Retailers, particularly in the hospitality sector are colluding to significantly raise prices online. Hotels typically dynamically adjust their pricing based on availability. However in recent years, they have shifted to using AI algorithms to adjust their pricing based on competitor information. As these algorithms monitor prices of competitors, a sort of bidding war cna be created to significantly inflate the prices creating non-competitive markets. Retailers are not looking to change their algorithms any time soon, so expect the customers to continue to be screwed.
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.
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.
The algorithms study the activity of online shops to learn the economic dynamics of the marketplace (how products are priced, normal consumption patterns, levels of supply and demand). But they can also unintentionally “talk” to other pricing programs by constantly watching the price points of other sellers in order to learn what works in the marketplace.
These algorithms are not necessarily programmed to monitor other algorithms in this way. But they learn that it’s the best thing to do to reach their goal of maximizing profit. This results in an unintended collusion of pricing, where prices are set within a very close boundary of each other. If one firm raises prices, competitor systems will immediately respond by raising theirs, creating a colluded non-competitive market.
Monitoring the prices of competitors and reacting to price changes is normal and legal activity for businesses. But algorithmic pricing systems can take things a step further by setting prices above where they would otherwise be in a competitive market because they are all operating in the same way to maximize profits.
This might be good from the perspective of companies, but is a problem for consumers who have to pay the same everywhere they go, even if prices could be lower. Non-competitive markets also result in less innovation, lower productivity and, ultimately, less economic growth.