If you spent any time at IPHFHA last week — or have been following along with what we've been publishing on unit economics — you probably recognized the tension we've been describing.
The numbers look fine. The reports run. The close gets done. And yet something about the picture your unit-level data is painting doesn't fully match what your operators are telling you, what your instincts are saying, or what you see when you actually spend time in your locations.
That tension isn't a sign that something is broken. It's a sign that your unit economics are doing exactly what most unit economics do — producing numbers that are technically accurate and structurally misleading at the same time.
Last month we talked about the gap between outcomes and drivers — between what your financial reports show and what's actually causing those results at the operational level. This month we're going deeper on the mechanics. Specifically on how misleading unit economics get built, maintained, and trusted by smart finance teams who are doing everything right with the data they have.
The problem isn't capability. It's construction.
The problem isn't capability. It's construction. Most misleading unit economics were built incrementally, by capable people, under conditions that prioritized speed over consistency.
It Starts With How The Data Gets Structured
Most multi-unit restaurant organizations don't build a unit economics framework from scratch. They build it incrementally — one location at a time, one system at a time, one reporting decision at a time — over years of growth that happen faster than anyone planned.
The first few locations get set up carefully. The chart of accounts is clean. Cost categorization is consistent. The reporting structure reflects how the business actually operates.
Then the third location opens in a new market with slightly different cost dynamics. A regional manager makes a reasonable call about how to categorize a shared expense. A vendor contract gets structured differently and the accounting treatment follows. None of these feel significant at the time. Each one makes sense in context.
But each one introduces a small inconsistency into the reporting framework. And because the inconsistency is small and the business is moving fast, it doesn't get corrected. It gets normalized.
Then location seven comes through an acquisition. It brings its own chart of accounts, its own categorization conventions, its own history of accounting decisions that made sense under previous ownership but don't map cleanly to how the rest of the portfolio is structured. The integration gets the entity into the consolidation correctly — the numbers roll up, the close works, the report runs. But the underlying structure is now carrying an inconsistency that shows up as a performance difference in the unit-level data.
Is location seven actually performing differently than its peers? Maybe. But the finance team can't be certain — because they can't tell whether what looks like a performance difference is an operational difference or a reporting difference. And because the report looks clean and the numbers make intuitive sense, the assumption becomes that it's operational. The reporting difference goes unexamined.
Multiply this across twenty or thirty locations — some opened organically, some acquired, some operating under different brand concepts — and what you have is a consolidation that looks like an apples-to-apples comparison and is actually an aggregation of meaningfully different measurement approaches.
The top line holds together. The detail underneath it is far less reliable than the report suggests.
What looks like an apples-to-apples comparison of unit performance is often an aggregation of meaningfully different measurement approaches.
The Franchise Dimension Makes This Harder
For franchise operators specifically — and this came up repeatedly in conversations at IPHFHA last week — unit economics carry a layer of complexity that pure company-owned operators don't face in the same way.
Franchise agreements create cost structures that vary depending on when they were originally signed and what terms were available at the time. Two locations operating under the same brand, in similar markets, with similar volumes, can have meaningfully different royalty structures, different marketing fund obligations, and different technology fee arrangements — simply because one franchisee signed their agreement ten years ago and the other signed two years ago. Those differences affect unit-level profitability in ways that have nothing to do with operational performance.
When those cost structure differences aren't explicitly accounted for in unit-level reporting — when the comparison is made at the gross margin or EBITDA level without isolating the franchise cost variables — the performance comparison becomes misleading in a specific way. A location that looks like it's underperforming its peers might actually be operating more efficiently. It just has a less favorable cost structure built in at the agreement level.
The finance team working from standard reporting isn't making a mistake. But the expansion decision or operational investment that follows from a misleading comparison is built on a foundation that hasn't fully accounted for what's actually driving the difference.
Then Informal Knowledge Fills The Gaps — And Creates New Ones
Here's where misleading unit economics get genuinely difficult to untangle.
Because finance teams are capable and operators are experienced, the gaps in the formal data get filled informally. The CFO knows that location seven came through an acquisition and its numbers should be read differently. The controller knows the food cost percentage at the downtown location looks high because of how shared kitchen expenses get allocated. The regional VP knows the suburban location's labor efficiency numbers are partly explained by a GM who regularly works shifts herself rather than scheduling a replacement.
This informal knowledge is accurate. It's valuable. It allows the organization to make reasonably good decisions despite the structural limitations of the formal data.
But it creates a dependency that is invisible until it isn't.
When the CFO leaves, the context about location seven leaves with her. When the controller moves on, the institutional memory about the downtown allocation goes with him. When the regional VP transitions out, the understanding of what's actually driving the suburban location's labor numbers disappears.
What remains is a reporting framework that looks complete — the reports still run, the numbers still tie out, the close still gets done — but is now being read without the context that made it interpretable. The new CFO looks at the unit-level data and sees what looks like a straightforward performance comparison. She makes decisions based on it. Some are well-founded. Some are built on comparisons the previous CFO would have known to discount.
The organization doesn't know which is which. Because the knowledge that would have told them is gone.
When the CFO leaves, the context leaves with her. What remains is a reporting framework being read without the context that made it interpretable.
The Compounding Effect Nobody Plans For
Misleading unit economics don't feel misleading from the inside. They feel like normal operations.
The reports look the same as they always have. The close process works. Leadership makes decisions — some of which perform as expected and some of which don't. In any growing restaurant group, performance variability feels like a normal part of doing business rather than a signal that the underlying data has structural problems.
The finance team gets asked to explain the variability and does the best they can. Some explanations are accurate. Some are plausible reconstructions that fill the gap between what the data shows and what leadership needs to understand. Over time those reconstructions become the accepted narrative — and the accepted narrative shapes the next round of decisions.
This is how a restaurant group can be three or four expansion cycles into a growth plan before anyone formally asks whether the unit economics they've been relying on are actually measuring what they think they're measuring.
By that point the cost has been compounding for years. Not dramatically. Not in ways that were obviously visible at any single decision point. But in the accumulated weight of decisions that were reasonable given the data — and would have been different given better data.
What This Means Practically
The organizations that break out of this pattern almost never do it because something failed dramatically. They do it because someone asked a harder question than the standard reporting was designed to answer.
That question is almost always some version of: if we're going to make a significant growth decision based on what our best locations are doing — how confident are we that we actually understand what's making them perform?
Not at the outcome level. At the driver level. In a way that would hold up if you had to formally justify the comparison and the conclusion you're drawing from it.
For most growing restaurant groups that question reveals more uncertainty than expected. Not because the team is incapable — but because the unit economics framework was built incrementally, in conditions that prioritized speed over consistency, and has been carrying structural limitations that capable people have been quietly compensating for ever since.
Because the next phase of growth depends on getting this right.
