVDs based on browsing data, sparking backlash from consumers who discovered the differences. Similarly, the UK’s Norwich Union once used satellite tracking for a “Pay As You Drive” car insurance model, which was discontinued after privacy concerns.
The future of pricing
Today’s combination of big data and AI allows retailers to create precise, individualised pricing models that adjust instantly. Experts warn this could undermine fair competition, reduce transparency, and widen inequality between consumers. Regulators like the FTC are now studying these systems closely to understand their impact on market fairness and consumer privacy.
For shoppers, awareness is key. Comparing prices across devices, clearing cookies, and using privacy tools can help reduce personal data tracking. As AI continues to shape how businesses price their products, understanding surveillance pricing is becoming essential to protect both privacy and pocket.