How AI Learns What You’re Willing to Pay
Why are we all paying different prices? Is it price “personalization” or price “discrimination”? The answer isn’t so simple.
Why are we paying different prices? Is it ‘price personalization’ or a form of ‘price discrimination’? The answer isn’t so simple.
The world of Artificial Intelligence (AI) dynamic pricing engines is rapidly progressing and changing the competitive landscape. This article provides an overview of a few areas that influence how an AI pricing engine decides the price to show you:
AI micro-segmentation uses many customer attributes and behaviors to bucket customers by estimated willingness to pay. To explain in simple terms, let’s assume we have three buckets. We segment our customers by (A) high paying customers, (B) medium paying customers and (C) low paying customers. One strategy to maximize profits would be to first sell only to the high paying customer group #A. Then any remaining seats could be sold to medium paying group #B. Lastly, any leftovers could be sold to low paying group C .
Wait a second! We all know airline prices go up as the booking date gets closer to the departure date. Everyone knows to get a great price on an airline ticket you should book early!
So, it’s not as easy as first selling to high paying group A, then medium paying group B and giving leftovers to low paying group C. In most cases the pricing and selling happens in the reverse order – Low paying group C, then medium group B and lastly high paying group A.
How then are sales to the high-paying group A maximized if selling to group C happens first? What prevents all the seats from being sold to low paying group #C with no seats being left to sell to the high paying group A?
Part of the answer is predicting the number of potential buyers and how much each of them is willing to pay weeks in advance. Often before most of the customers have even decided to travel!
A big part of the AI pricing game is having AI learn everything about what is happening in the market. The goal is to have better information than competitors in order to make better decisions. This information advantage is sometimes referred to as asymmetric information.
In terms of demand prediction, asymmetric information allows the AI pricing engine to achieve a more accurate demand prediction than competitors. Ultimately, this advantage results in greater confidence by the pricing engine to hold a price or move it up or down to maximize profit in response to what is happening in the market.
To see how this works, let’s take a hypothetical airline market with 3 airlines serving a destination like Porcupi, Montana. In the airline industry, Porcupi is a small town that sits in the ‘long tail’. This means it is just one of many towns and cities served where each generates only a small amount of revenue. However, like the classic example of Amazon.com, the sum of everything in the ‘long tail’ adds up to be a massive revenue number.
Suppose you are an airline operator and you know a big festival will soon take place in Porcupi. You know that significantly more people will be going to Porcupi than the number of available airplane seats across all the competitors. If you are the only airline operator who knows about the big festival, then the pricing strategy is easy.
