What are the applications of expert systems in pricing?
Machine learning models have a notable advantage: they require no programming to be used. All you need is a dataset large and high-quality enough to obtain useful predictions. However, this condition is rarely met in practice.
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Most machine learning models applied to pricing seek to optimize indicators such as units sold, revenue, and profitability. To do so, they rely on variables such as internal sales, competitor prices, costs, market shares, advertising investment, distribution investment, among many others.
The problem is that these data are not always available for a sufficiently long period. When information is incomplete or insufficient, the trained algorithms lack robustness and their predictions become unreliable.
This does not mean that artificial intelligence cannot be used in pricing. What it does mean is that machine learning —one branch of artificial intelligence— has limited applications when it comes to optimizing prices.
Advantages of expert systems
Unlike machine learning, expert systems do not depend on historical data. Their strength lies in being programmed based on accumulated human knowledge about pricing strategy, which makes them especially valuable for managing structured pricing processes.
Instead of trying to guess market behavior with incomplete data, an expert system replicates best practices developed over decades. This allows you to build complete and reliable models, even without large datasets.
For this reason, expert systems are the most effective and practical option for designing a comprehensive pricing solution. Machine learning can still be used, but only to calculate inputs such as elasticities, market segments, or attribute valuations.
How an expert system in pricing is structured
Designing an expert system starts with identifying the target market segments and the attributes most valued in each of them. This can be done, for example, through cluster analysis.
Based on this segmentation, the total perceived value of products or services—both your own and those of competitors—is calculated. Then, a value map is built which, by comparing prices and perceived values, allows for determining price premiums associated with attributes that cannot be economically quantified.
These premiums are added to those derived from the total economic value to define a menu of list prices.
In parallel, distribution channels—such as distributors or wholesalers—are analyzed, and the functions and conditions they must meet to access discounts are established. This creates the discount menu, which allows assigning each client a discount aligned with their profile.
At this point, the system already generates recommendations for list prices and customer discounts. By comparing these suggested net prices with current net prices, gaps and adjustment opportunities are identified.
Finally, with current unit data, elasticities, and incremental costs, units, net revenue, and expected gross contribution are projected when applying the new prices.
Advanced functions of an expert system
A well-designed expert system should also allow sensitivity analyses and simulate scenarios of partial implementation of the recommendations. This helps assess impacts and risks before executing adjustments.
In summary, expert systems allow the construction of solid pricing models by leveraging human knowledge instead of relying exclusively on historical data. This makes them the cornerstone of a rigorous and sustainable price-management process.