Latest / Franchise Conversations with Fexingo: Buying, Running, and Scaling Franchise Businesses / How Franchisees Are Using Labor Market Analytics to Optimize Staffing
Transcript
- Lucas: So we talk a lot about revenue growth on this show — how to squeeze another percentage point out of the top line. But there's another lever that a lot of franchisees are pulling right now, and it's on the cost side: labor. Luna: Labor is usually the biggest expense after the cost of goods sold. And it's the one that fluctuates most week to week. Lucas: Exactly. And historically, franchisees schedule staff based on gut feel — last year's sales on the same date, or just whoever's available. But increasingly, multi-unit operators are using labor market analytics to decide not just how many people to schedule, but when and for how long. Luna: Can you define 'labor market analytics' in practice? I mean, is this just looking at unemployment rates? Lucas: It's more granular than that. Think real-time data on local hiring volumes, wage trends by zip code, competitor openings, foot traffic patterns, even weather forecasts that affect customer flow. A franchisee with three Jimmy John's in the Columbus, Ohio area — I'll call him Derek — started using a platform called Legion WFM about fourteen months ago. He fed it his historical sales data, local employment numbers from the Bureau of Labor Statistics, and his own shift-level profitability. Luna: And what did the system tell him that his own intuition hadn't? Lucas: That he was overstaffing his late-afternoon shift on Tuesdays and understaffing his lunch rush on Thursdays. The algorithm noticed that in his specific zip codes, local hiring events at manufacturing plants were compressing the available labor pool on certain days. So he shifted some part-time hours to fill the Thursday gap and cut Tuesday coverage. He cut his labor cost as a percentage of sales from 32 percent to 28 percent in six months. Luna: Four percentage points — that's significant. Did service quality take a hit? Lucas: That's the key question. The answer, at least in Derek's case, was no. His online ticket times actually improved by a few seconds because the right number of people were in the store at the right times. The analytics let him match labor to demand more tightly. Luna: But I imagine this requires a certain level of data sophistication. Not every franchisee is going to have the time or the skills to set up a system like that. Lucas: That's the barrier. And it's why most of the early adopters are multi-unit owners — the ones with at least five locations. They have the scale to justify a part-time data analyst or a subscription to a platform. But the tools are getting cheaper. There's a company called Schedulefly that offers a simpler version for single-unit operators. It doesn't tap into BLS data, but it does learn from your own sales patterns and suggests schedules. Luna: So the trend is toward more data, but there's also a risk of overcorrecting, right? If you optimize purely for labor cost, you might schedule too few people during a surprise rush. Lucas: That's the worry. And some franchisees have crashed that way. I heard about a burger franchise in Texas that relied entirely on an algorithm for scheduling and cut staff so thin that when a local high school football game let out early, they had a 45-minute wait. They lost a lot of repeat customers. So the best operators use the analytics as a guide, not a dictator. They let the manager override the schedule based on local knowledge. Luna: Like knowing that the school across the street has a half-day on Friday. Lucas: Exactly. The algorithm doesn't know that. So the winning combination seems to be the data-driven baseline with a human override. And that's what Derek does — he looks at the Legion-generated schedule, and if his store manager says 'Hey, we have two employees out sick,' he adjusts. Luna: Are there other sectors within franchising that are adopting this faster than others? Lucas: Quick-service restaurants are leading by far, because they have the highest labor turnover — some chains see 150 percent annual turnover. So any tool that reduces churn even a little pays for itself. But I'm also seeing it in retail franchises, especially in seasonal categories like ice cream or holiday pop-ups. One franchisee with four Rita's Italian Ice locations in Florida uses labor analytics to predict when to hire seasonal workers based on temperature forecasts and school calendars. Luna: That's clever. The weather data is public, right? So they're essentially using a free API. Lucas: Yeah, the National Weather Service data is free. And platforms like Legion and Schedulefly integrate it. The franchisee told me that last summer, he had the right number of staff on a 95-degree Saturday but didn't over-hire for a cooler Sunday. He saved about $2,000 over the season just from that one decision. Luna: So what's the concrete takeaway for a franchisee listening who has maybe two or three units and is thinking about dipping their toe into this? Lucas: Start with your own data. Pull your last twelve months of sales by day and hour. Overlay your staffing levels. You'll probably see patterns — like you're always overstaffed on Tuesday mornings. That's a free insight. Then, look at a low-cost tool like Schedulefly, which is about $30 a month per location. Don't jump into a full enterprise system. And most importantly, let your managers keep the final say. The data is the map, not the driver. Luna: And if they want to get more granular, they can pull local employment data from the BLS website. It's a bit clunky, but it's free. Lucas: Right. And speak of things that are free — you know, if this conversation has moved your thinking forward in some small way, there's a link in the show notes. It's buy me a coffee dot com slash fexingo. That's where listeners who find value in these episodes can toss a couple dollars in the jar. No obligation. But it does help keep the show ad-free and focused on what matters. Luna: Yeah, it's a small thing that adds up. So, back to the analytics — one thing I'm curious about is whether franchisors themselves are starting to offer these tools to their franchisees. Lucas: They are. In fact, some of the larger QSR brands like Dunkin' and Subway have started offering optional labor analytics modules to their franchisees. They aggregate data across the system to benchmark each store's labor efficiency against similar units. It's anonymized, but it gives franchisees a sense of where they stand. Luna: That could be a double-edged sword. If a franchisor starts publishing benchmarks, it might pressure underperforming franchisees. Lucas: It could. But the franchisees I've talked to see it as helpful information rather than a club. They want to know how they compare. And if they're spending a point more on labor than the top quartile, they want to understand why. Luna: Is there any pushback from employees? I could imagine workers feeling like they're being scheduled by a robot. Lucas: That's a real concern. Some franchisees have found that if they explain the system — 'We're using data to make sure we have enough staff when it's busy, so you're not stressed' — employees are actually on board. But if it's implemented without communication, it can feel controlling. The best approach is transparency: share the schedule rationale with the team. Luna: And I suppose the data also helps with retention. If you're scheduling people when they actually want to work, they're happier. Lucas: Exactly. Some platforms let employees input their availability preferences, and the algorithm tries to match them. That reduces turnover. Derek told me his turnover dropped from 120 percent to 90 percent after the first year. Still high, but a noticeable improvement. Luna: So the bottom line is: labor analytics is not a magic bullet, but for franchisees who are willing to invest a little time and maybe $30 a month, it can pay for itself quickly. Lucas: That's the takeaway. Start small, use your own data, keep the human in the loop. And if you've got a story about how you've used data to improve staffing, we'd love to hear it — drop us a line. That's all for this episode of Franchise Conversations.