How a Square Seller Discovered Hidden Insights Using Location Analysis

I was surprised to learn this about which location makes the most money...

I was surprised to learn which location makes the most money wasn't our flagship downtown store. Not even close.

For three years, we poured resources into our main location—the one with the premium rent, the beautiful storefront, the foot traffic. Meanwhile, our smaller suburban shop quietly outperformed it by 40%. We had no idea until we finally ran a proper location analysis on our Square data.

Here's how that discovery changed everything.

The Challenge: Flying Blind Across Multiple Locations

When Sarah, a coffee shop owner I work with, expanded from one location to three, she thought she had a pretty good handle on how each store was performing. She'd check the daily totals in her Square dashboard, compare them mentally, and make decisions based on gut feeling.

"I assumed our original location was still the breadwinner," she told me during a consultation. "It had been our only store for five years. It felt like the heart of the business."

But when we sat down to actually analyze her location data systematically, the picture that emerged was completely different. Her original location? Third place out of three. Not just by a little—it was generating 35% less revenue than her newest location, which had only been open for eight months.

This is the problem with multi-location businesses using Square: the platform gives you the data, but it doesn't automatically surface the insights. You can see individual transaction reports, but understanding the patterns across locations requires digging deeper.

What the Data Revealed

We pulled Sarah's Square transaction data into our location analysis tool and started breaking down the numbers. Within minutes, patterns emerged that had been invisible before:

Revenue per location wasn't what we expected. The newest location—the one Sarah had been worried about because it was "still finding its feet"—was crushing it. $47,000 in monthly revenue compared to $31,000 at the original location.

Device activity told a different story than revenue. The original location had the most transactions, but the lowest average ticket size. People were coming in, but they were buying less. The new location had fewer transactions but significantly higher spend per customer.

Time patterns varied wildly. The downtown location peaked during morning rush and died after 2pm. The suburban location had steady traffic all day, with a second peak around 3pm when schools let out. The third location—inside a co-working space—had almost no morning business but went crazy between 1-4pm.

This kind of granular insight simply wasn't visible in Sarah's daily dashboard checks. She'd been making staffing decisions, inventory orders, and marketing investments based on assumptions that turned out to be wrong.

The Surprising Insight: Industry Benchmarks Changed Our Perspective

Here's where it got really interesting. We didn't just compare Sarah's three locations to each other—we compared them to industry benchmarks for coffee shops in similar markets.

That's when we discovered something that changed Sarah's entire strategy.

Her "underperforming" original location wasn't actually underperforming at all. When we looked at revenue per square foot and compared it to typical coffee shops in urban settings, it was right on target. The rent was high, but so was the revenue density. The real issue? She was measuring it against the wrong benchmark.

The suburban location that was generating the most revenue? It was performing at 160% of typical suburban coffee shop benchmarks. This wasn't just a good location—it was an exceptional one. The kind of performance that suggests you should be thinking about replication.

And the co-working space location? It was operating at 85% of expected performance for that type of setting. Good, but not great. Room for improvement, but also a realistic ceiling on how much growth to expect.

Without these industry benchmarks as context, Sarah had been thinking about her business all wrong. She'd been frustrated with her original location and worried about her new one, when the data showed she should be celebrating the suburban store and optimizing the co-working relationship.

I've seen this pattern over and over with Square merchants. Just like e-commerce sellers who track order status without understanding what the patterns mean, brick-and-mortar businesses often have the transaction data but lack the comparative framework to interpret it correctly.

Taking Action: What We Changed

Armed with these insights, Sarah made several strategic shifts:

1. Staffing optimization. She adjusted employee schedules at each location based on actual traffic patterns, not assumptions. The co-working location went from three people on the morning shift to one, with two staff members during the afternoon rush instead. Labor costs dropped 15% while customer satisfaction scores actually improved.

2. Inventory management. Different locations got different product mixes. The downtown location focused on grab-and-go items for the morning rush. The suburban store expanded its pastry selection and added prepared lunch items. The co-working space tripled its cold brew inventory and reduced the espresso machine capacity.

3. Marketing investment. Instead of spreading marketing dollars evenly, Sarah concentrated promotional efforts on the suburban location during its strongest hours. She also cut back on Instagram ads targeting the downtown location and shifted that budget to local community events in the suburbs.

4. Strategic planning. Most importantly, when a fourth location opportunity came up downtown, Sarah passed. Instead, she started scouting for a second suburban location with similar demographics to her star performer.

These weren't random guesses or trendy business tactics. They were data-driven decisions based on actual location performance analysis.

Results and Lessons Learned

Six months after implementing these changes, Sarah's overall revenue was up 23%. But more importantly, her profit margins improved by 31%. The efficiency gains from proper staffing and inventory management made a huge difference.

The biggest lesson? Location analysis isn't just about finding your best performer—it's about understanding why each location performs the way it does and optimizing accordingly.

Here's what I learned from working with Sarah and dozens of other multi-location Square merchants:

Your gut instincts about location performance are probably wrong. We overweight recency, visibility, and personal attachment. The location you visit most often feels like it's doing better. The newest location gets more mental attention. Your first location has emotional significance. None of that correlates with actual performance.

Device-level data matters more than you think. Which Square terminal or device is processing the most transactions can reveal staffing issues, workflow problems, or customer preference patterns. One merchant I worked with discovered that one specific device at his busiest location was processing 60% of transactions—because it was the only one positioned where customers could easily see the screen. Moving the other devices changed the flow of his entire checkout process.

Industry benchmarks are essential context. Without them, you don't know if a "low-performing" location is actually underperforming or just operating in a naturally lower-volume market. You can't set realistic goals or make fair comparisons without external reference points.

Time patterns are goldmines. Every location has a rhythm. Understanding when each location peaks and valleys allows you to staff appropriately, schedule maintenance during slow periods, run promotions at optimal times, and avoid the trap of applying one-size-fits-all operating hours across all locations.

Your Turn: What's Hiding in Your Location Data?

If you're running multiple locations with Square, you're sitting on insights you probably haven't discovered yet. The question isn't whether the patterns are there—they definitely are. The question is whether you're going to find them before your competitors do.

I built our Square Location Analysis tool specifically to make this kind of deep-dive analysis accessible without needing a data science degree. Connect your Square account, and within minutes you'll see:

Sarah's coffee shops went from gut-feel management to data-driven optimization in a single afternoon. The insights were always there in her Square data—we just needed to surface them properly.

Want to see what's hiding in your own location data? Try our analysis tools with your Square account. The discoveries might surprise you as much as they surprised Sarah.

And if you're looking for more ways to extract insights from your business data, our tutorials section walks through dozens of similar analyses across different platforms and industries. For additional support and consulting, check out our services page to see how we can help you maximize your multi-location strategy.

Because here's the thing I've learned after analyzing hundreds of multi-location businesses: the data always knows the truth before you do. The only question is how long you'll wait to listen to it.

→ Run your own Square location analysis now