In the field of machine learning, counterfactual explanations have become a valuable tool for interpreting predictions and guiding business decisions. However, conventional methods have significant shortcomings: they often require the user to externally define a target value and a distance function, elements that are difficult to justify in real-world business contexts. In scenarios such as pricing or product design, what truly matters is not reaching a specific value, but maximizing a tangible outcome, such as profit. A promising approach reframes counterfactual explanation as a profit maximization problem, where the cost of modifying attributes is interpreted as an investment and the goal is to obtain the highest possible return. Thus, instead of asking 'what changes would make the model predict X?', the question becomes 'how should I adjust the product to increase the net margin?'.
Let us take the sale of manga in Japan as an illustrative case. A publisher wants to improve the performance of a series. Using an artificial intelligence model trained on historical data, counterfactual recommendations can be generated: for example, slightly reducing the price, modifying the cover design, or including a free gift. These suggestions not only alter the sales prediction but are optimized to maximize total profit, considering production costs and the costs of implementing the change. This approach turns the explanation into a strategic decision aligned with business objectives. To put it into practice, companies need robust analysis and development platforms, such as those offered by Q2BSTUDIO, which integrate artificial intelligence for businesses with modeling and simulation capabilities.
Implementing these systems requires a solid technological ecosystem. Q2BSTUDIO provides custom applications that allow capturing, processing, and analyzing product data, as well as deploying optimization models in the cloud. The use of AWS and Azure cloud services ensures scalability and security, while business intelligence tools such as Power BI facilitate the visualization of counterfactual results for product teams. Furthermore, incorporating AI agents can automate the generation of real-time recommendations, adjusting manga features according to demand and costs. All of this is under a cybersecurity umbrella that protects both customer data and proprietary models. In summary, the combination of profit-oriented counterfactual explanations and an appropriate technological infrastructure enables companies to make more informed and profitable decisions, transforming artificial intelligence into a driver of continuous improvement.
