Abstract: This talk aims to discuss a practical question within the growing world of Mathematics Learning Centers (MLCs): when and for which courses are students showing up to get help? While we know MLCs are great for student success, much of the evidence is based on usage totals that miss the juicy details of student behavior patterns. To truly optimize support, we need to move beyond the swipe. We analyzed granular "swipe-in/swipe-out" data from a university's MLC to investigate a single, crucial question: "What are the patterns of student attendance at the math learning center and how do these patterns vary depending on the time of the academic year and the specific course?” We use a unique framework of a Retail Store Theory that views students as goal-oriented customers, this research digs deep into the data behind the demand driven by specific math curricula and the attendance spikes caused by various semester factors. The analysis reveals the rhythms of student need, identifying crucial windows of time and specific courses that generate the highest demand. These findings provide data-driven results for administrators, allowing them to make smart choices about staffing models, tutor training schedules, and proactive outreach to maximize the MLC's impact. Ultimately, this work turns raw attendance logs into a precise blueprint for a more responsive and effective academic support system. (Note this is part of a larger study analyzing various research questions, but for this talk we focus on only the one question listed.)