Customer context analysis (CCA) in brick-and-mortar shopping malls can support decision makers' marketing decisions by providing them with information about customer interest and purchases from merchants. It makes offline CCA an important topic in marketing. In order to analyze customer context, it is necessary to analyze customer behavior, as well as to obtain the customer's location, and we propose an analysis system for customer context based on these two aspects. For customer behavior, we use a modeling approach based on the time-frequency domain, while separately identifying movement-related behaviors (MB) and semantic-related behaviors (SB), where MB are used to assist in localization and the positioning result are used to assist semantic-related behavior recognition, further realizing CCA generation. For customer locations, we use a deep-learning-based pedestrian dead reckoning (DPDR) method combined with a node map to achieve store-level pedestrian autonomous positioning, where the DPDR is assisted by simple behaviors.