E-Commerce Pricing Algorithms, And How They Decide What Is "Best" For Consumers

For those people who have searched for a product on online shopping sites to realize that the price can change a few hours later, they are not alone.

They should know that they have been subjected to the e-commerce site's pricing algorithms.

In a good way, algorithms can sort many things out, making tedious and repetitive task for humans a lot more bearable.

On e-commerce websites, or airlines' websites and hotels, algorithms are doing most of the work rather than human, as automated systems have become ubiquitous on the internet.

With algorithms, these companies no longer need to manually set their prices. With algorithms, many e-commerce stores have moved from rule-based programs to reinforcement-learning ones, where the logic of deciding a product’s price is no longer within a human's control.

So, how does it really work?

Read: Types Of E-Commerce Models

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Traditionally when deciding the price of a product, the sellers are the ones that consider the product's price, based on the target income, the potential buyers, and how much similar products cost.

But in the modern technologically-driven marketplace, pricing algorithms are most often conducting these activities, and they are the ones that set the price of products within the digital environment.

What’s more, these algorithms may decide what's good or bad for certain customers, by including certain customer data, like purchase history, location, interest and others, to make them come to a conclusion.

To do this, these pricing algorithms leverage machine learning technology.

The algorithms can learn from the activity of online shops, to get the idea of the economic dynamics of the marketplace (how products are priced, normal consumption patterns, levels of supply and demand). They can also "spy" on competitors by constantly watching the price points of other sellers in order to learn what works in the marketplace.

Originally, online shopping sites were hailed because they work on the web, allowing consumers to easily compare prices.

However, with the increase in competition, normally, this would cause a number of sellers to force their products' prices down. But with algorithms, modern revenue management pricing systems allow online retailers to use market data to predict demand and set prices accordingly to maximize profit.

These revenue management systems have led to the term "dynamic pricing", which refers to online sellers in instantly alter the price of goods or services in response to the slightest shifts in supply and demand.

So instead of pulling the price down, the algorithms can sort things out to keep the price up as long as possible, and even potentially increase the price in a blink of an eye, if certain criteria is met.

These systems have been exceptionally popular within the hospitality and tourism industry, particularly because many of them have fixed costs, perishable inventory, and fluctuating levels of demand.

In most cases, revenue management systems allow these companies to quickly and accurately calculate ideal prices, based on past performance data and current market data. As a result, products can be put on sale with the companies having the ability to easily adjust the prices.

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While humans still play an important role in revenue management systems by analyzing the collected data and making the final decision about prices, but the algorithms can largely work by themselves.

This might be good from the perspective of companies. But for consumers, that is a different story.

Customers are the ones who are subjected to the pricing algorithms, which can fluctuate prices, not just at sale time, but several times over the course of a single day.

As previously mentioned, if supply is larger than the demand, sellers were forced to keep their price down. But with algorithms, consumers can see a relatively steady price (or sometimes even subjected to an increase in prices each time they start comparing prices).

This results in an unintended collusion of pricing, where prices are set within a very close boundary of each other.

If one site raises the price of a product, competitors' systems can immediately respond by raising theirs, creating a colluded non-competitive market.

This can also hurt offline retail stores if they don't keep up.

For consumers, there is nothing they can do. Companies and sellers are the ones who benefit the most with this pricing algorithms.

This is why they should be regulated, to make sure that pricing algorithms in e-commerce won't result in artificially high prices throughout the marketplace.