As service brands expand across multiple locations, small inefficiencies in staff scheduling can compound quickly. Overstaffing increases labor costs, while understaffing leads to inconsistent experiences that weaken retention and loyalty.

Yet most scheduling tools rely on rigid templates or outdated assumptions, making it difficult to staff around real demand. MyTime takes a different approach with Labor Forecasting, which leverages a recently upgraded AI-driven model to deliver staffing recommendations grounded in actual booking behavior.

This article breaks down how MyTime’s upgraded Labor Forecasting engine works, why demand-aligned staffing is essential for multi-location brands, and how predictive staffing helps operators reduce labor costs while protecting client experience.

What Is Labor Forecasting?

Labor forecasting predicts how many staff members a business needs at any given hour based on historical booking patterns, recent demand trends, and provider productivity. The goal: avoid overstaffing (wasted labor dollars) and understaffing (poor client experience). MyTime automates this with a multi-layered, continuously updating AI model which adapts to each location’s real booking behavior.

Why Labor Forecasting Matters for Service Franchises

• Aligns hour-by-hour staffing to real demand instead of assumptions
• Reduces unnecessary labor spend across all locations
• Adapts automatically to real booking behavior, seasonality, and demand shifts
• Prevents understaffing during peak times, protecting client satisfaction
• Centralizes forecasting for consistent decision-making across the network
• Eliminates guesswork by grounding staffing recommendations in both historical patterns and recent booking data

1. Why Traditional Staffing Breaks Down in Multi-Location Brands

Most service organizations—salons, spas, barbershops, med spas, fitness studios—still rely on static schedules, gut instinct, or last year’s numbers. That approach collapses quickly when you operate 3, 10, or 200+ locations.

Here’s why.

Overstaffing: Hidden Labor Waste That Compounds Across Locations

Fixed schedules often keep too many staff on slow days. Managers repeat the same template even when demand drops.

Result:

Thousands in unnecessary labor spend per month across the franchise, especially as small scheduling inefficiencies multiply across many sites.

Understaffing: The #1 Cause of Service Bottlenecks

When schedules fail to anticipate peaks, clients wait longer, appointments run behind, and experiences deteriorate across several locations at once.

Result:

• Lower revenue per hour
• Lost bookings
• Burnout among staff
• Lower membership retention
• Inconsistent client experience

Stale Patterns: Last Year’s Data Doesn’t Match Today’s Demand

Consumer behavior changes constantly. Relying on old schedules creates misalignment between actual demand for services and scheduled labor.

Examples:

• New slow days emerge
• Local competition changes
• Economic cycles shift booking patterns
• Weather or community events disrupt normal flow

Result:

Small mismatches quickly multiply across a franchise, leading to labor waste, inconsistent service quality, and schedules that go stale long before operators notice.

Multi-Location Complexity: No Two Sites Behave the Same

A downtown location might peak at lunch.

A suburban location might peak after school.

A mall location might peak on weekends.

A “one-schedule-fits-all” approach doesn’t work.

MyTime solves this with location-specific, pattern-specific forecasting.

2. How MyTime Uses Multiple Demand Layers to Improve Forecast Accuracy

Most staffing tools look only at recent appointment volume. MyTime analyzes multiple layers of historical demand to understand true booking rhythms across every location.

Yearly Seasonal Patterns

Recognizes recurring surges such as holidays, summer lulls, back-to-school, and promotional periods.

Monthly Trends

Captures periodic spikes tied to:

• Pay cycles
• Membership renewals
• Local events
• Marketing campaigns

Weekly Patterns

Identifies strong days (e.g., Wednesdays & Saturdays) and weak days (e.g., Mondays).

Daily & Hourly Patterns

At the operational level, MyTime models fine-grained demand by recognizing:

• Peak booking hours
• Mid-day lulls
• Demand fluctuations by service category
• Expected throughput per hour based on provider productivity

Daily and hourly patterns drive the most precise staffing recommendations, ensuring operators match labor to real demand on a shift-by-shift basis.

Why this matters to operators:

Predictive staff scheduling built on multi-layer demand modeling is significantly more reliable than systems that rely only on only month-to-date or week-to-date data.

This gives franchise operators a clearer, more stable foundation for optimizing labor costs at scale.

3. How MyTime Adapts to What’s Happening Right Now (Not Last Year)

A forecasting model is only useful if it adapts quickly.

MyTime incorporates a 90-Day Recent Demand Layer, which continuously learns from the most recent three months of booking trends.

This enables MyTime to:

• Detect newly slow or inactive days (e.g., closed Mondays)
• Respond to short-term behavioral shifts
• Override old seasonal trends when reality changes
• Smooth out one-off anomalies
• Adjust for temporary downturns or surges

Example:

If the last three months show Thursdays have slowed significantly, MyTime immediately adjusts staffing recommendations—even if last year’s Thursdays were busy—reducing unnecessary labor hours at every affected location.

Zero-Activity Day Logic

If a weekday shows consistently zero bookings across 90 days, MyTime recommends zero staffing.

This eliminates unnecessary shifts and supports more cost-efficient staff scheduling franchise-wide.

4. How MyTime Uses Real Operational Data (Not Generic Staffing Ratios)

Most scheduling systems forecast labor based on appointments alone. MyTime goes further by incorporating actual provider productivity, giving brands a clearer picture of true staffing needs.

MyTime’s forecasting uses:

• A full year of appointment history
• Provider throughput per hour
• Service durations
• Historical booking trends
• Real-time operating hours
• Multi-location variability

This ensures forecasts reflect each location's real staffing capacity, rather than relying on generic labor ratios.

5. What More Accurate Forecasting Means for Multi-Location Operators

When labor allocation is based on real behavior—not guesswork—multi-location brands see immediate improvements across operations.

1. More Accurate Staffing Hour by Hour

Schedules match real demand, not static templates.

2. Lower Labor Costs Across the Franchise

Removing just 2–3 unnecessary shifts per week per location produces meaningful savings.

3. Better Client Experience During Peak Times

Peak periods are fully staffed, protecting your brand reputation.

4. Fewer Manual Schedule Corrections

Managers save hours each week adjusting misaligned schedules.

5. More Consistent Service Across All Locations

Every site receives forecasting tailored to its unique behavior.

Conclusion: Smarter Labor Decisions Start With Better Forecasting

MyTime’s enhanced Labor Forecasting gives multi-location brands the precision they need to control labor costs, protect client experience, and make informed staffing decisions at scale. By layering long-term patterns with recent booking signals, eliminating anomalies, and incorporating real provider productivity, MyTime offers one of the industry’s most accurate predictive scheduling tools.

Because Labor Forecasting is built directly into the MyTime Scheduler, operators can act on predictions immediately—without extra tools, spreadsheets, or manual adjustments. Even small improvements in labor efficiency translate into meaningful savings and stronger operational stability across every location.

MyTime customers can contact Support to start using enhanced Labor Forecasting. New to MyTime? Schedule a demo to see it in action.