Latest / Franchise Conversations with Fexingo: Buying, Running, and Scaling Franchise Businesses / How Franchisees Use Geolocation to Boost Sales
Transcript
- Lucas: You know how franchisees often rely on the franchisor's site selection team to pick locations? Well, some top franchisees are taking site selection into their own hands—using customer geolocation data from their own loyalty apps to figure out where to open next, and more importantly, where to market. Luna: That's interesting. I always thought site selection was mostly demographic data from the franchisor. How would a franchisee even get that kind of data? Lucas: It starts with the loyalty app. When a customer opts in, the app collects location data anytime they're near a store or check in. The franchisee can aggregate that data—anonymized—to see clusters of where customers are coming from. One multi-unit operator of a fast-casual chicken chain with 12 locations in the Southeast did exactly that. Luna: And what did he find? Lucas: He used a third-party tool that cost about five hundred dollars a month. It mapped all his app users' home and work neighborhoods. He discovered that a huge portion of his lunch crowd came from a specific industrial park three miles from his busiest store, but those customers rarely visited the store closest to the park. So he created a localized Facebook ad campaign targeting that park—offering a lunch special if they came to the nearer location. Luna: Smart. So instead of blanketing the whole city, he went hyperlocal. Lucas: Exactly. Over six months, same-store sales at that location grew 22 percent year-over-year. And he didn't even open a new store—he just redirected marketing spend based on where his existing customers actually lived and worked. Luna: What did the franchisor think? I imagine their site selection team might push back on a franchisee doing their own geography analysis. Lucas: There was some friction. The franchisor's national model said that area was already saturated. But the franchisee argued that the national model doesn't capture local nuances—like the fact that the main road was being widened and traffic patterns were shifting. He showed them the data, and eventually they gave him the green light to test a secondary location nearby. Luna: So it's a tool for influencing, not necessarily replacing, the franchisor. Lucas: Right. The data is also useful for lease negotiations. Another franchisee I talked to used geolocation heat maps to prove to a landlord that a specific shopping center had a high density of his target customers. He got a 15 percent rent reduction for the first two years. Luna: That's a tangible win. But I have to ask about privacy. Collecting customer location data—that's a sensitive area. Lucas: Absolutely. The key is anonymization and opt-in. The apps I'm talking about require explicit consent, and the data is aggregated before the franchisee ever sees it. You're not tracking individuals—you're looking at trends in a zip code. Still, franchisees need to be transparent in their privacy policies. Luna: That makes sense. So what tools are they using? Lucas: A popular one is a platform called QuikGraph—it's built for franchisees. Or some use a combination of Google's BigQuery with a visualization tool like Tableau. But the simplest approach is using the loyalty app's own backend analytics, if it provides exportable customer location data. Luna: And the cost? You mentioned five hundred a month for one operator. Lucas: That was for a mid-tier tool with a limited number of stores. For a single-unit franchisee, it can be as low as a hundred dollars a month. The ROI is usually immediate if you shift ad spend from broad to targeted. Luna: It's worth it if you can get even a fraction of that 22 percent lift. Lucas: And it's not just for quick-service. A fitness franchisee used geolocation to figure out which zip codes had members who were most likely to refer friends. He then ran a referral campaign targeted to those areas and saw a 30 percent increase in new membership sign-ups. Luna: So it's basically understanding your customer geography better than the franchisor's generic model. Lucas: Exactly. The franchisor's model is based on average demographics—income, population density, competitor proximity. But your actual customers might not fit that average. You might have a store in a low-income area but pull customers from a higher-income neighboring suburb because of a unique menu item. Luna: That's a strong argument for using your own data. Lucas: And once you have that data, you can also use it to schedule staff. If you know lunch peaks come from a certain office park, you can align labor with that traffic pattern. Luna: Alright, I'm sold. But what about franchisees who don't have a loyalty app? Lucas: You can still get location data from online orders or delivery partners. Many third-party delivery apps provide aggregated data on where orders are coming from. Or you can run a simple promotion: 'Show us your zip code for a free drink' and collect that manually. Luna: Low-tech, but effective. Lucas: The point is, geolocation is underutilized in franchising. Corporate often guards that data, but if you have a direct relationship with your customers, you can build your own dataset. Luna: I'm thinking about the next step: could franchisees use this data to decide when to exit a location? Lucas: Absolutely. If the data shows your customer base is moving further away, that's a red flag. One franchisee used declining geolocation density as a signal to sell his restaurant before the downturn hit. He got a better price because he had the data to back up his reasoning. Luna: So it's a tool for both offense and defense. Lucas: Right. And the best part is, you can start small. Pick one location, download your app data, and map it. Even if you have just a few hundred customers, you'll see patterns. Luna: If you found that practical example useful—and I hope you did—you know our show is ad-free. The only way it stays that way is through listener support. If today was worth a coffee to you, that's the link: buy me a coffee dot com slash fexingo. Lucas: It genuinely helps us keep doing these deep dives. And now, back to geolocation—another franchisee used a similar approach to negotiate better lease terms by showing a landlord the density of their best customers. Luna: Right, you mentioned that earlier. The 15 percent rent reduction. Lucas: Yes. So this isn't just marketing—it's a strategic asset. I think we'll see more franchisees pushing for data rights in their franchise agreements. Luna: That's a whole other episode, I'm sure. Lucas: Indeed. For now, the takeaway is: you don't need to be a data scientist. Just start mapping where your customers are and ask yourself if your marketing and store locations align with that map. Luna: I'm going to try that with my own small business. Thanks, Lucas. Lucas: Glad to help. That's it for today—see you next time.