You check prices for a flight today. You leave your browser open. Two hours later, the price is increased and half a day later, it drops. Welcome to the world of algorithmic pricing, where technology constantly tries to figure out the maximum price you’re willing to pay.
Artificial intelligence (AI) is quietly remaking how companies set prices. Earlier, companies used dynamic pricing where not only do prices shift rapidly with general demand. However, firms are now shifting to personalised pricing where they are tailoring prices to individual customers.
This shift isn't just technical—it raises big questions about fairness, transparency, and regulation. In this article, Nitika Garg, Professor of Marketing at UNSW Sydney, explains how this mechanism works and why it poses a risk to internet users.
How different pricing models work
Dynamic pricing
Dynamic pricing reacts to the market and has been used for years on travel and retail websites. Algorithms track supply, demand, timing, and competitor prices. When demand peaks, prices rise for everyone; when it eases, they fall. Think of Uber’s surge fares, airline ticket jumps during school holidays, or hotel rates during major events. This variable pricing is now commonplace.
Personalised pricing
Personalised pricing goes further. AI uses personal data—your browsing history, purchase habits, device, and even postcode—to predict your individual willingness to pay. The final price, therefore, varies with the individual. Some call this “surveillance pricing”.
Two people looking at the same product at the same time might see different prices. For instance, a person who always abandons carts might receive an immediate discount, while someone who rarely shops might see a premium price.
A study by the European Parliament defines personalised pricing as “price differentiation for identical products or services at the same time based on information a trader holds about a potential customer”. Whereas dynamic pricing depends on the market, personalised pricing depends entirely on the individual consumer.
The Origin: Starting with airfares
This shift began with the airline industry. Since deregulation in the 1990s, airlines have used “yield management” to alter fares depending on how many seats are left or how close to the departure date a booking is made. More recently, airlines have combined yield management with personalisation. They draw on shopping behaviour, social media context, device type, and past browsing history—all to craft fare offers uniquely for you.
Hotels quickly followed suit. A hotel might raise its base rate but send a special "member only" discount to a repeat guest, or offer a price drop to someone lingering on a booking page. In hotel revenue management, pricing strategies enable companies to target distinct customer segments (such as leisure versus business travelers) with different benefits.
AI significantly enhances this process by enabling the automated integration of large amounts of customer data into individual pricing. Now, the trend is rapidly spreading. E-commerce platforms such as Booking.com routinely test personalized discounts based on a user's profile. Ride-share apps, grocery promotions, and digital subscription plans all demonstrate the broad reach of this technology.
How AI-driven personalisation works
At its core, such systems mine a tremendous amount of data. Every click, the time spent on a page, prior purchases, abandoned carts, location, device type, and browsing path—all feed into a user profile. Machine learning models then predict your "willingness to pay". Using these predictions, the system automatically selects a price that maximises revenue without losing the sale.
Some platforms go further. Teams at Booking.com, for example, used modeling to select which users should receive a special offer while meeting budget constraints. This drove a 162 percent increase in sales while limiting the platform's overall promotion cost.
In short, you might not be seeing a standard price; you might be seeing a price engineered specifically for you.
The risk: Consumer backlash and regulatory gaps
There are, of course, major risks to the personalised pricing strategy.
- Fairness: If two households in the same suburb pay vastly different prices for the same product, it seems arbitrary. Pricing that uses income proxies (such as device type or postcode) might entrench inequality. Algorithms may, even unintentionally, discriminate against certain demographics.
- Alienation: Consumers often feel cheated when they find a lower price later. Once trust is lost, customers might turn away or seek to game the system by clearing cookies, browsing in incognito mode, or switching devices.
- Accountability: Currently, transparency is low; firms rarely disclose the use of personalised pricing. If AI sets a price that breaches consumer law by being misleading or discriminatory, the question arises: who is ultimately liable—the firm or the algorithm designer?
What regulators are saying
(India TV input)
In India, the Guidelines for Prevention and Regulation of Dark Patterns, 2023 provides a framework to address deceptive practices such as false urgency, basket sneaking, and subscription traps. However, these guidelines do not explicitly mention personalised pricing.
Separately, the Digital Data Protection Act mandates that the data principal (the user) has the right to access information from the data fiduciary (the company) regarding what data is collected and the purpose of its collection, ensuring personal data is handled with respect and transparency.
Adding to this regulatory activity, the Central Consumer Protection Authority (CCPA) recently issued notices to tech giants and ride-hailing platforms over concerns regarding software performance and pricing inconsistencies, specifically citing instances where Android and iPhone users were shown different prices for the same product or service.
Despite these actions, there is still no explicit provision in Indian law directly regulating personalised pricing.
Efficiency vs Ethics
We are entering a world where your price might differ from mine, even in real time. That can unlock efficiency, new forms of loyalty pricing, or targeted discounts. However, it can also feel Orwellian, unfair, or exploitative.
The challenge for businesses is to deploy AI pricing ethically and transparently in ways customers can trust. The challenge for regulators is to catch up. The ACCC’s actions suggest Australia is moving in that direction, but many legal, technical, and philosophical questions remain.
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