Latest / Franchise Conversations with Fexingo: Buying, Running, and Scaling Franchise Businesses / Franchisees Are Using Revenue Intelligence to Predict Unit Performance
Transcript
- Lucas: There is a tool that a growing number of franchisees are using that I don't think gets enough attention. It's called revenue intelligence — basically using external data streams to predict how a given unit will perform before you even sign the lease. Luna: I've heard the term thrown around in the context of corporate retail, but less so among franchisees. What does it actually look like in practice? Lucas: So imagine you're looking at a bagel chain. Not a huge national name, but a regional player with about 40 locations. A franchise group I spoke with recently — let's call them the Bagel Group — was evaluating a handful of underperforming stores. Same brand, same menu, same operational playbook. But some stores were struggling. Luna: And they wanted to know which ones were worth investing more into versus cutting loose. Lucas: Exactly. So instead of relying on gut feel or just looking at last quarter's P&L, they built a revenue intelligence model. They pulled in foot-traffic counts from a third-party data provider, local employment data from the Bureau of Labor Statistics — at the zip-code level — and competitor pricing from mystery shops. Then they layered in their own store-level transaction data. Luna: So they were basically creating a predictive engine that said, 'Given these external conditions, a store should be doing X in revenue.' Lucas: That's exactly it. And what they found was surprising. One store in a mid-sized Ohio town was underperforming by about 20 percent versus the model's prediction. The franchise group had assumed it was a location problem. But the model said the foot traffic was fine — actually above average — and local employment was stable. Luna: So what was the disconnect? Lucas: Competitor pricing. A new Dunkin' had opened a mile away and was running aggressive $2 coffee promos. The bagel store had not adjusted its pricing or its own promotions. The model flagged that the revenue gap was almost entirely explainable by that one variable. Luna: So the fix wasn't closing the store or moving — it was a pricing and marketing response. Lucas: Right. They introduced a $1.99 coffee and bagel combo, matched the competitor's price point, and within three months that store was tracking at the model's predicted revenue line. The franchise group ended up investing in three more locations in the same metro area based on the model's signal. Luna: That's a concrete win. But I imagine the model is only as good as the data you feed it. What about franchisees who don't have clean unit-level transaction data? Lucas: That's the rub. A lot of franchisees operate on point of sale systems that aren't standardized across the system. Or the franchisor doesn't share granular data. In those cases, you're relying on estimates and external proxies. It's still useful, but you lose predictive power. Luna: There's a franchisee I know who runs a dozen sandwich shops in the Southeast. He told me he tracks his own data on a spreadsheet — literally manual entry — because the franchisor's system doesn't give him daily sales by hour. Lucas: Yeah, and that's a huge missed opportunity. Revenue intelligence really shines when you have daily or weekly data. That Bagel Group was lucky because their franchisor had recently upgraded to a cloud-based POS and gave franchisees access to the API. Luna: So the franchisor's tech stack matters a lot here. If you're a prospective franchisee shopping for a brand, this might be a question to ask during due diligence. Lucas: Absolutely. Ask: 'Can I get unit-level transaction data in a machine-readable format? And can I layer third-party data on top of it?' Some of the more forward-thinking franchisors are actually building these models themselves and offering them to franchisees as a service. Luna: There's a fast-casual brand called Sweetgreen that does something like that — they have an analytics platform for their operators. But that's a corporate chain, not a franchise system. Still, the idea is spreading. Lucas: It is spreading. And it's not just for new location decisions. Some franchisees use it for ongoing performance monitoring. They set up alerts: if actual revenue deviates more than 10 percent from the model's prediction for two consecutive weeks, they investigate. It's like a check engine light for the business. Luna: That's smart. But I want to push back a little. Is there a risk that franchisees become too reliant on the model and ignore local knowledge? Like, maybe the foot traffic data says one thing, but the store manager knows the local factory just laid off 200 people. Lucas: That's a fair point. The model is a tool, not a replacement for judgment. In the Bagel Group case, they actually overrode the model once. It flagged a store in a college town as high potential, but the local franchisee knew the university was switching to a food delivery contract with a different vendor. So they held off. Luna: So the best use is a blend. Model plus on-the-ground intuition. Lucas: Exactly. The franchisees who are getting the most out of revenue intelligence are the ones who treat it as a conversation starter, not a verdict. It tells you where to look, not what to do. Luna: I want to circle back to something you said earlier about the Bagel Group's model including competitor pricing. How do they get that data reliably? Mystery shopping every week can get expensive. Lucas: They used a mix. For the initial analysis, they did a one-time sweep. Then they set up a subscription to a service that scrapes public menu boards from competitor websites and Google Business profiles. It's not perfect — sometimes prices are out of date — but it's good enough to spot trends. They also trained their store managers to report competitor prices whenever they visit for personal errands. Luna: Crowdsourcing from your own team. I like that. Low-cost, continuous, and it keeps managers engaged with the competitive landscape. Lucas: Right. And it feeds back into the model. The more data points you have, the better the predictions get. Some franchisees are even sharing data among themselves in informal co-ops. A group of ten franchisees from different brands in the same region pooled their foot traffic data to get better coverage. Luna: That's interesting. But I imagine there are antitrust concerns if they start sharing pricing data. Lucas: Yeah, you have to be careful. The co-op I mentioned is sharing only foot traffic counts, not pricing or revenue. So it's fine. But it's a reminder that revenue intelligence isn't just a technology problem — it's also a governance and trust problem. Luna: Let's talk about cost. How much does it take to set up a basic revenue intelligence capability for a small franchisee with maybe three stores? Lucas: Surprisingly affordable. The Bagel Group spent about $15,000 upfront on data purchases and a part-time data analyst for three months. After that, ongoing costs were around $500 a month for the data subscriptions. For a multi-unit operator, that's a rounding error if it helps avoid one bad location decision. Luna: And for a single-unit franchisee? Might be harder to justify. Lucas: For a single unit, you could start with just the free or low-cost data sources. The Bureau of Labor Statistics data is free. Google Maps popularity data gives you foot traffic proxies. You can build a simple model in a spreadsheet. It won't be as powerful, but it's better than nothing. Luna: You know, these conversations we have — they often hinge on data and better decision-making. And we try to keep them accessible and ad-free because we think that's how they're most useful. Lucas: Yeah, we've never run ads on this show, and we plan to keep it that way. If these episodes have helped you think about your franchise operations differently, and you want to support that choice, the link is buy me a coffee dot com slash fexingo. That's buy me a coffee dot com slash fexingo. Luna: And it genuinely makes a difference. Every contribution helps us keep doing the research and bringing you these stories. Lucas: Okay, back to revenue intelligence. One development I'm watching is the emergence of 'franchise intelligence platforms' — companies like FranData and others that are essentially offering revenue intelligence as a subscription for franchisees across multiple brands. Luna: So you don't have to build it yourself. You just plug in your store IDs and they give you a dashboard. Lucas: Exactly. They aggregate data across the industry, so you can benchmark your store against similar units in similar geographies. That's powerful. One franchisee I spoke with uses it to set annual revenue targets — not based on what the franchisor says, but on what the model says is achievable given local conditions. Luna: That shifts the conversation with the franchisor, too. Instead of being told 'you need to hit this number,' you can say 'here's what the data says is realistic based on my market.' Lucas: It levels the playing field a bit. Of course, not all franchisors love that. Some see it as franchisees second-guessing their system. But the smart ones embrace it because better-informed franchisees make better decisions. Luna: So where do you see revenue intelligence heading in the next year or two for franchisees? Lucas: I think it becomes table stakes. Within two years, a franchisee who isn't using some form of external data to validate their unit economics will be at a clear disadvantage. The cost of data is dropping, and the tools are getting easier. It's not just for big operators anymore. Luna: And the Bagel Group — are they still using it? Lucas: They've expanded it to all 40 locations. They now have a monthly 'revenue intelligence review' where they go store by store. They've caught two more underperformers early and made adjustments before they became problems. The model paid for itself in the first year, easily. Luna: That's the kind of story that makes you think: what other parts of franchise operations could benefit from this same data-driven mindset?