Unlocking Retail Success: Empowering Decision-Making with Advanced Sales Forecast Models

Abstract

The gross revenue indicator contributes to the understanding of the company’s situation, and generating sales revenue forecasts is a strategy that helps the manager in directing the business. This work aims to develop a set of Machine Learning (ML) models to forecast sales in physical retail. Methodology – To carry out this work, a methodology was proposed to create, compare and evaluate ML models. Findings – When analyzing the forecast scenarios, it was observed that Hourly forecasts performed better than Day forecasts. We highlight the LIGHTGBM model, which presented the best scores in the F1-score metric with 82.95%, 79.26% and 76.53% scenario representing one hour, two hours and three hours ahead, respectively. Value – It is expected that the forecast models will help managers to find insights to support the operational decisions of physical retail contributing to carry out actions to optimize companies’ processes.