Have you ever searched for a product online in the morning and gone back to look at it again in the evening only to find the price has changed? In which case you may have been subject to the retailer’s pricing algorithm.

Traditionally when deciding the price of a product, marketers consider its value to the buyer and how much similar products cost, and establish if potential buyers are sensitive to changes in price. But in today’s technologically driven marketplace, things have changed. Pricing algorithms are most often conducting these activities and setting the price of products within the digital environment. What’s more, these algorithms may effectively be colluding in a way that’s bad for consumers.

Originally, online shopping was hailed as a benefit to consumers because it allowed them to easily compare prices. The increase in competition this would cause (along with the growing number of retailers) would also force prices down. But what are known as revenue management pricing systems have allowed online retailers to use market data to predict demand and set prices accordingly to maximize profit.

These systems have been exceptionally popular within the hospitality and tourism industry, particularly because hotels have fixed costs, perishable inventory (food that needs to be eaten before it goes off), and fluctuating levels of demand. In most cases, revenue management systems allow hotels to quickly and accurately calculate ideal room rates using sophisticated algorithms, past performance data and current market data. Room rates can then be easily adjusted everywhere they’re advertised.

These revenue management systems have led to the term “dynamic pricing.” This refers to online providers’ ability to instantly alter the price of goods or services in response to the slightest shifts in supply and demand, whether it’s an unpopular product in a full warehouse or an Uber ride during a late-night surge. Accordingly, today’s consumers are becoming more comfortable with the idea that prices online can and do fluctuate, not just at sale time, but several times over the course of a single day.

However, new algorithmic pricing programs are becoming far more sophisticated than the original revenue management systems because of developments in artificial intelligence. Humans still played an important role in revenue management systems by analyzing the collected data and making the final decision about prices. But algorithmic pricing systems largely work by themselves.

In the same way that in-home voice assistants like Amazon Echo learn about their users over time and change the way they operate accordingly, algorithmic pricing programs learn through experience of the marketplace.