Abstract
customer paths can be used for several purposes, such as understanding customer needs, defining bottlenecks, improving system performance. Two of the principal difficulties depend on discovering customer paths due to dynamic human behaviors and collecting reliable tracking data. Although machine learning methods have contributed to individual tracking, they have complex iterations and problems to produce understandable visual results. Process mining is a methodology that can rapidly create process flows and graphical representations. In this study, customer flows are created with process mining in a supermarket. The differences between the paths of customers purchased and non-purchased are discussed. The results show that both groups have almost similar visit duration, which is 87.5 minutes for purchased customers and 86.6 minutes for non-purchased customers. However, the duration of aisles is relatively small in non-purchased customer flows because customers aim to return or change the item instead of buying.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
