Evidence note: Present this as a scenario and validate it with a controlled schedule test. Baseline, class mix, capacity, payroll, facility constraints, and causal test are absent. Source status: No primary source was found for the earlier universal claim as of 2026-09-01.
This guide covers how to read capacity utilization data, what it tells you about schedule problems vs. demand problems, and how to act on it.
Evidence note: Mako documents class capacity and actual attendee counts against capacity; 14 divided by 20 equals 70%. This is supported by first-party product documentation, not established as a universal industry standard. Sources: Mako first-party: Class Scheduling and Membership Software for Studios.
The calculation needs to be based on actual attendance, not bookings. Bookings overstate fill rates in studios with late cancel and no-show rates — a class with 20 bookings and 5 no-shows has a 75% attendance fill rate even though it "looked" full at booking. The late cancel and no-show rate directly impacts this metric.
The useful segmentation for fill rate analysis: by time slot (weekday mornings vs. evenings vs. weekends), by format (hot yoga vs. yin vs. vinyasa), by instructor, and by week of month (do fill rates drop in the third week of the month?). Each segmentation reveals a different layer of the capacity story. Time slot analysis shows schedule mismatches; format analysis shows format popularity trends; instructor analysis shows instructor-driven demand; week-of-month analysis shows billing cycle effects on attendance behavior.
Evidence note: Recompute from explicit class count, period, capacity, attendance, and price. Weeks per month, attendance, price, and class frequency are inconsistent or unstated. Source status: No primary source was found for the earlier universal claim as of 2026-09-01.
The revenue model context matters: studios with predominantly unlimited memberships benefit less directly from fill rate improvement (members are already paying regardless of how often they attend) and more indirectly (higher-utilization classes feel more vibrant, which improves retention). Studios with a higher share of pack and drop-in customers benefit more directly from filling empty spots.
A class consistently running at 35% fill rate is not necessarily a failed class. The interpretation depends on which question it answers:
Is it a scheduling problem? A 35% fill rate at 11:30am on Wednesday, while the 9am Wednesday class runs at 90%, suggests a time slot that doesn't fit demand patterns rather than a format or instructor problem. The answer is schedule adjustment: move the 11:30am slot to a time with demonstrated demand, or eliminate it.
Is it a format problem? A restorative yoga class consistently underperforming while every other format in the same time slot runs higher means the format has weak demand at this studio. The answer may be replacing the format rather than the instructor or the time.
Is it an instructor problem? If the same time slot has 70% fill rate with Instructor A and 35% fill rate with Instructor B, the format and time aren't the variable. The answer requires a direct conversation with Instructor B about their class, their promotion of it, and whether adjusting their approach improves numbers.
Is it a trend problem? A class that ran at 75% six months ago and is now at 40% is a different situation than one that has always been at 40%. The trending class is telling you something changed — a competing class was added to the schedule, a neighborhood demographic shifted, or member preferences evolved. Understanding the inflection point in the data leads to the answer.
The schedule is a product, not a fixture. Most yoga studio schedules accumulate over years through incremental additions and are rarely reviewed against demand data. The result is schedules with 6 classes per day that include 2 that run at 30%, alongside waitlisted classes that could support a duplicate slot.
Evidence note: Use a dated rolling window and compare fill, waitlists, margin, and member impact. Seasonality, instructor, format, slot, waitlist, and local-demand confounds are not addressed. Source status: No primary source was found for the earlier universal claim as of 2026-09-01.
The waitlist data and the fill rate data are complementary inputs to this analysis. Waitlisted classes at 100% fill rate identify supply gaps; chronically low fill rate classes identify supply excess. The schedule optimization is matching supply (classes offered) to demand (students who want to attend) as tightly as possible.
Low off-peak fill rates (weekday late mornings, early afternoons) are a structural feature of most yoga studio demand patterns, not a fixable scheduling problem. The demand for yoga at 1pm on Tuesday is genuinely lower than at 7am or 7pm — no amount of format experimentation or instructor substitution will change that demographic reality.
Off-peak capacity is best addressed through targeted incentives rather than schedule manipulation: pack pricing that's valid only for off-peak classes, specialized formats (prenatal, senior, therapeutic) that serve segments with off-peak availability, or corporate wellness partnerships that bring a dedicated group to consistent off-peak slots. These approaches accept the demand structure and adapt to it rather than fighting it.
When evaluating whether your software gives you the capacity data you need: Does it produce fill rate reports segmented by time slot, format, and instructor? Does it distinguish attendance fill rate from booking fill rate? Can you view fill rate trends over time (8-week rolling average) per class? Does it connect fill rate and waitlist data in the same view for schedule decision-making?
Evidence note: Mako documents class capacity, actual attendance, utilization, and analytics views. Exact cross-tab dimensions, trend reports, and plan availability are not all documented. Say Mako documents capacity, attendance, and utilization views and verify exact report fields. Sources: Mako first-party: Class Scheduling and Membership Software; Mako first-party: Business Analytics for Service Businesses.
