Pricing & Elasticity
Pricing Experimentation: Test the Increase Before You Bet the System On It
“We want to try a price change at some stores first. How do we set that up so the result actually means something?”
A pricing test is only as good as its design. Pick the wrong test stores, skip the control group, or read the results too early, and you get an answer that feels rigorous but is really noise. A proper pricing experiment needs three things: treatment stores that represent the system, true control stores matched on market, volume, and trend that receive no change at all, and a pre-committed read date long enough for customer behavior to settle. Quantiiv designs all three before a single price moves.
The payoff is a decision you can defend: the tested change produced this much check growth and this much traffic response at treatment stores versus their matched controls, so rolling it out system-wide is predicted to be worth this much. Franchise systems get an extra benefit, because a clean result from corporate test stores is the most persuasive evidence a franchisee will ever see.
Sound Familiar?
Tests get run at convenient stores, not representative ones
The pilot happens at the stores closest to the office or the operators most willing to try it. Those stores rarely represent the system, so the result does not transfer, and nobody finds out until after the rollout.
There is no control group, so the market grades the test
If every store in the test region takes the change, the comparison defaults to last year, and last year is polluted by weather, promotions, and macro shifts. The test inherits whatever the market did.
Results get read too early and on the wrong metric
Two good weeks after a price change mostly measures the fact that customers had not adjusted yet. Reading only sales, without splitting traffic from check, hides exactly the damage a bad increase does.
How Quantiiv Answers It
- 1
Define the decision the test must support
Every experiment starts from the rollout decision it is meant to inform: what change, what target, and what result would justify or kill the system-wide move. The design follows from the decision, not the other way around.
- 2
Select treatment and control stores that can carry the answer
Treatment stores are chosen to represent the system's markets and volume bands. Comparison stores are matched to them on trend, volume, and market so the read is fair, and they keep current prices, which is what makes them a clean point of comparison afterward.
- 3
Predict the outcome before the test starts
The elasticity model produces an expected result in advance. The test then confirms, sharpens, or contradicts the prediction, which is far more informative than running blind and interpreting afterward.
- 4
Run long enough for behavior to settle
Customer response to price unfolds over weeks as habits adjust. The read date is committed up front, with early checkpoints only to catch operational problems, not to declare victory.
- 5
Read the result cleanly, then roll out with confidence
The readout compares the stores that changed against the ones that did not on traffic, check, mix, and net revenue, and translates the result into the system-wide rollout case, including which store types should and should not receive the change.
Why Quantiiv
Experiment design, not just measurement
Most pricing tests fail in the design, before any data arrives. Store selection, control matching, and duration are set with the same rigor as the analysis that follows.
A prediction on the table before the test
Because the test is built on your elasticity read, there is a forecast to check the result against. Every experiment makes the next model better and the next test cheaper.
Frequently Asked Questions
How many stores do you need for a pricing test?
It depends on how much store-to-store variation your system has and how small an effect you need to detect. A handful of stores can validate a large, obvious change; detecting a subtle traffic response reliably takes more. The honest answer comes from a power check before the test, which is part of the design rather than an afterthought.
How long should a restaurant pricing test run?
Long enough to capture adjusted behavior, not first reactions. In practice that usually means a couple of months as a minimum, longer when the change is large or the item is habitual. Short tests systematically flatter price increases because customers have not yet finished responding.
Can a franchise system run pricing tests?
Yes, and it is often the smartest path to system-wide pricing alignment. Corporate stores typically serve as the treatment group, franchisee stores that keep current prices act as natural comparisons, and the resulting evidence gives franchisees a store-level, data-backed reason to adopt the change voluntarily.
What if the test says the change did not work?
Then the test paid for itself, because it stopped a system-wide mistake at pilot scale. A failed test also carries detail a rollout never would: which items and store types drove the miss, which usually points directly at a revised change worth testing next.
Related Problems We Solve
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Read moreStop Fighting Your Data.
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