Retail Labor Forecasting: How to Avoid Overstaffing

Written By: Reem Abouemera
Last Updated October 5, 2026

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How many labor hours will your retail store need next week? The number can be hard to pin down.

Customer demand can shift throughout the year, changing both how busy your store is and how much work your team needs to handle. Buddy Punch user data shows just how much this varies: February generally sees 12% less activity than average, while June through August reach up to 16% more than that. That makes it harder to know how much labor you’ll need from one period to the next.

Retail labor forecasting helps you plan for these fluctuations. By estimating the work ahead of time and translating it into labor hours, you can match your labor needs to the work that actually needs to get done. 

In this guide, you’ll learn how to calculate your required labor hours using a practical example and how to keep improving your forecasts.

What retail labor forecasting means and why it matters

Retail labor forecasting helps you turn expected workload into a clear labor requirement. Instead of starting with a staffing number, you estimate the work instead and determine the hours you’ll need to handle it. 

Labor forecasting comes after demand forecasting and before scheduling:

• By forecasting demand, you predict the customer activity and workload you expect, using inputs such as sales, transactions, foot traffic, online orders, and other planned work.
• When you forecast labor, you take that estimated workload and translate it into the labor hours needed to complete the work.
• Then you schedule: Assign employees to those hours based on factors such as availability, skills, and coverage needs.

Getting that middle step right is critical. It can help you:

• Keep labor costs under control by avoiding unnecessary hours and limiting overtime.
• Maintain customer service by making sure you have enough coverage when demand increases.
• Reduce pressure on your team by avoiding shifts with too few employees. 
• Plan more accurately by accounting for both demand-driven work and the tasks that happen regardless of sales volume.

When labor doesn’t match the workload, the effect can extend far beyond labor costs. Brandon Hartman, Founder of Bey Warehouse, describes what this could look like in practice:

“Retailers often have just two employees managing all processes like serving customers, replenishing shelves, packing online orders, and running the checkout, all in the same shift. Something has to give. The customers have to stand in line longer, the shelves remain empty, the employees are blamed for things beyond their control, and the managers are responsible for bad results in customer surveys. The primary way to fix all those problems is to staff for the real workload rather than sticking to the budget.”

Calculating labor requires four components:

The result is a labor requirement, not a finished schedule. Once you know how many hours the expected workload requires, you can move on to deciding which employees should cover them.

How to build a retail labor forecast step by step

Below, we break down each component of the formula and work through the calculation using a simple retail example: 

1. Identify the workload you need to cover

Start by defining the scope of your forecast. Decide whether you’re forecasting hours by store, department, role, day, or time block so you can collect the relevant data.

Next, gather two types of inputs.

Historical inputs:

• Pull sales, transactions, foot traffic, online orders, returns, deliveries, and other relevant activity from your POS, e-commerce, workforce, or operations systems. 
• Use data from the most comparable periods available, such as the same month last year or recent weeks with similar trading patterns. 
• If you’re forecasting for a new store or department with little to no historical data, use relevant data from comparable stores, departments, or trading periods as a starting point and clearly flag those figures as estimates. As the store builds up its own history, use that data to replace your initial benchmarks and refine future forecasts.

Forward-looking inputs:

• Review the upcoming period for anything that could change the expected workload. Check the promotional calendar, planned deliveries, events, holidays, inventory activity, and other scheduled work.

Then, combine these inputs to get your expected workload. 

For example, say you’re forecasting labor needs for a clothing store in September 2026. You can use September 2025 as your main historical reference, supplement it with recent data, and account for planned activities during September 2026: 

*The figures above are illustrative and use simplified assumptions to demonstrate the forecasting process.

2. Convert volume-driven workload into labor hours

Next, you need to convert the workload you calculated above into the labor hours needed to complete it. 

These are volume-driven task hours — labor hours that change as the amount of work changes. 

To calculate them:

• Identify the tasks created by the workload drivers. For example, online orders need to be fulfilled, returns need to be processed, and deliveries need to be received. Other drivers, such as foot traffic, don’t necessarily translate into a specific labor task. Instead, they can affect coverage requirements, such as how many employees you need on the sales floor. 
• Set a productivity rate for each task, using your own historical data where possible. For example, if your team typically processes 1,000 returns in 150 labor hours, you can use the resulting productivity rate (1,000/150 = 6.67 returns per labor hour) as a starting point.
• Adjust the productivity rate when the same task requires different amounts of labor hours during different periods, roles, departments, or stores. For example, if your checkout data shows that employees handle 12 transactions per hour during regular periods but only 10 during peak periods, apply the lower rate to the transactions you expect during those peak periods. 
• Divide the expected workload by the productivity rate to get the labor hours required.
• Add the hours for each task to get your total volume-driven labor requirement.

For our clothing store, we’ll account for lower checkout productivity during peak periods by applying a different rate to those transactions:

That gives the store 1,068 hours of volume-driven labor hours for September.

3. Calculate your fixed coverage

Some labor is required whether your store is busy or quiet. That’s fixed coverage: the labor hours needed to keep the store operating regardless of how much work comes in. 

To calculate it:

• Identify the employees and hours required to open and close the store. 
• Determine your minimum coverage during operating hours. Identify how many employees need to be on the sales floor or in specific departments at any given time to keep the store running.
• Calculate the daily labor hours for each requirement. Multiply the number of employees needed by the hours they need to cover, then add the required hours together.

For our example, the clothing store needs:

Assuming the store operates 30 days a month:

4. Capture the additional workload that sales data misses

Not every hour your team works is tied to fixed coverage or volume-driven workload based on customer demand. Additional workload covers the known operational work that sits outside those two categories, such as replenishment, cleaning, inventory, merchandising, and administrative tasks.

To account for it:

• List the additional activities your team needs to complete during the forecast period. 
• Estimate the workload using the number of tasks, units, hours, or other relevant measures. 
• Set a labor requirement using your historical records or established operating standards.
• Multiply the workload by the labor requirement to calculate the labor hours needed.
• Add the resulting labor hours to calculate your total additional workload.

For our clothing store, suppose the September plan includes the following work outside the fixed and volume-driven tasks:

This adds 221 labor hours to the store’s September forecast.

5. Account for planned changes and contingency

Your forecast so far is based on the workload and labor requirements you expect under normal working conditions. Before you finalize it, account for anything you already know will change that workload, then add a small contingency for factors you can’t predict.

Planned changes  

Planned changes are things you can identify before the forecast period, such as promotions, events, holidays, unusual trading days (like planned changes to store hours), or other changes to normal operations. 

To account for planned changes:

• Identify what will be different from normal operations.
• Estimate how each change will affect your workload or labor requirement. For example, a promotion may increase transactions, while a holiday may increase foot traffic. 
• Add the resulting labor hours to your forecast.

For our clothing store, suppose a three-day promotion is expected to increase checkout volume by 10%. The store normally processes roughly 300 transactions per day, and because the promotion falls during a peak period, we’ll use the 10-transactions-per-hour productivity rate from step three. 

Therefore, the promotion adds:

Across three days, that will mean: 

Add those nine hours to the forecast. Repeat this process for any other planned changes.

Contingency

Next, add a contingency. A contingency is a buffer for unknown factors that could increase the labor you need, such as unexpected demand or unplanned operational issues.

To set a reasonable contingency, compare the forecasts you’ve run until now, using your current processes, with the actual labor that was eventually required during the forecast period. 

• Calculate how much your actual labor needs exceeded the forecasts when unexpected factors occurred. For example, if you forecasted 100 employees but ended up needing 103, your staffing gap was three employees, or 3% higher than forecast.  
• Look at this difference across several comparable periods. If the difference is consistently around 2% to 4%, for example, a contingency of 3% may be reasonable. If it varies widely, investigate what caused the larger gaps before choosing a percentage. 
• Use the percentage that reflects your typical level of uncertainty, rather than automatically choosing a large buffer based on an exceptional event. For example, if your store typically needs 2% to 3% more labor than forecast but once needed 8% because of a major operational issue, a contingency between 2% and 3% would better reflect your typical experience.
• Multiply your labor forecast (after the planned changes) by the contingency percentage to calculate the contingency hours.
• Add the contingency hours to your adjusted forecast to get the final labor hours needed.

For our clothing store, we’ll use a 3% contingency. After the planned promotion, the forecast is:

Then, we’ll apply the contingency: 

Rounded to the nearest whole hour, that’s 64 contingency hours. 

Together, the planned changes and contingency add 73 hours to the forecast.

6. Turn the forecast into a staffing requirement

You’ve now calculated each component of the labor forecast. Bring them together to determine the total labor requirement for the forecast period.

To recap, the formula is as follows:

For our clothing store, the forecast includes:

The store therefore needs 2,202 labor hours to cover its September 2026 workload. That figure then becomes the starting point for building the schedule, where you assign those hours across the days, shifts, and employees. If you need help with that, The End of Fixed Retail Teams walks you through how to build a more flexible retail staffing schedule.

How to improve your forecast over time

It’s normal for your first few forecasts to miss the mark slightly. Over time, you can refine your approach to make them more accurate. Here’s how:

Compare forecasted labor with actual results 

Once the forecast period is over, compare your forecast with what happened in practice. To do this:

• Look at the labor you actually used: Compare your forecasted labor hours with actual hours worked. 
• Look at the operational results: Check whether the team kept up with the workload you expected. Look for signs that the labor level was too high or too low, such as unfinished work, longer customer wait times, or missed service targets. 

For our clothing store, we’d compare the 2,202 labor hours forecasted for September 2026 with the actual hours worked. Suppose the team ended up working 2,250 hours. We’d look at what happened during those hours. For example, whether the store:

• Processed more transactions than forecast
• Spent more time on replenishment, inventory, or other tasks
• Used more labor outside the tasks included in the forecast 

This gives us a good starting point for investigating why the forecast was 48 hours below the actual labor that the store needed.

Identify which assumptions caused the biggest differences

Your goal here is to look for consistent patterns and determine what’s driving the difference. If your forecasts repeatedly miss in the same direction, for example, the issue may be an underlying assumption that no longer reflects your store rather than random variation.

Before building the next forecast, revisit the assumptions. For example, you might find that:

• Your productivity rates are outdated: U.S. Bureau of Labor Statistics data shows that retail labor productivity has changed from year to year: It fell 1.4% in 2022 and then rose 0.7% in 2023, 5.8% in 2024, and 2.9% in 2025. That’s a good reason to review an old minutes-per-task assumption instead of automatically carrying it forward. 
• You relied on generic industry averages: Retail doesn’t follow the same seasonal pattern as every other industry, so a benchmark from another sector may not tell you much about your store. Among Buddy Punch users, retail punch volume peaks in July and bottoms out in February. In comparison, construction peaks in October, healthcare is much flatter throughout the year, and hospitality peaks in July. 
• You assumed your busiest periods based on the calendar alone: Holidays and major shopping events may seem like obvious periods to expect higher labor needs, but your store’s real patterns may tell a different story. For example, for Buddy Punch’s retail users, Black Friday and Christmas don’t produce a punch surge. Instead, summer is the peak period. 
• You assumed your current operating conditions would remain the same: Changes to processes, technology, staffing, or the way work is completed can affect how much labor a task requires. If the way your store operates has changed, that may be a reason your previous assumptions no longer hold. 

In our example, time-tracking data could show that employees are taking longer to complete replenishment tasks than they did when we set the original productivity rate. On closer inspection, we might find that the store introduced an additional step during the year, increasing the time required to complete each task.

In this case, the variance could point to two assumptions that need to be revisited: 

• We assumed the productivity rate would remain unchanged over time.
• We assumed the operating conditions would remain the same.

The 48-hour gap therefore wasn’t really a forecasting error. It reflected changes in how the work was being done.

Use these findings to adjust the next forecast

The point of reviewing your forecast is to carry what you learn into the next one. Here’s how:

• Keep your data up to date throughout the period: Keep your systems updated with the latest retail trends and your own real-time data, rather than waiting until the next forecast period to review what happened in retrospect. The more data you build from your own store, the more confidently you can identify patterns that should shape future forecasts. Buddy Punch can support this by giving you ongoing labor hours data.
• Repeat the process for every forecast period: After each period, compare the forecast with actual results, identify any assumptions that need revisiting, and carry those changes into the following forecast. Over time, this creates a forecast that becomes increasingly grounded in your store’s own operating patterns.

Better labor planning starts with a better forecast 

A good labor forecast shifts the question from “How many employees do we usually schedule?” to “How much labor will this workload actually require?” That shift helps you avoid paying for labor you don’t need while making sure you have enough staff to handle the work. 

As a next step, pick up a past forecast and compare what you expected with what truly happened, then use those findings to identify which assumptions need to change. That gives you something concrete to build on when you forecast the next period.

Contributors 

• Brandon Hartman, Founder, Bey Warehouse
• Rachid Wehbi, Founder and CEO, Sell The Trend

About the Author

Reem Abouemera is an HR and workplace-technology writer with seven years of experience creating content for HR platforms and people-operations teams. With a background in business administration and leadership roles in content operations, she brings a strong understanding of how HR tools are evaluated, implemented, and communicated. Reem combines real-world experience with practical insights to produce clear, trustworthy guidance on workforce systems and everyday operational challenges.