How a 13-Location Operator Found Its Marketing Was Working in Some Stores and Not Others
The problem
A quick-service restaurant franchise operating thirteen locations had plenty of data. What it didn't have was a way to look at it.
Each store's point-of-sale system held a detailed record of what happened inside that store. But the records didn't talk to each other, and they weren't structured the same way from one location to the next. Leadership could tell you how any single store was performing. What nobody could answer was the question that actually mattered: how was any given store performing relative to the others?
That gap has a cost, and it's a quiet one. Without comparison, every store looks locally reasonable. Underperformance hides in plain sight because there's nothing to measure it against.
What we did
We pulled the transaction data out of the POS systems and moved it into a central system where all thirteen locations could be seen together, in the same shape, on the same terms.
That's the unglamorous part of the work, and it's most of the work. Getting data out of systems that weren't designed to release it, then normalizing it so a comparison actually means something, is the step that everything else depends on. Once it was done, the operator could run analytics across the whole estate for the first time — apples to apples, store to store.
What we found
The first thing the comparison surfaced wasn't on the cost side. It was traffic, and the marketing spend meant to drive it.
Some locations were seeing real return on their marketing. Others were spending comparable money for noticeably less. And when we looked more closely, the pattern was sharper than simple over- and under-performance: different types of marketing worked differently depending on the store. What reliably drove traffic at one location did comparatively little at another.
None of this was visible before, because the spend had been evaluated at the chain level. Averaged across thirteen stores, effective and ineffective marketing look like one mediocre result.
The outcome
The operator stopped spending uniformly and started allocating by what each location actually responded to. Marketing budget moved toward the channels that worked in the stores where they worked.
The result was better marketing efficiency at the store level, and improved profitability at each location — not from spending more, but from spending the same money where it returned.
Why this matters if you sell online
Swap "location" for "channel" and this is a story about ecommerce.
A brand selling across a marketplace, its own storefront, and retail partners has the same structural problem the operator had: performance data living in systems that don't reconcile, evaluated channel by channel or averaged into a single blended number. Both readings hide the same thing. Blended CAC across three channels tells you as much as blended performance across thirteen stores — which is to say, not enough to act on.
Add jurisdictions and it compounds. Duties, fees, shipping, and payment costs differ by market, so the same acquisition spend buys a different margin outcome depending on where the order lands. Channel-level revenue can look healthy while contribution margin quietly diverges underneath it.
The fix is the same one here: get the data into one place, in one shape, so the comparison is real.
This started with an audit — getting the data out, and into a shape that could be compared.
Here's what that looks like