Ask yourself: when your sales drop 5% in a market, what does the conversation look like? Someone says it's the weather. Someone says it's pricing. Someone blames the new competitor. Someone thinks it's just bad execution. The truth is that they're probably all partially right, but nobody knows how much each factor matters.
This post walks you through new capabilities and technology that restaurant brands can use to contextualize store performance and make better decisions, faster.
The Fundamental Problem: Why Traditional Analysis Fails
You're a multi-unit restaurant brand leader. Sales are down in some markets, flat in others, and unpredictable everywhere. Your team has theories, your dashboards show you what happened, but nothing can tell you with any confidence which factors are driving performance.
Traditional analytics shows you one historical signal next to another, but they can't tell you WHY with any confidence, which means they definitely can't tell you WHAT TO DO ABOUT IT.
Let's take your Downtown Chicago location that drops 8% in Q4, for example. The year-over-year comparison shows down 8%, same-store sales show down 8%, and the interpretation is simple: bad performance. The typical action? Replace the GM or run a discount promotion.
This is single-signal analysis. You're looking at one number—your sales—and assuming it tells the whole story. But it doesn't. You're missing weather changes, competitor moves, construction impacts, economic shifts in the neighborhood. You're looking at the outcome without understanding the inputs.
And when your team does try to factor in other signals, they look at them one at a time. "Well, foot traffic was down to the whole trade area, so that probably hurt us." But what caused that? And how does that impact the future? For how long? Nobody knows. So decisions get made on gut feel dressed up as analysis.
This isn't a failure of effort or intelligence. It's a structural limitation of how business intelligence has traditionally worked: backward-looking, single-signal, and static. That's the fundamental problem SignalFlare solves. And we solve it through what we call Signals.
What Signals Actually Are
A Signal is a measured input that we know affects your business—and we know how much it typically affects businesses like yours.
Think about weather. We all know weather impacts restaurant traffic. But how much? Is a rainy Tuesday in March the same as a rainy Tuesday in July? Does rain affect QSR the same as casual dining? Does 0.5 inches of rain matter the same as 2 inches? A Signal captures all of that nuance — it's a quantified understanding of how weather patterns specifically impact restaurant traffic in your segment, in your micro-market, season-by-season.
We track dozens of different Signals for every location, organized into two broad categories.
External Signals cover conditions you can't control — economic conditions like local income levels and employment rates, weather patterns that go far beyond rain or shine, competitive activity including new entrants and saturation levels, and consumer behavior patterns around mobility and spending habits.
Internal Signals focus on factors you can control — your pricing levels both absolute and relative to competition, your promotional activity and what's actually working, your menu composition and value perception, and your operational execution from consistency to digital experience.
What makes this fundamentally different from just "having data" is that we don't look at these Signals individually. We look at how they interact.
Take two of our most powerful proprietary signals, for example:
Customer Purchasing Power measures your customers' actual ability to spend — not just income, but disposable income after accounting for local cost realities. A $100K household in Manhattan might have less discretionary income for dining out than a $65K household in Oklahoma City. Traditional analytics would call Manhattan the "higher-value customer." Signals reveal the truth.
Guest-to-Restaurant Ratio measures competitive intensity — but not the way you'd expect. We identify where your actual customers live and work, then calculate how many competing restaurants are fighting for those same customers. Your downtown store might have 50 restaurants within 3 miles, but if your customers commute from suburbs with only 15 competing options, your real competitive intensity is much lower than it appears. This changes how you should price and position each location.
These two Signals alone — purchasing power and competitive intensity — create the foundation for store-level strategy. Plot every location on these two dimensions and you immediately see which stores should pursue premium positioning versus value strategies, which markets can absorb price increases, and which locations face structural disadvantages no amount of operational excellence can overcome.
And these are just 2 of the many Signals we track for every location.
How We Know This Actually Works
At this point, you're probably thinking some version of: "This sounds interesting, but how do I know those numbers are right? How do you know weather caused -2.8% or that a competitor impact was -3.2%?" This is the right question to ask.
The Multi-Signal Approach: Triangulation
GPS works because it doesn't rely on a single satellite. It uses multiple satellites simultaneously—each with some measurement error — and triangulates to find your accurate position. Even when individual signals have noise, the triangulated answer is reliable. We do the same thing with business data.
Take foot traffic patterns. We pull that from mobile device location data — about 180 million devices. Is that data perfect? No. It has sampling bias. Coverage varies by device penetration. Distinguishing actual customers from people walking by isn't always clean. If we used mobile data alone for point-in-time benchmarking — "exactly how many customers did you have last Tuesday?" — it would be too noisy to trust.
But we don't use it alone. We triangulate: mobile data shows relative traffic patterns, credit card transaction data shows spending patterns, your actual POS data shows what you captured, weather data explains some variance, and economic data explains other variance. When all of these Signals point in the same direction, we're confident. When they conflict, we know to dig deeper.
Remember that Downtown Chicago store that dropped 8%? When we apply Signals to that situation, here's what we see:
Weather was worse than last year: -2.8% expected impact
New competitor opened 0.3 miles away: -3.2% expected impact
Local office building started major construction: -1.5% expected impact
Local household income declined due to layoffs: -1.1% expected impact
Total external impact: -8.6%
Which means your store actually performed +0.6% better than expected given the circumstances. You don't need to replace the GM — you need to figure out what they did right that allowed them to outperform terrible conditions.
The triangulation makes imperfect data sources collectively reliable — much more reliable than any single "perfect" source. This is what lets us build models that have been 85-90% accurate in predicting outcomes over 25 years and 14 trillion transactions.
A Real Example: Pricing in an Inflationary Environment
We worked with a fast-casual chain facing a challenge many of you are dealing with—how to price without killing traffic.
The traditional approach? A national pricing change and hope for the best. Instead, we analyzed Signals across their 200 locations and found pricing elasticity varied massively — from -0.28 in high-income, low-competition markets to -0.71 in middle-income, high-competition markets.
We recommended differentiated pricing: 8% increases where customers could absorb it, 4% in moderate markets, and holding prices where competitive pressure was too high.
The result: 6.2% revenue growth versus 3.8% with uniform pricing, and only 2.1% traffic decline versus 4.5% projected.
Principled Approach
Our team has spent 25 years improving these models, and we know the skepticism that comes with sophisticated analysis. So let us address the trust question directly.
First, transparency. We show our work. You can see which Signals drove which recommendations. We're not a black box where we just look at what the algorithm said to do. When something doesn't work as predicted, we explain why and show how the model adjusts.
Second, track record. This methodology has powered 30 million+ price changes for 60% of the top 100 restaurant chains. The reason we work with so many industry leaders isn't marketing — it's that it works. They can measure it working.
Third, you test it first. We don't ask you to trust it blindly. Phase 1 is Signals Activation — we show you what Signals reveal about your business right now. You can validate that against what you already know. Then we co-create a roadmap focused on decisions where you need the most confidence.
This is About How You Operate
Most restaurant operators today move from one high-stakes decision to the next — each one painstaking, costly, and risky. You spend months analyzing a pricing change, agonize over the decision, pull the trigger, and hope it works. Six months later, you start over. You're constantly reacting to market forces rather than adapting to them.
SignalFlare changes how your entire organization operates. Instead of discrete, high-risk decisions separated by months of uncertainty, you shift to continuous, informed decision-making.
Signals suggest raising prices 6% in specific locations with a prediction: 4.2% traffic decline, 8.1% revenue growth. You implement it, and we track what actually happens — say, 3.8% traffic decline and 7.9% revenue growth. The system learns that price elasticity in your concept is slightly different than we modeled, and the next pricing decision becomes more accurate.
This is what we call Enterprise Memory. The platform remembers your standards, understands your markets, and learns your decision patterns. Each analysis builds on the last. And critically — YOUR learning stays with YOUR organization. Your competitive intelligence, your decision patterns, your outcomes—that all stays in your environment, compounding YOUR advantage over time.
This is how you become a learning organization. Not by getting smarter reports, but by building systems that learn from every decision and make the next decision better.
The Bottom Line
The restaurant industry is operating in one of the most challenging environments in decades. Inflation is sticky. Traffic is down. Competition is intense. Consumers are price-sensitive but still demanding quality.
In this environment, making decisions based on backward-looking dashboards and simple year-over-year comparisons isn't enough. You need forward-looking intelligence that helps you understand what's actually driving performance and what you should do differently. The companies that get ahead aren't the ones with perfect data. They're the ones who can make better decisions faster with imperfect data.
That's what Signals provide. Not perfect predictions — nothing is perfect in this industry. But meaningfully better decisions, made faster, with quantified levels of confidence.
If that sounds valuable for your organization, schedule a capabilities call. We'll show you what Signals reveal about your business and whether this approach makes sense for you.
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SignalFlare is a decision intelligence platform for restaurant operators. Designed from experiences building pricing and optimization tools that have powered over 30 million price changes and are trusted by 60% of the top 100 U.S. restaurant chains.
Want to explore what Signals reveal about your business? Contact our team.
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