Most yoga studios know intuitively which classes are full and which are empty. They don't know the actual fill rates, the trend over time, how fill rates vary by instructor, or the revenue impact of their utilization pattern. That gap — between intuitive knowledge and measured data — is where significant revenue leakage hides. A studio running an average fill rate of 58% when the same schedule, taught by the same instructors, could run at 74% with modest schedule adjustments is losing real money every week to a problem that's visible in the data but invisible to intuition.
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.
How to Calculate Fill Rate
Fill rate is the percentage of available spots that are actually attended in a given class or time period. For a class with a capacity of 20 students that runs with 14 attendees, the fill rate is 70%. At the studio level, the aggregate fill rate is total attendances divided by total available spots across all classes in the period.
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.
The Revenue Math of Fill Rate Improvement
The financial case for taking capacity utilization seriously: a studio running 25 classes per week at capacity 20, with a current average fill rate of 60%, has 15,000 available student-slots per month and fills 9,000 of them. The 6,000 empty slots represent different things depending on membership model — for unlimited members they're sunk-cost capacity, but for class pack and drop-in students they're real revenue: 6,000 empty slots × $15 average drop-in equivalent = $90,000/month of theoretical maximum revenue vs. actual $54,000 being captured.
Obviously no studio fills every seat, and unlimited members dilute the per-seat revenue calculation. But the directional math is useful. Improving average fill rate from 60% to 72% — a 12-point improvement that's achievable through schedule optimization in most studios — is a 20% increase in class utilization with no increase in class count, instructor payroll, or facility cost. The revenue captured per dollar of fixed cost improves materially.
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.
What Low Fill Rates Actually Tell You
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.
Using Fill Rate Data to Make Schedule Decisions
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.
A data-driven schedule review, run quarterly, asks: which slots consistently underperform? Which consistently generate waitlists? Which time slots have untested demand? This analysis — which requires 8–12 weeks of fill rate data by slot — produces specific schedule recommendations: cut the Thursday 2pm class, add a second Wednesday 7pm class, experiment with a Friday evening slot.
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.
Off-Peak Capacity: A Different Problem
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.
What to Look for When Evaluating
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?
Mako CRM provides fill rate analytics by class, instructor, and time slot — including attendance-based (not booking-based) fill rates and trend data over configurable periods. Try the self-serve demo to see how capacity analytics connect to schedule and operational decisions.
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