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Understanding shopper behaviour is one of the keys to success for retailers. In particular, it is necessary that managers know which retail attributes are important to which shoppers and their main goal is to improve the consumer shopping experience. In this work, we present sCREEN (Consumer REtail ExperieNce), an intelligent mechatronic system for indoor navigation assistance in retail environments that minimizes the need for active tagging and does not require metrics maps. The tracking system is based on Ultra-wideband technology. The digital devices are installed in the shopping carts and baskets and sCREEN allows modelling and forecasting customer navigation in retail environments. This paper contributes the design of an intelligent mechatronic system with the use of a novel Hidden Markov Models (HMMs) for the representation of shoppers’ shelf/category attraction and usual retail scenarios such as product out of stock or changes on store layout. Observations are viewed as a perceived intelligent system performance. By forecasting consumers next shelf/category attraction, the system can present the item location information to the consumer, including a walking route map to a location of the product in the retail store, and/or the number of an aisle in which the product is located. Effective and efficient design processes for mechatronic systems are a prerequisite for competitiveness in an intelligent retail environment. Experiments are performed in a real retail environment that is a German supermarket, during business hours. A dataset, with consumers trajectories, timestamps and the corresponding ground truth for training as well as evaluating the HMM, have been built and made publicly available. The results in terms of Precision, Recall and F1-score demonstrate the effectiveness and suitability of our approach, with a precision value that exceeds the 76% in all test cases.

In retail field, customer culture is shifting towards in-store researching, and retailers need to re-evaluate their location services to better assist customer. In-store mapping help retailers learn how their employees are interacting and it satisfies user intent to search for products, something that is often ignored by retailers especially for the secondary placement, which contains offers and promotions that change very often. In this paper, we describe a retail robot that moves autonomously inside a store and gathers points cloud data for a semantic store mapping. With all the data collected, it is possible to build a 3D map of the store with the exact product locations. This retail robot combines the features of both Robotics and Artificial Intelligence. Three classification approach have been compared in order to achieve the best performances: a machine learning technique, PointNet++ and a novel Reflectance PointNet++ especially designed for this task. Experiments are performed in a real retail environment that is an Italian supermarket, during business hours. A dataset has been built and made publicly available. The application of our approach yields good results in terms of precision, recall and F1-score and demonstrates the effectiveness of the proposed approach.