Payoff Reads · Staffing and labor

Did the extra staff and the pay raise earn back their cost through more sales at peak hours?

Find out what added staff hours and pay changes contributed at every store after their cost: in sales and customers served at peak hours, in how often customers come back, and in fewer employees quitting and less overtime. Each store is read vs. a twin built from stores that haven't made the change.

2 Pays for itself
4 No significant effect
6 Costs money

Heartland Markets is a fictional chain. Every number on this page is made up to show the format.

What your numbers can't show

Your labor reports show what each store spent on hours. They can't show what the hours earned.

What you have: labor hours and labor cost by store, and sales per labor hour.
What it can't show: what the added hours sold. Sales per labor hour drops the week you add a person, even when that person pays for themselves, and rises at a short-staffed store that's losing customers to the line. It also can't show the sales a long line costs, what a raise saves when fewer employees quit and less overtime is needed, or whether those savings cover it.
What the read shows: what each staffing change earned at every store after its cost: sales and customers served in the hours it touched, how often customers came back, employees quitting and overtime, and the margin left once the added pay comes off.

Store employee turnover in convenience retail typically tops 100% a year. The most profitable 10% of companies spend $23.10 per labor hour on wages and benefits, almost $5 more than the least profitable 10%, and earn $8 more gross profit per labor hour (NACS). That's a pattern across companies. Whether paying more will pay off at your stores requires a read.

What lands on your desk

A staffing change, as Seurat reads it

Heartland Markets added a 2nd employee to the Monday-to-Friday morning shift, 6 to 10 a.m., at 16 stores from September 2024, a few stores at a time as it hired. Of those, 12 were read. The other 4 had a remodel or the fresh section in the same weeks, so they're held out. Each store was read vs. a twin built from stores without a 2nd morning employee.

The answer, in three lines:

Mornings got busier: +45 visits a week and a $0.30 bigger basket, mostly coffee, fountain drinks and breakfast food. That's about $24,000 a year in added sales per store.
Across the 12 stores, it doesn't pay for itself: after the 20 added hours a week, the 2nd employee costs $155 a week per store, about $8,000 a year, and that's statistically significant.
It pays at the 4 stores on commuter routes, where the morning line was long enough to turn customers away. At the other 8, mornings weren't busy enough to need a 2nd person.

per store, per week, Monday-to-Friday mornings, vs. stores without a 2nd morning employee

What changed	Result	Verdict
Visits, 6 to 10 a.m.	+45	Significant lift
Visits in the busiest 15 minutes, per morning	+1.8	Significant lift
Basket, 6 to 10 a.m.	+$0.30	Significant lift
Visits per Heartland Rewards member, per month	+0.1	No significant effect
Sales of food and dispensed drinks, 6 to 10 a.m.	+$460	Significant lift
Margin on the added sales	+$270	Significant lift
Added labor: 20 hours a week	−$425	Cost of the change
What the 2nd employee adds to the bottom line	−$155	Costs money

Sales vs. the bottom line: the 2nd employee added $460 a week in morning sales, about $24,000 a year. After the cost of the 20 hours, it's $155 a week short of paying for itself.

Where the visits came from: a fifth of the added visits came in the busiest 15 minutes of the morning, when the line used to be longest. Those are customers who used to leave the line or skip the stop.

Sales per labor hour: at the 2 stores where the 2nd employee pays for itself, sales per labor hour still fell, because hours rose faster than sales. Judged on that number alone, both stores would have cut the shift.

By store type: the 2nd employee pays at the 4 stores on commuter routes, where the morning line was long enough that customers left it or skipped the stop. A 2nd person shortened the line, those visits came back, and Heartland Rewards members there came in more often: +0.3 visits a month.

by store type, per store per week

Store type	2nd employee adds	Confidence band	Verdict
4 commuter-route stores	+$190		Pays for itself
The other 8 stores	−$325		Costs money

store by store (6 of 12)

Store	2nd employee since	Adds per week	Verdict
Store 33, Kennedy Pkwy	Sep 2024	+$330	Pays for itself
Store 10, Wiley Blvd	Sep 2024	+$240	Pays for itself
Store 18, C Ave	Oct 2024	+$40	No significant effect
Store 15, Glass Rd	Oct 2024	−$150	No significant effect
Store 2, Oak Ave	Nov 2024	−$370	Costs money
Store 20, Lake Rd	Nov 2024	−$460	Costs money

The pay raise: in October 2025, Heartland raised starting pay $1.50 an hour at the 8 stores in its east district, where employees quit most often. Over the next 6 months, the number of employees quitting fell by about a third, and overtime fell. Those savings covered most of the raise. It's too soon to see it in sales.

the $1.50 raise, per store, first 6 months, vs. stores in the other districts

What changed	Result	Verdict
Employees who quit, per month	−0.9	Significant drop
Overtime hours, per week	−9	Significant drop
Inside sales, per week	+$260	No significant effect
What the raise costs after the overtime and hiring it saves, per week	−$70	No significant effect

KeepFixStopWhat to do: keep the 2nd employee at the 2 stores where it pays, stop it at the 6 where it costs money, and at the other 4, keep it on the 2 busiest mornings only. Keep the raise, and read it again at 12 months, when sales have had time to show it.

The confidence band shows the 95% confidence interval. When the band spans zero, the result isn't statistically significant. Heartland Markets is a fictional chain. Every number on this page is made up to show the format.

How the read works

Staffing changes usually roll out a few stores at a time, as hiring allows, and pay often changes by district. That's what makes the read possible: the stores that haven't changed yet show what the changed stores would have done without it.

Give each store a twin: a predicted line of sales and visits by hour, built from stores that haven't made the change, weighted so it matches the store for a year or more before.
Count the sales a long line costs. The twin predicts each hour's visits. When the busiest 15 minutes gain the most after a person is added, the extra visits are customers who used to walk out of the line or skip the stop, and the read counts what they buy.
Read the hours the change touched. A morning shift is read on the morning, and the rest of the day serves as a check: if afternoon sales also shifted, something else changed at that store, and we'll explain it.
Count the savings as well as the cost. Employees quitting, overtime and the hours spent hiring and training come from counts you already keep, valued at your own rates.
Put a price on it, and check it. Sales are valued at your margins, and the added pay comes off. Each twin has to have tracked its store closely before the change. A placebo test then reruns the read on a made-up start date, months before anything changed. Because there's nothing to find during this period, it should show no effect. If it shows an effect, the read isn't working properly, and we tell you.

Can your staffing change be read?

It depends on how the change reached your stores, more than on how many stores you run. There are 3 routes.

1
Hours added or moved at some stores
How the read works
Each store vs. a twin built from stores that kept their schedule, read on the hours that changed
What it answers
What the hours sold, and whether they pay for themselves
2
Longer opening hours at some stores
How the read works
Each store vs. a twin built from stores that kept their hours
What it answers
What the extra hours sold after their labor, and whether the rest of the day moved too
3
Pay changed in some districts or stores
How the read works
Stores with the new pay vs. a twin built from stores without it
What it answers
Employees quitting, overtime and sales, and what the change costs after what it saves

We check your schedule and pay history on the first call, before you commit to anything.

Scope this read

What we need, and what stays with you

We need:

Weekly sales by store, split into food, drinks, and everything else
Transactions and sales by store and hour, with and without fuel, in 15-minute steps if your system keeps them
Labor hours by store, by day and hour if your scheduling system keeps them
Average hourly pay by store and month, and the date any pay change started
Hires and employees who quit, by store and month, as counts
Overtime hours by store and week
Your margins by category, as percentages
Where your loyalty program can follow members, visits by member, with member IDs you've scrambled (hashed) yourself
Customer complaints or survey scores by store and month, if you track them
1 to 2 years of history

We never ask for:

Employee names, IDs or anything else that identifies an employee
Individual pay or payroll records
Names, phone numbers, emails or anything else that identifies a customer

How we handle your data →

How long it takes

About 2 weeks from the day your data lands, then a 60-minute readout with whoever needs to hear it.
We scope it to your chain on a short call: which stores, which schedule changes, which pay changes.

Scope this read Book a call

Questions buyers ask us

Our sales per labor hour fell after we added hours. Did it fail? Not necessarily. Sales per labor hour drops the week you add a person, even when the person pays for themselves. The read counts what the added hours sold and the margin left after their cost.
Can you tell whether better staffing improves service? Not directly, but the read sees where service shows up: how many customers a store serves at its busiest times, how often customers come back, how many employees quit, and, if you log them, complaints or survey scores.
Can you tell which stores are short-staffed? Where some stores added hours and others didn't, the read shows where the hours paid. Where nothing has changed yet, we can show which stores and hours look most like the ones where added hours paid, so you know where to try first.
We raised pay and our sales went up. Was it the raise? Maybe. Pay usually rises when the local job market tightens, and that changes sales too. The read compares stores with the raise to stores without it in the same months, so the job market drops out.
Do you need employee records? No. Hours by store and hour, average pay by store and month, and counts of hires and of employees who quit. No names, no IDs and no individual pay.
What if the read shows the change doesn't pay? A read with no significant lift is a private finding. You see every result first, and nothing goes anywhere without your approval.
Do we have to connect any systems? No. One export from your back-office and scheduling systems, sent however your IT team prefers.
Who does the work? We do. Your team runs the export, shares it with us, and comes back to hear our readout.

After this read

Read several changes in one request, or keep staffing measured week by week.

Scope several changes →How monitoring works →

Sources: NACS, "Key Metrics for a C-Store Employee Retention Strategy," January 2026.