A deep learning-based system to track and analyze customer behavior in retail store

Andrea Generosi · Silvia Ceccacci · Maura Mengoni

50 citations0/0 files storedIEEE International Conference on Consumer Electronics - Berlin (ICCE-Berlin)

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Emotion and Mood Recognition Consumer Retail Behavior Studies Face recognition and analysis

Abstract

The present work introduces an emotional tracking system to monitor Shopping Experience at different touchpoints in a retail store, based on the elaboration of the information extracted from biometric data and facial expressions. A preliminary test has been carried out to determine the system effectiveness in a real context regarding to emotion detection and customers' sex, age and ethnicity discrimination. To this end, information provided by the system have been compare with the results of a traditional video analysis. Results suggest that the proposed system can be effectively used to support the analysis of customer experience in a retail context.

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References

2009Customer Experience Creation: Determinants, Dynamics and Management Strategies 2012The Experience Economy 2007Understanding customer experience.2009Customer Experience Management in Retailing: An Organizing Framework 2016Training deep networks for facial expression recognition with crowd-sourced label distribution 2015Understanding Customer Experience 2016EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild 2004Customer retailer loyalty in the context of multiple channel strategies

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