Author: Chris Hogan, CEO

Key takeaways

• We measured 164 stores. Conversion averaged 21%, with top performers at nearly 47%. Customer-only traffic varied by >25x across different stores
• Each of these stores has a wildly different story that would’ve gotten buried in the average. We highlighted five of them
• Comparing your stores against each other, instead of against the average, shows which ones to learn from and which ones to coach

Every multi-unit business has a summary view. Totals roll up from the stores to the districts to the region. Near the top sits a short list of numbers, and leadership uses it to decide where to put its attention. It’s tidy, it’s quick to read, and it’s the right tool for the questions it was built to answer.

It was never built to tell you what a particular store needs. One location might be short on experienced closers. Another might have a staffing gap during its busiest hours. A third might be doing most things right and losing customers at the entrance because of how the floor is laid out. Each of those has a fix a good manager can make quickly, once someone knows which problem belongs to which store. Folded into a company-wide summary, all three become one mildly disappointing number, and the same advice goes out to every location.

We decided to study 164 of the stores that reached their one-year ReBiz anniversary in the first half of 2026. Every store was measured over the same stretch of the calendar, a year apart, so a change in the numbers belongs to the store and not to the season.

By the numbers

• Across the 164 stores, customer-only conversion ran from modest at the low end to 47% at the high end, with a cohort average of 21%.
• 62 of the 164 stores sit five points or more from the average, and 14 sit ten points or more away.
• Daily customer-only traffic ran from about 3 customers a day at one store to about 80 at another.

From those 164, we pulled five stores that tell the story of why that spread matters. Snapshots like this show the same three things every time. Gross profit can mislead. The numbers change once customer-only traffic sits beside them. And an operator can act on the spread once it’s in front of them.

What does a portfolio average hide?

A portfolio average hides the stores doing the most right and the most wrong, because the two cancel each other out. It reports the middle and erases the range, which is the only part an operator can act on. 

Apply that to the 164-store cohort. Pooled across every store, customer-only conversion lands at 21%. If that were the only number on the page, the story would be a cohort of ordinary stores. Store by store, the story looks nothing like that. The strongest store sat 26 points above the average, and the spread is about seven points. The average describes very few of the stores behind it.

Customer-only traffic spreads the same way. The busiest store drew about 80 customers a day and the quietest about 3, against a typical day of about 20. No store in the cohort lives inside the average, and no single piece of advice fits all of them.

Now a look inside the cohort. 

see store-level conversion next to customer-only traffic counts

Why 164 stores, and why pull out five of them?

We studied 164 stores because it gave us a big enough dataset from the cohort we brought on a year ago. Each reached its one-year monitoring anniversary in the first half of 2026, so every store has a same-window comparison from a year earlier. We pulled out five because an average of 164 can’t show how any one store got where it did, and five can.

Here is how we chose them.

• Matched windows. Each store was measured over the same ~30-day window in both years, so a longer or shorter window can’t explain the gain.
• Both numbers up. Conversion and gross profit both rose, which makes these the stores an operator would want to learn from.
• Two segments. Three franchise stores and two multi-unit dealer stores, so the pattern doesn’t belong to one business model.
• Traffic beside gross profit. Every store is shown with its customer-only traffic, so you can see how the gross profit got there.

What does the spread inside the cohort look like?

It looks like a different business in every row. Over a matched window, with the same number of days in both years, five stores gained between $11,000 and $30,000 in gross profit each, and the way they got there differed.

Store	Customer-only traffic	Conversion	Gross profit
A	460 to 430	13% to 25%	$14,300 to $29,000
B	140 to 360	12% to 25%	$5,200 to $23,200
C	1,180 to 1,490	23% to 30%	$43,400 to $73,400
D	200 to 550	14% to 17%	$9,400 to $33,700
E	730 to 810	20% to 28%	$19,400 to $30,800

Which of those gains came from selling better?

Stores A, C and E, where traffic held roughly flat or grew modestly, show the clearest conversion gains. Store C is the cleanest read, with customer-only traffic up 27%, conversion up seven points, gross profit up $30,000. Store E added about 80 customers and eight points of conversion.

Stores B and D tell a different story. Traffic grew 159% and 181%, so most of their gross profit came from more customers walking in. Using them as conversion proof would be dishonest, and I’d rather lose the headline than the argument. Store A lost customers (460 to 430) and still doubled gross profit, which I wrote about earlier. link

Why do two stores with the same conversion rate tell different stories?

Because conversion is a rate, and a rate says nothing about how many customers it was applied to. Stores A and B both finished at 25% conversion. Store A got there with fewer customers than the year before. Store B got there with more than twice as many.

A district manager reading only the P&L sees two stores that both improved. A district manager reading conversion and customer-only traffic together sees one store that sold better to a slightly smaller crowd, and one store whose door got much busier. The first has something to teach. The second has a question to answer: what brought the extra customers in, and will they keep coming?

Why not judge from gross profit alone?

Because its gross profit made the selling look better when the selling had slipped. Another store from the 164 more than doubled its gross profit while its conversion fell from 26% to 24% and customer-only traffic more than doubled. More customers came in, and a smaller share of them bought.

On gross profit alone, that store reads as a success. On conversion, it’s a team converting a smaller share of a much bigger crowd, with the extra traffic covering for it. That is a coaching conversation, and a gross profit line never starts it. The exclusion also shows how the five stores above were chosen, which is the honest way to present a case study.

What does it cost to manage by the average?

It costs you the two decisions that matter most: who to learn from and who to coach. When every store is judged against the same middle number, strong stores get no credit for what they do differently, and weak stores get the same advice as everyone else. (The average store is the one store you don’t own.)

It also rewards the wrong thing. A store that grows because traffic rose looks as good as one that grows because its people sold better, and the average treats them as the same. Each operator was handed one number, and it was the wrong one to steer by.

The 164-store cohort shows the cost. The cohort-wide number says 21%. Underneath it, 28 stores convert five points or more above that average, and each of them is doing something worth finding out about. The stores below the line are where that knowledge pays off. Managed to the average, nobody has that conversation.

What does a spread need before you can trust it?

It needs measurement that is the same in every store and every period. The five stores above were compared over matched windows, ~30 days in both years, using customer-only traffic counts. Change the window, the definition of a customer, or the way traffic is counted, and a gap between two stores can turn into an artifact.

That’s the standard to hold any store-level comparison to. Matched windows. One definition of a customer. Counts verified daily, so a bad week doesn’t quietly rewrite the picture. And segments kept apart, which is why the table separates franchise stores from multi-unit dealer stores. A spread is only fair when you compare like with like.

What can an operator do with a spread?

Use it as a work list. A spread tells you which stores to learn from, which to coach, and which numbers to stop trusting at face value. Three questions put it to work.

1. Which stores sit furthest above your average on conversion, and what do their people do differently on the floor?
2. Which stores grew gross profit on traffic alone, and is that growth holding when traffic flattens?
3. Which stores look healthy in the P&L and weak in conversion?

Apply them to the five stores above. Stores A, C and E are conversion stories, so the first job is to find out what their people do differently and carry it to the weaker stores. Stores B and D are traffic stories, so the first job is to find out where the extra customers came from and whether they’ll stay.

I expect the operators who start managing the spread to look very different from the ones who keep managing the average. The first group knows where more is possible. The second is waiting for the number to tell them. What’s the gap between your best and worst store on conversion right now, and does anyone own closing it?

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