Towards a Predictive Model that Supports the Achievement of More Assertive Commercial KPIs Case: Wood Trading Company

This article presents a predictive model to determine possible causes of commercial results in a company that commercializes products and services for the furniture and wood industry in Colombia. To achieve this, a literature review was carried out to identify analysis strategies and new technologies that could influence the proposed model. Then, using the CRISP-DM methodology, the main variables and indicators that make up the business model and the problems associated with decision-making were identified, with the aim of predicting and optimizing their KPIs, improving performance metrics and achieving an increase in commercial benefits. A case study was carried out with a data set of 99,972 records collected between 2020 and 2023, which facilitated the application of variable selection techniques to identify the most influential in the prediction. The model was developed using algorithms such as decision trees, random forests, and logistic regression. Once the model was trained, it was determined that the random forest regression algorithm with the Out-of-Bag validation method and an R2 of 94.1% provided the best results and delivered the highest sales prediction. In testing, the model showed that it was influenced by variables such as average invoice value, number of invoices, available inventory, and order fulfillment. These findings expand decision-making capacity by defining which variables must be controlled to improve results. In conclusion, The machine learning-based predictive model can identify potential causes of business outcomes and improve the accuracy of decisions at a strategic level in the area of timber trading. However, it is suggested to complement it with other variables to obtain an even more precise diagnosis.