  Understanding customer behavior in retail stores plays a crucial role in
improving customer satisfaction by adding personalized value to services.
Behavior analysis reveals both general and detailed patterns in the interaction
of customers with a store items and other people, providing store managers with
insight into customer preferences. Several solutions aim to utilize this data
by recognizing specific behaviors through statistical visualization. However,
current approaches are limited to the analysis of small customer behavior sets,
utilizing conventional methods to detect behaviors. They do not use deep
learning techniques such as deep neural networks, which are powerful methods in
the field of computer vision. Furthermore, these methods provide limited
figures when visualizing the behavioral data acquired by the system. In this
study, we propose a framework that includes three primary parts: mathematical
modeling of customer behaviors, behavior analysis using an efficient deep
learning based system, and individual and group behavior visualization. Each
module and the entire system were validated using data from actual situations
in a retail store.
