Improving Sales Prediction for Point-of-Sale Retail Using Machine Learning and Clustering

Point of sale retail represents an important aspect of daily consumer purchases.
Even with the increasing growth of online retailing, physical retail stores
provide useful services for consumers. Data analytics can be applied to improve the performance of this type of retailing by better predicting product sales and optimizing product availability. Large physical retail chains sell a wide range of products in different store locations which makes high-quality predictions across different products, categories, and store locations complex and often results in low-quality sales forecasts. Developing a data analytics model for every single product and store in a retail chain would be difficult to scale. Against this background, machine learning methods are highly promising and could be used to cluster stores with similar properties to subsequently provide a single model for predicting their specific product sales. Yet, literature that provides a systematic approach for clustering stores based on a standardized list of properties is limited. This paper addresses this gap by identifying the main factors for clustering retail stores and examines model combinations of clustering and prediction algorithms that improve sales forecasts in retail stores. The results of this paper show selected factors for organizing stores and present the best performing algorithms for predicting product sales.