Studying shopping behavior is an important and interesting topic for researchers and practitioners. With the improvement of technology in data collection and handling, it is possible and important to take full advantage of these data opportunities to analyze in-store shoppers’ movements so as to understand shopping behavior from different standpoints. In this study, we set up a three-component procedure-based application with the use of a method from Markov chain approach. In this procedure, the sensor network system, the Shopper In-store Movement Graph (SIMG) generator, and the Transition Matrix Compression Algorithm (TMCA) engine, are used to analyze in-store shopping paths and to cluster in-store zones with similar transition behaviors. After clustering, some characteristics of complicated shoppers’ movements can be observed more clearly. An experiment is performed on real data to illustrate that the procedure works in practice. Finally, we close with a brief conclusion and an outlook for the future.