AI increase buyers inequality with individual prices generation

Artificial intelligence is gradually changing the very nature of pricing. More and more companies are moving from conventional dynamic prices to personalized ones – adapting the cost to each customer individually.

The traditional dynamic pricing model depends on demand – when it increases, prices increase, when it decreases – they decrease. This approach has been used for years by airlines, travel services and online stores. Algorithms monitor the market situation, delivery times and prices of competitors.

Personalized pricing works differently. AI analyzes personal data – browsing history, purchases, device, even zip code – and predicts how much a particular person is willing to pay. As a result, two users may see different prices for the same product at the same time. The one who buys often can be encouraged with a discount, while the other will be set a higher price by the system.

Unequal pricing?

The European Parliament defines it as “differentiating prices for identical goods or services based on potential customer data.” This approach originated in the airline industry, where companies began to vary prices based on how full a flight is or how close a departure date is. Now they combine this with user behavior analysis, social media data, and device data.

Hotels have adopted the same logic—they raise base rates but offer discounts to repeat customers or those who spend more time on the booking page. This is how they segment their audience into segments—tourists, business travelers, new guests, and loyal customers.

AI allows for the integration of large amounts of data into this process. Platforms like Booking.com are testing personalized discounts, and taxi services are testing dynamic rates based on previous trips. Each user action – click, time on the page, cancelled purchase – forms a “portrait of solvency”. The algorithm determines the optimal price that provides the highest profit.

For example, at Booking.com, modeling helped select users to whom special offers were sent according to the budget, which increased sales by 162%, while reducing advertising costs.

But such a strategy also has a dark side. The main problem is fairness. If two families from the same area receive different prices for rent or credit, this can be discrimination. Algorithms that take into account the type of device or index risk exacerbating social inequality.

In addition, buyers may feel cheated when they learn that others got the same product cheaper. In this case, companies usually reveal how their algorithms work.

Opacity of pricing algorithms – who is to blame?

Lawyers emphasize: if AI sets prices that violate consumer protection law, the question arises – who is responsible: the company or the algorithm developer?

The Australian Competition and Consumer Commission (ACCC) in a five-year study recognized that the opacity of algorithms and consumer harm are a serious threat. Regulators call for reforms: increased control over online platforms, the fight against manipulative trading practices and forced disclosure of the principles of AI in pricing.