Never Be Understaffed Again! Use POS Data to Plan Peak Hours

Lunch and dinner rushes vs. slow afternoons. Busy weekends vs. quiet weekday mornings. Friday night rush vs. late-night stragglers. Not all hours are equal. Every business faces staffing challenges—too few employees during rushes and too many during slow hours.

What if you could predict demand and schedule accordingly? That’s where POS data helps.

Instead of relying on guesswork, use POS data analytics to track sales patterns and identify peak hours. This blog explores how POS reports help optimize scheduling and best practices to cut costs while boosting efficiency. Let’s dive in!

Different Businesses, Different Peak Hours (And Why They Matter)

Understanding when your business experiences the most traffic helps prevent staffing nightmares.

Restaurants → Peak: Lunch, Dinner, Weekends

People tend to dine out in predictable waves—lunchtime (12 PM to 2 PM) and dinner (6 PM to 9 PM) see the highest foot traffic. Weekends are even busier as families and groups eat out.

Retail Stores → Peak: Evenings, Weekends, Holiday Seasons

After-work hours see a rise in shoppers, while weekends bring larger crowds. Holiday shopping seasons (Black Friday, Halloween, and Christmas) are peak traffic periods.

Liquor Stores → Peak: Fridays, Holidays, Late Nights

Many customers stock up before the weekend or holidays, leading to rush on Friday evenings and before big celebrations. Late-night spikes also happen just before closing time.

Without proper staffing, your business might have to face:

• Long wait times
• Slow service
• Chaotic checkout counters
• Lost sales
• A higher risk of theft and fraud 
• Frustrated customers who might not return.

Key Benefits of Using POS Data for Smarter Staffing

Instead of hiring too many employees when it's slow or too few when demand is high, using POS data analytics helps businesses with:

• Better Customer Experience – Reduced wait times, faster service, and happy customers who return.
• Lower Labor Costs – Avoid overstaffing and paying for unnecessary shifts.
• Higher Employee Productivity – Ensure a balanced workload to prevent burnout or disengagement.
• Data-driven Decision Making – No more guesswork—schedule staff with confidence.
• Scalability – As your small business grows, structured staffing and scheduling ensure smooth operations. 

10 Ways to Use POS Data to Predict Peak Hours and Optimize Staffing  

Your POS analytics report collects valuable data daily about customer behavior, sales, and staff for better results. Analyzing these insights can help you schedule staff efficiently, cut unnecessary labor costs, and improve customer experience. Here are ten ways to leverage POS reports for smarter staffing decisions.  

1. Analyzing Sales Trends Over Time

Tracking sales trends by the hour, day, or week helps identify peak times, ensuring adequate staffing during rush hours while avoiding overstaffing. In the sales over time POS report, you can set daily and hourly filters to track revenue patterns over different timeframes.  

How to Use It

• Look at year-over-year sales data to identify peak seasons and prepare staffing in advance.
• If foot traffic is high but sales are low, fewer staff might be needed during those hours.

For example, a coffee shop's all-in-one POS system shows that sales peak between 8 AM 10 AM (morning coffee rush) and 1 PM to 3 PM (lunch break). Scheduling more baristas during these hours and reducing staff in the afternoon can maximize the shop’s efficiency without overspending on labor.  

2. Tracking Order Volume to Confirm Demand Spikes

Tracking order volume shows when the most orders happen. Knowing this from the order counts analytics report can help you schedule the right number of staff during peak hours.

How to Use It

• If high order volume includes small transactions, you may need more cashiers rather than sales staff.
• Dine-in vs. takeout vs. delivery orders might peak at different times, requiring different staffing strategies.

For example, a retail store POS shows that even though weekdays seem quiet overall, the lunch break from 12 PM to 2 PM consistently has high order counts. This means the store should schedule extra cashiers only during these hours instead of the entire day.  

3. Examining Hourly Transaction Data

The transaction history report provides an hourly breakdown of transactions, including counter-specific POS data. It helps you find out if specific checkout counters get overwhelmed while others remain idle.  

How to Use It

• Peak return and refund times may require additional staff at customer service counters.
• Determine if one checkout counter is busier than others and redistribute staff accordingly.

For example, a liquor store POS shows that although Fridays are busy, the real congestion happens between 7 PM and 9 PM at one particular checkout counter. Placing an extra cashier at that counter during those hours can improve the store’s service without hiring extra employees for the entire shift.  

4. Studying Daily Sales Performance

The day-wise report gives a big-picture view of how different days perform, helping businesses decide which days need more staffing. It also shows how discounts and promotional ideas impact sales trends.  

How to Use It

• Sales may spike on holiday event days, requiring extra staff.
• Track if customers who are part of a loyalty program visit on specific days and adjust staffing accordingly.

For example, a cloud kitchen POS notices that Saturdays consistently bring in 40% more sales than weekdays. Instead of hiring more full-time employees, bringing in part-time staff every Saturday can manage cloud kitchen operations and optimize labor costs.  

5. Evaluating Employee Shift Efficiency

The employee shiftwise report includes hours worked, order count, sales, pay, and more. It tracks how productive each employee is during their shifts. It helps you identify if current shift assignments align with actual sales volume. You can set up flexible work hours or split shifts to match demand.  

How to Use It

• Identify employees who perform best during peak hours and schedule them accordingly.
• If sick leave spikes on weekends, plan a backup staffing strategy.
• Adjust the number of employees based on actual customer flow rather than standard schedules.

For example, a fine-dining restaurant POS shows that the dinner rush brings in the most orders, but servers from the afternoon shift are still on the clock. Staggering shifts and calling in the evening team an hour earlier can improve the restaurant’s service without unnecessary labor costs.  

6. Considering the Impact of Discounts & Promotions

Discounts and promotions can drive traffic. So, they often justify hiring more staff. A sales by discount report tracks how discounts affect foot traffic and peak sales periods. So, you can adjust staffing before major promotions.   

How to Use It

• If discounts drive volume but not revenue, reconsider staffing for those periods.
• If promotions and combo deals lead to bigger orders, schedule extra staff to upsell additional products.

For example, while running a “Wednesday Discount Day,” a pizzeria POS shows that only a few extra customers come in. Instead of adding staff, the pizzeria can redistribute existing employees to manage stock replenishment and checkout speed.  

7. Monitoring Payment Methods for Checkout Speed Optimization

Different payment methods affect checkout speed. POS data reveals if cash transactions slow down lines during peak hours. Businesses can schedule extra cashiers or promote contactless payment options.

How to Use It

• If self-ordering kiosk experience high usage, fewer cashiers may be required.
• Refunds slow down queues, so assigning staff to handle them at peak times improves efficiency.

For example, a grocery store finds that 70% of evening customers pay in cash, causing long queues. Assigning more cashiers at peak times can speed up the store’s transactions and improve customer satisfaction.

8. Analyzing Customer Demographics & Purchase Patterns

POS data tracks customer demographics, helping businesses adjust staffing based on customer preferences based on the age group. If certain customers shop more at specific hours, trained staff can be scheduled accordingly.

How to Use It

• Senior citizens may shop in the morning, while younger shoppers prefer evenings.
• If new members sign up during specific hours, ensure staff is available to assist them.
• If certain demographics buy in bulk, adjust staffing to manage larger orders.

For example, a beauty specialty store finds younger customers visit in the evening and spend more time browsing. Assigning beauty consultants at those hours can help the store keep service on point and boost sales.

9. Identifying Product-Specific Demand Spikes

Some products sell more during specific hours, creating a need for specialized staffing. POS data highlights these trends to optimize shift planning.

How to Use It

• Some products peak in demand seasonally, requiring temporary staff increases.
• Ensure that premium product sales hours are staffed with experienced sales associates.
• Some products require more preparation, requiring extra staff before rush hours.

For example, a bistro POS shows that their dessert sales peak from 9 PM to 11 PM. To handle demand, the bistro can assign extra staff to their dessert counter during those hours for faster service.

10. Evaluating Online vs. In-Store Order Trends

POS data helps businesses manage online and in-store sales simultaneously. If online orders peak when the store is busy, staff can be redistributed efficiently.

How to Use It

• If third-party delivery orders spike at specific times, ensure kitchen staff is prepared.
• If online carts are abandoned more at night, consider adding late-night support staff.
• If online customers prefer picking up orders at specific times, allocate staff to handle fulfillment efficiently.

For example, a quick service restaurant POS shows high online pre-orders in the morning but peak walk-ins in the evening. Adjusting staff schedules can help the QSR ensure both order types are handled smoothly.

Final Thoughts: Smarter Staffing = Better Business  

Say goodbye to guesswork in staffing. POS data takes the guesswork out of scheduling! By tracking sales trends, order volumes, and employee productivity, you can:

• Cut down on unnecessary labor costs
• Keep customers happy with faster service
• Ensure your staff is working at peak efficiency

Why struggle with overstaffing or understaffing when you can make data-driven decisions? OneHubPOS gives you the insights you need to staff smarter, not harder. Ready to optimize your workforce? Book a demo with OneHubPOS today!