SELMARK

The project focused on developing a platform based on Machine Learning techniques to forecast lingerie product sales and support decision-making during the Oneshot period of the annual autumn-winter and spring-summer marketing campaigns. The main goal was to estimate the most requested product mix by retail outlets, optimizing commercial planning and production management.

To achieve this, an algorithm capable of analyzing historical purchase data from sellers was developed, identifying those that acquired the most representative product mix of total sales. Thanks to this information, in future campaigns, sales agents can prioritize visits to the most relevant sellers, anticipating demand and improving production efficiency.

This project helped to improve campaign management efficiency, reducing uncertainty and strengthening responsiveness to real market demand.