Latest / Franchise Conversations with Fexingo: Buying, Running, and Scaling Franchise Businesses / How Franchisees Are Using Revenue Intelligence
Transcript
- Lucas: So you walk into a franchise location — could be a sandwich shop, a gym, an oil change place — and the owner can tell you exactly what they sold yesterday, probably down to the penny. But ask them what they should have sold, and you get a blank stare. Luna: That gap — between what you actually sold and what you could have sold — is where revenue intelligence lives. Lucas: Exactly. And it's a concept that's been gaining traction in the franchise world over the last couple of years. The basic idea is simple: use data — your own transaction history, customer behavior, maybe even local economic indicators — to identify specific opportunities to increase revenue without adding new locations or buying more equipment. Luna: If today's episode helps you think differently about that gap, we hope you'll consider supporting the show. The reason Franchise Conversations stays ad-free is listener support — buy me a coffee dot com slash fexingo. It's how we keep these conversations going. Lucas: Yeah, genuinely — if this has moved your work forward in some small way, that link is buy me a coffee dot com slash fexingo. Now, let's get into how one franchisee actually did this. Luna: Walk us through the case. Lucas: So there's a multi-unit franchisee in the quick-service restaurant space — they operate about 15 locations of a national chain in the Midwest. They've been around for a decade, good operator, solid margins. But they hit a plateau. Same-store sales were flat for two years, and they couldn't figure out why. Luna: Flat for two years — that's frustrating. What did they try first? Lucas: The usual stuff — they ran more local ads, tried limited-time offers, even tweaked the menu. Nothing really moved the needle. So they hired a data consultant, someone who specialized in what's called revenue intelligence. The consultant spent a month pulling together their POS data, their loyalty program data, and even some weather data — because for this chain, weather affects traffic. Luna: Weather data — interesting. So they were looking for patterns that weren't obvious day to day. Lucas: Right. The key finding was about pricing. This franchisee had a standard pricing structure across all 15 stores — every sandwich cost the same at every location. But the data showed that certain stores, in higher-income areas, had customers who were less price-sensitive on specific items. Those customers were ordering less because they perceived the value wasn't there at the standard price. Luna: So the solution was to raise prices at some stores? That sounds risky. Lucas: It does, and they tested it carefully. They used a machine learning model that predicted the optimal price point for each item at each location, taking into account local demographics, competition, and historical purchase behavior. They started with a pilot at three stores, raising prices on about 20 percent of the menu by an average of 4.5 percent. Luna: And what happened? Lucas: Over six months, those three stores saw same-store sales increase by 8 percent — and the margin improvement was even bigger because the cost of goods stayed the same. The other 12 stores, which kept standard pricing, remained flat. So the franchisee rolled out the optimized pricing to all locations. That's revenue intelligence in action — using data to find the price that maximizes revenue, not just the one that's easiest to manage. Luna: But isn't this just dynamic pricing? Like what Uber does? Lucas: Similar concept, but with a key difference. Dynamic pricing changes in real time based on demand. Revenue intelligence here is more about structural pricing — setting the right base price for each location and then maybe adjusting seasonally. It's not about surge pricing your lunch customers. The franchisee never changed prices during the day, just set them differently per store. Luna: Got it. So it's more like micro-segmentation on price. How do smaller franchisees — say someone with just one or two units — get started with this? They probably don't have a data consultant on speed dial. Lucas: Fair question. The good news is you don't need a consultant to start. Most POS systems already capture transaction-level data — what was sold, at what price, when, and often with customer identifiers if you have a loyalty program. The first step is just to export that data into a spreadsheet and start looking for patterns. Luna: Patterns like what? Lucas: Look for items with low price elasticity. If you raise the price of a coffee by twenty-five cents and sales barely drop, you have pricing power. Also look at time-of-day patterns — maybe your lunch rush is busy but your afternoon snack window is dead. Revenue intelligence would suggest a targeted discount or a bundled offer for that dead window, not a blanket promotion. Luna: So it's as much about when to discount as when to raise prices. Lucas: Exactly. The consultant I mentioned actually found that this franchisee was over-discounting on certain combo meals during off-peak hours. The discounts were costing them margin without driving enough incremental traffic. So they cut the discounts and instead used a simple loyalty punch card — buy nine, get the tenth free — which increased visit frequency without eating into per-visit revenue. Luna: That's a good example of a low-tech revenue intelligence move. Any other tools you've seen franchisees use? Lucas: There's a growing ecosystem of software platforms that do this. Some are built specifically for franchises — they integrate with major POS systems like Toast, Square, or Clover, and they use AI to generate pricing recommendations and promotional strategies. A few are even starting to incorporate external data like local employment rates or foot traffic from mobile devices. Luna: What's the typical cost for a franchisee? Lucas: It varies widely. For a single unit, you might pay a few hundred dollars a month for a basic revenue intelligence dashboard. For a multi-unit operator, it can be a few thousand. But the ROI can be significant — even a 2 to 3 percent lift in same-store sales can justify the cost quickly, especially if margins are thin. Luna: Are there any pitfalls? I imagine it's easy to over-optimize and alienate customers. Lucas: Absolutely. One common mistake is ignoring local context. A model might tell you to raise prices, but if your store is in a small town where everyone knows each other, a price hike could backfire socially. Another pitfall is data quality — if your POS data is messy or incomplete, the insights will be garbage. You need clean, consistent data. Luna: So it's not just about having the tool, it's about having good data habits. Lucas: Right. And the third pitfall is implementation. Even if the model is perfect, if the franchisee doesn't train their staff to explain the new pricing or if they change prices too often, customers get confused and annoyed. Revenue intelligence works best when it's applied thoughtfully and incrementally. Luna: What about the franchisor? Do they typically support this kind of store-level pricing variation? Lucas: It depends on the system. Some franchisors mandate uniform pricing across all locations. That's a hard constraint — you can't do store-level revenue intelligence if you can't change prices. But many franchisors now allow a pricing band — for example, you can set prices within plus or minus 5 percent of the recommended price. The franchisee I mentioned worked within that band. Luna: So there's a conversation to be had with the franchisor early on. Lucas: For sure. And in some cases, the franchisor itself is starting to offer revenue intelligence tools as part of their tech stack. I know of at least two major QSR chains that are rolling out analytics dashboards to their franchisees this year. They see it as a way to improve system-wide performance without raising royalty fees. Luna: That's interesting — the franchisor benefits when franchisees are more profitable. Lucas: Exactly. Higher franchisee profitability means lower churn, more reinvestment, and ultimately higher royalty revenue for the franchisor. So it's aligned. The challenge is getting franchisees to actually adopt and use these tools. A dashboard is useless if nobody looks at it. Luna: What's the one takeaway you'd want a franchisee listening to walk away with? Lucas: Start small. Pick one location, export six months of POS data, and find one item that seems underpriced. Test a small increase — say 3 percent — and track the impact for two months. If it works, you've got a replicable process. If it doesn't, you've learned something without betting the farm. Luna: That's practical. And I'd add: don't forget the human element. Data can tell you what, but it can't tell you why. Talk to your customers, listen to your staff. Combine the quantitative with the qualitative. Lucas: That's a perfect way to put it. Revenue intelligence is a tool — a powerful one — but it's still just a tool. The best franchisees use it to inform their judgment, not replace it. Luna: Alright, that's a good place to wrap. Thanks for joining us on Franchise Conversations with Fexingo. Lucas: See you next time.