Check the costs for a flight to Melbourne online today. It costs $300. You leave your browser open. Two hours later it's $320. Half a day later $280. Welcome to the world of algorithmic pricingHere the technology tries to search out out what price you might be willing to pay.
Artificial intelligence (AI) is quietly changing the best way corporations set prices. Not only do prices change with demand (dynamic pricing), but corporations are also increasingly adapting prices to individual customers (personalized pricing).
This change isn't just technical – it raises big questions on fairness, transparency and regulation.
How different pricing models work
Dynamic pricing responds to the market and has been used for years Travel and retail Websites.
Algorithms track supply, demand, timing and competitive pricing. When demand peaks, prices rise for everybody. When it wears off, they fall. Think of Uber's rising fares, skyrocketing airline tickets during school holidays or hotel prices during major events. This sort of variable pricing is now commonplace.
Personalized pricing goes even further. AI uses personal data – your browsing history, your purchasing habits, your device and even your zip code – to predict your willingness to pay. The price varies depending on the person. Some call this “surveillance pricing.”
Two people the identical product at the identical time may even see different prices. A one who at all times abandons the cart might get a reduction, while someone who rarely purchases might get the next price.
A study by the European Parliament defines personalized pricing as “price differentiation for concurrently equivalent services or products based on information a retailer has a few potential customer.”
While dynamic pricing will depend on the market, personalized pricing will depend on the person consumer.
It began with airfares
This change began with the aviation industry. Since deregulation within the Nineties airlines have used “Yield Management” to alter fares based on the variety of seats remaining or proximity to a booking’s departure date.
More recently, airlines are combining this with personalization. They depend on shopping behavior, social media context, device type and former browsing history – all to create rate offers unique to you.
Hotels followed. A hotel might increase its base rate but offer a special “members only” discount to someone who has stayed there before, or offer a price cut to someone who stays on a booking site. In hotel revenue management Pricing strategies enable corporations to focus on different customer segments with different advantages (e.g. leisure or business travelers).
AI improves this process by making it possible automated integration large amounts of customer data into individual pricing.
Jakub Porzycki/NurPhoto via Getty Images
Now the trend is spreading. E-commerce platforms like Booking.com routinely test personalized discounts depending in your profile. Ridesharing appsFood campaigns, digital subscriptions – the reach may be large.
How AI-driven personalized pricing works
At their core, such systems analyze loads of data. Every click, time spent on a web site, previous purchases, abandoned carts, location, device type, browser path – all of this flows right into a profile. Machine learning models predict your “willingness to pay.” Based on these predictions, the system selects a price that maximizes revenue while hoping to not lose the sale.
Some platforms go even further. At Booking.com, Teams used modeling to pick which users should receive a special offer while staying inside budget constraints. This resulted in a 162% increase in sales while limiting promoting costs for the platform.
Therefore, you could not see a regular price. You may even see a price designed for you.
The risk is consumer backlash
Of course, the personalized pricing strategy carries risks.
First: fairness. If two households in the identical suburb pay different rent or mortgage rates, it seems arbitrary. Pricing that uses income proxies (e.g. device type or zip code) could entrench inequality. Algorithms can (even unintentionally) discriminate against certain population groups.
Second: alienation. Consumers often feel cheated after they later find a less expensive price. Once trust is lost, customers may turn away or attempt to game the system (delete cookies, browse incognito mode, switch devices).
Third: accountability. Transparency is currently low; Companies rarely disclose using personalized pricing. If AI sets a price that violates consumer law by being misleading or discriminatory, who’s liable – the corporate or the algorithm developer?
What the regulators say
In Australia, the Australian Competition and Consumer Commission (ACCC) is taking notice. A five-year investigation
The study, published in June 2025, highlighted algorithmic transparency, unfair trading practices and consumer harm as key issues.
The commission said:
Current laws are inadequate and regulatory reform is urgently needed.
It really useful stronger oversight of digital platforms, economy-wide unfair trading rules and mechanisms to force disclosure of algorithms.
Is this efficient or scary?
We're entering a world where your price can differ from mine – even in real time. This can lead to increased efficiency, latest types of loyalty rewards or targeted discounts. But it may possibly also feel Orwellian, unfair or exploitative.
The challenge for corporations is to implement AI pricing in an ethical and transparent manner, and in a way that customers can trust. The challenge for regulators is to catch up. The ACCC's actions suggest Australia is moving on this direction, but many legal, technical and philosophical questions remain.
