## 1 Introduction  
The retail sector has transformed over time, integrating IT innovation to interact better with today's consumers increasingly connected to the Internet. Modern technology tools provide users with highly personalized and fluid experiences across every shopping channel. Companies by means of data analysis can offer a better customer experience, with faster, more reliable, services and better operational efficiency. The digital transformation and expansion of online commerce allows companies to implement an omni-channel strategy. Therefore, the need arises to manage the various sales channels and various stores of a company. This research aims to investigate the applicability and performance of approaches based on boosting techniques in a case study related to the sales prediction in a multi-store or multi-channel context. In particular, two different implementations are designed, implemented and preliminarily tested. The output of the developed approach helps an e-commerce company to automate its internal processes and optimize workflows. Most business companies have an online presence and have expanded online interfaces to offer additional services. Today, customers have the opportunity to browse an inventory, locate their purchases and collect them in the store. Companies are increasingly relying on data analysis for their operations, including product positioning, inventory optimization and forecasting of supply and demand. The use of technological tools in the sales sector, through both traditional channels and e-commerce websites, favors the collection of Big Data. These data contain valuable information for companies, which can leverage them to develop effective business strategies. The large amount of data, their heterogeneity and the speed they are collected with,force the use of Big Data Analytics methods in order to mine this information [1]. In particular, machine learning provides several methods for optimizing the supply chain and product-sourcing operations, for both marketing and customer loyalty. Starting from data collected on product sales and consumer behavior, machine learning can offer highly personalized recommendations on the purchase of products. For example, customers can receive personalized content and product suggestions that could catch the customer interest. The analysis of historical data using machine learning makes it possible to evaluate a variety of factors in order to optimize the choice for suppliers [2; 3] and the product-delivery routes for greater logistical efficiency [4; 5]. The automated customer segmentation is very useful for maximizing sales, as it allows companies to understand the specific needs of customers and to communicate with them in a personalized way. For example, clustering analysis permits the identification of the best customers, encouraging them to make new purchases through targeted email marketing campaigns. Machine learning approaches are used in various technical and scientific applications for their ability to automate processes and improve performances. The research [6] presents a methodology, based on k-means clustering, which can be applied to automatically control the quality of various industrial production processes, such as turning, glazing and laser cutting. Authors [7] propose an approach that uses machine learning algorithms to process rock tunnel face images. The developed methodology is used to segment rock fractures and quantify the fracture traces. One study [8] discusses an automatic approach based on machine learning algorithms, such as a gradient boosting tree, for the automatic classification of rock traces. An added value of machine learning lies in its predictive nature. In fact, in addition to being able to detect the freshness of perishable goods [9], leakage in water-piping networks [10] and the wear of machinery [11], machine learning-based technologies are able to predict the propagation rate of the plume in underground water leakages [12], and customer behavior, future trends and market demand [13].