Knowledge is power – and the more you know, the better decisions you can make. That’s why big data has come to the grocery store, as grocers see the value in using artificial intelligence (AI) to run their operations more efficiently.
AI is also powerful in optimizing labor models. Its granular forecasting capabilities lead to more intuitive scheduling, segmented by season, day of the week, and even individual department demand within dayparts.
This article highlights what you need to know to generate more accurate labor forecasts that enhance customer service and provide a better return on staffing investments. Readers will learn:
- How to use immense amounts of data to recognize patterns and derive insights to help with tasks like anticipating inventory and ordering needs
- How AI can help grocers make wise pricing and promotional decisions
- Why AI improves employee satisfaction
Table of contents
Knowledge is power, and the more you know, the better decisions you can make. That’s why big data has come to the grocery store, as grocers see the value in using artificial intelligence (AI) to run their operations more efficiently.
By culling massive amounts of data to recognize patterns and derive insights, AI helps grocers with tasks such as anticipating inventory and ordering needs and making wise pricing and promotional decisions.
But grocers are also finding AI to be particularly powerful in optimizing labor models. Its granular forecasting capabilities lead to more intuitive scheduling, segmented by season, day of the week, and even individual department demand within dayparts.
The result of a more accurate labor forecast is enhanced customer service, more satisfied employees, and a better return on staffing investments. Want to put the power of data behind your forecasting? Here’s what you need to know.
Fluctuating traffic patterns = complicated forecasting
It’s no secret that grocers have a unique labor challenge, given that they notoriously operate on wafer-thin margins — much smaller than other forms of retail. “They have to be far more focused on the labor aspect because truly every dollar counts,” says Chase Austell, practice manager for UKG in the retail, hospitality, and food services sectors.
But forecasting can be tricky, given the varying traffic patterns grocery stores encounter. Among the challenges are:
- Department needs: The deli might be extra busy in the morning and then again at lunch; curbside pick-up might spike during the dinner rush.
- Holidays: Shopping patterns vary widely in the days leading up to and following holidays, but they can be hard to predict. “Seasonality can be tough for grocers because holidays aren’t standard; they fall on different days of the week or at different times of the month from year to year,” notes Meghan Sparks, retail consultant for UKG.
- Payday periods: Grocers also see demand rise at certain periods when paychecks or government aid is distributed, and recipients can replenish their pantries and refrigerators.
- Promotions: Certain promotions drive traffic in droves, which can spur outsized demand in a particular department, as well as at check stands. “One grocer recently said their traffic will surge the day their ad comes out announcing that ribeyes are on special,” says Sparks, as an example.
Machine learning allows grocers to monitor traffic patterns and fine-tune the forecast to identify these peaks and valleys. “Static historical algorithms have a hard time picking up on these variations, whereas workforce management applications that use AI and machine learning do that quite easily,” Sparks says.
But the programs are only as good as the data you give them. “Machine learning is extremely data-hungry — the more data you can give it, the better it will learn,” she says, recommending that grocers start with a minimum of two years of historical data, while up to four years is even better. The data can be mined from the point-of-sale system, making it easy to access.
“None of our customers will have the same algorithm because each store has its own variables,” Sparks says. Historically stores would have applied the same formula to different locations, giving average results that were workable. However, using AI to detect patterns allows the manager to make more accurate decisions, to predict and fine-tune forecasting for each location based on the qualities that make it unique from others.
None of our customers will have the same algorithm because each store has its own variables.” – Meghan Sparks, Retail consultant for UKG
It’s important to note that forecasting can be more challenging than ever these days, as shoppers’ patterns have fluctuated dramatically during 2020, with increased demand from online shopping and curbside pick-up upending traditional staffing needs.
“You can’t just take your old labor model and expand it across the board,” says Austell. “You have to rethink the way you are allocating your hours and apply new metrics around processes that didn’t previously need them.”
For example, he cites one grocer whose curbside pick-up volume increased from 25 to 150 orders a week, compelling them to incorporate new strategies to staff a side of the business that hadn’t existed at such scale.
As shoppers continue their quest for a contactless experience and recognize the convenience of placing an order for pick-up, these patterns are likely to represent a shift in how grocery stores will operate going forward, becoming one more metric that needs to be addressed.
You can’t just take your old labor model and expand it across the board. You have to rethink the way you are allocating your hours and apply new metrics around processes that didn’t previously need them.” – Chase Austell, Practice manager for UKG in the retail, hospitality, and food services sectors
Improving the forecast translates into better scheduling
More accurate forecasts, achieved through machine learning, allow grocers to optimize labor and get the right person with the right qualifications to the right position at the right time.
But Austell finds that scheduling remains a laborious manual effort for many store managers. “They’ll arrive at their numbers by referencing volume during that same time period over the last couple of years and extending it to the current week; then they’ll plug names into a spreadsheet or even handwrite a chart to post on the break room wall.”
However, he sees the tide turning, with increased interest in automating the process. “Grocers are realizing that AI can produce more strategic results, and automation will cut down on the time-consuming task of manually creating a schedule,” he says. That means they can reallocate their time too valuable tasks such as coaching associates or helping customers.
Sparks has seen that using an automated workforce management application can reduce the forecasting task from two days to two hours or from two hours to 15 minutes, depending on the complexity of a grocer’s scheduling.
“With that solid forecast in hand, the manager can create an optimized schedule that takes all complexities and compliance concerns into account, without the risk of human error,” Sparks explains. “The manager can then take the generated schedule and adjust as needed to ensure it covers all the bases.”
This sophisticated method translates into a suitable workforce every hour of the day, which allows grocers to optimize the wages allocated for each shift and increase customer loyalty through better, more efficient shopping experiences.
With that solid forecast in hand, the manager can create an optimized schedule that takes all complexities and compliance concerns into account, without the risk of human error.” – Meghan Sparks, Retail consultant for UKG
AI improves employee satisfaction
While an improved forecasting and scheduling model will provide an enhanced customer experience, it has benefits for associates as well.
“Proper staffing means that employees aren’t working in a stressful, hectic environment, nor are they standing idle,” Austell says. The right schedules, or the right mix of schedules, can be a positive impact on employee wellbeing. It may be impossible for a manager to keep track of how often an employee had to “clopen” — close the last shift and open the next one — but the effects can wear on the employee and affect retention.
AI can help make self-service easier, which gives employees more control over their preferences. For example, UKG Dimensions™ allows employees to take the initiative to make schedule changes and find the flexibility that works for them.
Since today’s society is used to conducting the majority of tasks on mobile devices, people expect the same intuitive experience from business applications as from consumer apps. That’s where the AI that powers UKG Dimensions™ steps in and picks up on past behaviors to present better choices, making it easier to conduct tasks like swapping or picking up shifts straight from the phone.
So, rather than the frustration of scrolling through all available coworkers and shifts, the system can predict what changes the employee might be looking for and will first present the names of coworkers with whom they’ve switched in the past, as well as preferred shift times that meet previous qualifications.
The platform will subsequently account for these employee changes in the scheduling function to help the manager make better predictions regarding who can work which shifts, thus generating a more efficient schedule upfront.
“We see this as an important tool for employee engagement because they’re getting more of the type of work they want to do at more convenient days and times,” Austell says. It provides heightened work/life balance and makes it less likely someone will abandon a shift.
“Some of the grocers I work with knew they were behind as a competitive employer since the best people have their choice of where to work,” says Sparks. “They saw a benefit to offering their team more control over their schedule with this platform that’s easy to use and easy to access on their phone.”
We see this as an important tool for employee engagement because they’re getting more of the type of work they want to do at more convenient days and times.” – Chase Austell, Practice manager for UKG in the retail, hospitality, and food services sectors
Putting AI on your shopping list
As grocers strive to maximize their workforce and improve customer service, they can use a boost — and AI and ML can come to the rescue, just as they help optimize supply chain and other operational functions. Using a platform like UKG Dimensions™ gives grocers increased insight into how they can more accurately forecast demand, then generates a schedule that allows them to assign shifts to qualified, available employees.
“The industry is really starting to embrace the benefits of machine learning in regards to customer and employee satisfaction, while simultaneously reducing the burden on managers,” Austell says. “When organizations see their labor is not aligned with sales, it’s clear they need to make adjustments to stay competitive, and they’re beginning to understand how they can leverage AI to meet those benchmarks more efficiently.”
The result? By using AI, grocers can streamline their operation so it runs more efficiently, while at the same time offering a more rewarding workplace for employees and a better experience for the customer.