Operators struggle with staffing. They often guess how many team members they need. This wastes wages. It also hurts customer service. Both damage your business. Data predicts demand. This guide shows you how to staff smarter, not harder.
Identify Your Core Data Points
Guessing staffing needs costs money. Gather the right information. Sales data is your most important tool. Track transactions, average check size, and customer counts. Your Lavu POS collects this automatically.
Record employee hours. Also, track tips and wages. This creates a clear picture of labor costs. Accurate data entry in your POS makes forecasting possible.
Analyze Historical Sales Performance
Look back before you look forward. Examine past sales data. Check daily, weekly, and monthly trends. Find your busiest hours. Find your slowest periods. Monday lunches might be light. Friday nights are packed.
Marty, Lavu’s AI analytics layer, quickly shows these patterns. It helps you see predictable sales spikes and dips. Understand these rhythms. They form your staffing predictions.
Factor in External Influences
Sales are not just about your menu. External factors affect customer traffic. Holidays, local events, and weather change your forecast.
A big concert nearby might double evening sales. A snowstorm can halt all traffic. Adjust staffing predictions for these outside influences. Do not rely only on historical averages for unique days.
Calculate Your Labor Targets
What is your ideal labor cost? Determine your target labor cost percentage. Many full-service restaurants aim for 25-35% of sales. If your weekly sales are $15,000, your labor spend should be $3,750-$5,250.
Track sales per labor hour. This metric tells you how much revenue each hour of work generates. Low sales per labor hour might mean overstaffing. High sales per labor hour could mean understaffing and rushed service.
Build a Data-Driven Staffing Model
Connect sales data to staffing levels. For every $600 in sales during a busy dinner hour, you might need two servers and one additional kitchen staff member. During slower periods, $300 in sales might only require one server.
Use historical data from Lavu POS to define these specific ratios. Marty suggests optimal staffing levels. It bases this on past performance and sales volume. This moves you from guessing to a data-backed approach.
Implement and Adjust Schedules
Create schedules using your new data-driven model. This gives a strong starting point. Be ready to make minor adjustments daily.
Check actual sales against forecasts regularly. If an unexpected rush happens, add a server. Lavu tracks actual labor costs against your schedule. This allows constant optimization and better decisions.
FAQ
Can I really forecast accurately?
Yes. Consistent data tracking improves accuracy. Lavu POS provides the necessary data.
How often should I update my staffing forecast?
Update weekly for immediate needs. Re-evaluate your core staffing model quarterly.
Does seasonality affect my forecasts?
Yes. Always factor in seasonal changes. Marty’s historical data view helps spot these patterns.
What is a good labor cost percentage?
It varies by restaurant type. Many full-service restaurants aim for 25-35%.
Can technology help with this?
Yes. A modern POS like Lavu collects essential sales and labor data. Marty AI analyzes it for you.
What if I overstaff or understaff?
Overstaffing wastes money. Understaffing leads to poor service.
Should I include managers in labor cost calculations?
Yes. All labor costs, including salaried management, contribute to your overall labor percentage. This gives a true picture of operational expenses.
