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Modelling and Forecasting Customer Navigation in Intelligent Retail Environments

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Abstract

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.

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Authors and Affiliations

  1. Department of Information Engineering (DII), Università Politecnica delle Marche, Via Brecce Bianche, Ancona, Italy

    Marina Paolanti, Daniele Liciotti, Rocco Pietrini, Adriano Mancini & Emanuele Frontoni

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  1. Marina Paolanti
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  2. Daniele Liciotti
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  3. Rocco Pietrini
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  4. Adriano Mancini
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  5. Emanuele Frontoni
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Correspondence to Marina Paolanti.

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Paolanti, M., Liciotti, D., Pietrini, R. et al. Modelling and Forecasting Customer Navigation in Intelligent Retail Environments. J Intell Robot Syst 91, 165–180 (2018). https://doi.org/10.1007/s10846-017-0674-7

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