Setting the right price is a key factor in business success. Still, many companies use gut feeling rather than facts when determining the cost of their products. With predictive pricing, we can apply machine learning to the problem of setting the best price and improve revenue optimization.
We can change the price dynamically too.
Many companies use gut feeling rather than facts when determining the cost of their products
Dynamic pricing is often divided into two categories: revenue management, which is driven by supply like hotel rooms or seats on a flight, and supply driven, which is propelled by competitor price-matching or out-of-stocks.
Customer acceptance for dynamic pricing is generally quite high for products with fluctuating availability and demand, where the product by nature vanishes after a certain time.
Take a global car rental company with thousands of locations as an example. They may change millions of prices each day to optimize their revenue based on a huge number of factors, from the weather, to local events, to the prices of their competitors.
Customer acceptance for dynamic pricing is generally quite high for products with fluctuating availability and demand
This can clearly not be done manually, and prediction algorithms can help find the optimal pricing for each car, in each location, at every hour.
Luckily, readymade solutions for revenue optimization exist, and they can predict the best price of most products—provided you have enough data to crunch.
One example is Perfect Price, which offers a product that covers both revenue management and supply-driven situations. I had a conversation with Alexander Shartsis, co-founder and CEO of Perfect Price.
He says that “Today, consumers accept and even expect price fluctuations. Frequently, they are able to time them better than the companies setting the prices—costing companies billions annually. It seemed clear that there should be a simple service to automate the complex data science required to make dynamic pricing possible for any business.”
This platform generates a demand function that is accurate down to the micro-segment level, meaning it is specific for very small groups of customers. It then can calculate an optimal price based on business rules and other configurable parameters in order to optimize the customer’s objective—profit, revenue, etc.
It is obvious this type of technology can benefit many companies, in particular those where the pricing is changing often, as is the case with flight tickets, rental cars, and hotel rooms.
Prediction algorithms can help find the optimal pricing
Additionally, AI can be used to predict the most likely selling price for certain goods that are never exactly the same, like the price of a used car or a house on the real-estate market.
In such cases, data about the sale of similar products are gathered over time, for example the selling price of used cars, along with data attributes like the type of engine, stereo, color, mileage, equipment, the owner’s neighborhood, and more.
Given this data, a price prediction algorithm can guess the most likely selling price for the next used car to be sold, even though it has never seen exactly the same combination of attributes before.
A price prediction algorithm can guess the most likely selling price
This works similarly for real estate properties and other categories of products where there is only one unit of the same configuration available. Regression algorithms are suitable for this kind of functionality.
An unexpected example of a solution like this comes from GoDaddy, the web hosting and domain name supplier.
Their domain value and appraisal tool uses natural language processing to understand the word combinations used in a domain name. It then predicts the price of the domains using those words on the domain name aftermarket.
That’s why when you go to buy a domain, the one you want may be expensive, while similar ones without the same pull can be quite cheap.