AI-powered pricing systems track consumer preferences and price products accordingly. They also factor in demand elasticity to recommend the optimal price point for a product that preserves sales and keeps margins high.
AI-powered price optimization is fast becoming a common tool for retailers. However, businesses need to understand the nuances of AI pricing.
Optimal pricing is the selling price and volume at which a company maximizes its profits. It is based on price elasticity of demand and ensures that customers are charged exactly what they’re willing to pay.
It is a very complex problem and businesses have tens or even hundreds of pricing strategies they use, some of which fail and some that succeed. It is a challenge to find the optimal price point for a product or service when competitors’ prices, customer purchase histories and seasonality play a role.
Artificial intelligence solutions like Machine Learning are trained to spot meaningful patterns in consumer spending habits that help businesses automate price segmentation. This allows companies to offer different pricing tiers, bundles or sales sections to reach different customer segments with their products. It also enables businesses to track price elasticity of demand across customer subsegments to avoid cannibalization (cross-pricing effects).
Using AI-backed dynamic pricing methods, companies can swiftly and precisely balance supply and demand to maximize productivity levels. Uber uses this technique to improve capacity utilization in the sharing economy space, for example.
Dynamic pricing algorithms, also known as “surge pricing,” can also be used to adjust prices based on a wide variety of factors, including competitor prices and inventory fluctuations. This technology can help retailers better pace their prices in response to changing supply and demand, and in turn boost sales and margins.
Another way that AI can optimize pricing is by allowing companies to view their data at a micro level. This gives them the ability to uncover nuances that would otherwise be out of sight and out of mind in more traditional price setups.
In addition to helping companies make smarter decisions, AI-driven pricing techniques can improve customer experiences and build loyalty by personalizing pricing recommendations and offers. Retailers can use this type of system to automatically send customers personalized product suggestions based on their shopping habits and purchase history, or even encourage them to check out with a discount the very next day.
AI-powered pricing models comb through vast amounts of data, assessing thousands of unrevealed relationships between the items in your product portfolio. They suggest individual prices that maximize revenue and sales across your entire product portfolio.
In addition, AI-powered pricing systems can provide recommendations for exclusive products that differentiate you from competitors — private label items that you sell at a higher price. These recommendations can also help you protect your margins and reduce the impact of a pending price change on consumers and upstream supply chains.
However, algorithmic recommendations can be difficult to implement for many industries because they depend on historical data that is not always available. For example, if all your historical transactions were sold at one price point, it is hard for an AI model to provide you with a reasonable range of prices in the future.
Transparency is an important part of ensuring that AI systems are trustworthy. This involves ensuring that the system can be accessed and interpreted by users, and that they understand what their data is being used for, as well as how the algorithm has made its decisions.
Many stakeholders will need to be consulted when defining transparency requirements. These include individuals whose personal information is being processed, regulators, watchdogs or the general public, depending on the nature and function of the system being developed and implemented.
The most effective way to ensure that an AI system is transparent is to provide a clear and comprehensible explanation of the algorithm’s models and decisions to those who would need to use them. This should be done in a manner that does not compromise the integrity of the system.
Despite the benefits of transparent AI, some researchers have warned that exposing information about a machine-learning model can make it vulnerable to attack. For example, researchers at the University of California, Berkeley, have shown that if an algorithm’s explanations are provided to others, they can be used to steal entire algorithms.