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.