Products compared together: cross-merchandising
Which products do shoppers weigh against each other? How pairs picked up one after the other inform placement, pairings and screen content.
- Home
- In-store A/B testing
How to run an A/B test in a physical store
Websites test everything; stores mostly guess. How to test a placement, a fixture or screen content in a real store with product interaction data, and how to read the result honestly.
The short answer
Change one thing, measure the same products before and after, or in two matched stores at the same time, and compare rates: pickups per day, put-back rate and hold time. Keep everything else still, give it enough days, and treat the result as a change you observed, not as proof.
Two ways to test
| Design | How | Good for | Watch out for |
|---|---|---|---|
| Before and after, one store | Measure a period, make the change, measure the same length again | Placement, facing, fixtures, screen content | Seasons, campaigns and weather change behaviour on their own |
| Two matched stores, same period | Change one store and leave the other as it is | Separating the change from the season | Stores are never identical: compare each with its own baseline too |
Step by step
Write the question
One change and what you expect it to move. For example: moving a model to eye level raises its pickups.
Choose the measure
Pickups per day, put-back rate or hold time for the products involved, decided before you start.
Record a baseline
The same products in the same places, for a normal period.
Make one change
Move, re-face or swap content, and change nothing else in that area.
Measure the same length again
The same days of the week if you can; avoid campaign days unless the campaign is what you are testing.
Compare rates
Per product and per store, and check whether neighbouring products lost what the moved one gained.
Reading the result
- Small differences need many pickups. A few pickups more or less is noise; look for a difference that holds day after day.
- Look next door. A product that gains pickups may take them from its neighbours; check the whole area.
- A change, not a cause. A before-and-after shows what changed; it does not prove your change caused it. GoTrack shows the change; it does not run statistical tests for you.
- Join with sales for revenue. GoTrack does not read POS data; combine its exports with your sales.
Questions
Can we test a new product placement with GoTrack?
Yes, as a before-and-after comparison: move a product to a new shelf zone or fixture and compare its pickups, hold time and put-backs with the period before. GoTrack shows the change; it does not run statistical tests for you.
Can GoTrack tell marketing which products are examined, and for how long?
Yes. It reports pickups per product and variant, average hold time, put-back rate, products compared together and never-touched products. It measures how long a product is held, not how long someone stands in front of it.
Can GoTrack show whether sales went up?
Not on its own. GoTrack measures what happens at the shelf — pickups, hold time and put-backs — and does not read POS data. To relate interest to sales, join GoTrack's CSV exports or webhook events with your own sales data.
What is a good put-back rate?
There is no universal benchmark: it depends on the category, the price and the store. The useful comparisons are a product against itself over time, and against similar products in the same store and zone. GoTrack reports the rate per product, colour, zone and store so those comparisons are possible.
Further reading
Have a change to test?
Tell us what you want to try; we will show you what GoTrack would measure.
or write to info@gotonom.com