Abstract
Personal customer care is one of the advantages of physical retail over its online competition, but cost pressure forces retailers to deploy staff as efficiently as possible resulting in a trend of staff reduction. For staff and managers it becomes harder to keep track of what is happening in a store. Situations that would benefit from intervention like cases of aimless customers, lost children or shoplifting go unnoticed. To this end, real-time tracking systems can provide managers with live data on the current in-store situation, but analysis methods are necessary to actually interpret these data. In particular, anomaly detection can highlight unusual situations that require a closer look. Unfortunately, existing algorithms are not well-suited for a retail scenario as they were designed for different use cases or are slow to compute. To resolve this, we investigate the use of long short-term memory autoencoders, which have recently shown to be successful in related scenarios, for real-time detection of unusual customer behavior. As we demonstrate, autoencoders reconcile the precision of reliable methods that have poor performance with a speed suitable for practical use.
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This work is based on VICAR, a project partly funded by the German ministry of education and research (BMBF), reference number 01IS17085C. The authors are responsible for the publication’s content.
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Nalbach, O., Bauer, S., Dahlem, N., Werth, D. (2020). Real-Time Detection of Unusual Customer Behavior in Retail Using LSTM Autoencoders. In: Abramowicz, W., Klein, G. (eds) Business Information Systems. BIS 2020. Lecture Notes in Business Information Processing, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-030-53337-3_7
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DOI: https://doi.org/10.1007/978-3-030-53337-3_7
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