How we help you
The entire process, in 3 simple steps.
01 You set the goal
More profit, more sales, or more repeat visits?
No menu can maximize all three at once, so before we run anything we work out the mix with you: which one comes first, and how much the other two weigh. Every recommendation is then scored against that mix.
Margin
Choose margin when profit per store is what's under pressure: costs have gone up, the store count isn't growing, and the stores you already have need to earn more from the sales they already make.
- Raise prices where customers barely react.
- Fix delivery prices where fees eat the profit.
Sales
Choose sales when growth is the goal and total revenue is the number you're judged on, for example while opening new stores. Prices then work to bring more people in and make each order bigger, even if each one earns a little less.
- Sharpen the entry prices that bring people in.
- Price combos so the order gets bigger.
Visits
Choose visits when you're thinking in years: stores that live on regulars, where a price that drives a weekly customer away costs more than it earns. Prices then protect the items regulars order most and move slowly, with a set gap between changes.
- Protect the prices your regulars order every week.
- Move prices slowly, with a set gap between changes.
02 The models run
Then the models do the work.
They estimate what each item earns, how its sales react to price, and which changes are worth making under your rules. The full detail is on the How it works page.
- Your dataPOS history, delivery statements, cost sheets.
- Six modelsMargin, demand, price response, baskets, optimization, testing.
- Store by storeWhat matters at this location, scored against your goal.
- RecommendationA specific price change, with a dollar estimate and a range.
- Measured resultChecked against stores that didn't change.Wins, losses and nulls all count.
03 You get a decision letter
A short letter, not a dashboard.
Every few months: which prices to change, at which locations, and what each change should earn. Where we're confident, it's a recommendation. Where we're not, it's a test.
A decision letter
The changes worth making now, each with a dollar estimate and the reason behind it.
Raise seven items at Store 184: +$3,400 a month, 87% confident.A measured result
What the change actually earned, compared with similar stores that didn't change.
Classic wings +$0.30: +$1,900 a month, measured.A named analyst
One person who knows your chain and runs this with you.
You can argue with the letter, and with them.What a test is
What happens when we recommend an experiment.
Click through the four stages.
Not sure enough to recommend.
Classic wings, +$0.30, at 6 stores. Our confidence is 71%, below the 78% we need to recommend. So it becomes a test.
Designed before any price moves.
Six test stores are matched with six similar stores that keep their prices. We check first that the test is big enough to show a result.
- Test stores6
- Comparison stores6, matched on past sales
- Length4 weeks
- Big enough to show a resultYes
- GuardrailsAll 16 applied
The other stores never see the change.
For four weeks both groups are tracked on margin. Nobody touches the dial.
The number you judge us on.
The test stores ended about six points above the others. The result is recorded, claimed against delivered. Wins, losses and nulls all count.
Sample experiment: classic wings, +$0.30 Show the chartHide the chart
Margin, indexed to the four weeks before the change = 100
Sample data, not a client result. Matched tests need roughly eight locations or more. Below that we measure against your own history and say so.
Talk with us
Want to see what your data says?
Send us one POS export and we'll show you where the margin is. The first look is free, and you keep the readout either way.