This is the case of a toy department in a grocery supermarket.
The case shows how decisive moves reduced category stock by 70%.
How much of this did we pay in falling sales? Zero. Stock reduction didn’t hurt the category sales.
On contrary. The sales increased.
It’s a real case. But not based on the classic optimization attempts.
| Bay1 | Bay2 | Bay3 | … | Bay18 |
|———————————-|
TOTAL ≈ 22.5 m
The category position was good but highly underperfoming in terms of sales per m2.
Before any analysis or optimization, toy Category occupied 18 gondola bays — more than 22 meters of linear shelf space.
With over 1000 SKUs stock rotation was slow.
We loaded the data into Omnibus Data engine and helped it get to some conclusions.
Despite wide assortment and heavy stock, profitability and rotation were poor.
Below you can see that more than 20% of high rotating stock generated 60% of sales …
… And there was more.
Even with a simplified dataset (1 category, limited attributes), the Data Engine logic revealed where performance truly lived:
Shopper behavior insight: Parents and children buy impulsively; fewer SKUs make choice easier and increase the likelihood of conversion.
And that special superpower moment: if we cut the assortment, we don’t only reduce the stock and release the capital, but we actually trigger additional sales.
Neuroscientific shopper insights are essential part of Shopper Logic behind the model. Too much choice blocks. That’s called paradox of choice. Shoppers faced with abundant SKUs often feel anxious, delay decisions, or regret their purchase.
Toys in grocery supermarkets are not a Destination category, but a Routine/Impulse add-on.
Limit breadth and depth, focus on shelf productivity and impulse hit logic!
Parents and children buy impulsively; fewer SKUs make choice easier and increase the likelihood of conversion.
Top 10 SKUs generate
55% of sales
Cut the long tail
Expand the heroes
Seasonal spike but no exit plan
Seasonal Exit protocol
OUTCOME
-70% DOWN
+10,4% UP
This is not a toy story only. The logic applies to any category with
The Data Engine simply makes the logic visible – fast.
Think of household gadgets in hypermarkets, hand tools in DIY stores, toys for dogs & cats in Pet food stores, office accessories in stationery stores, gift books & illustrated editions in bookstores, and many, many more.
These categories don’t suffer from under-assortment. They suffer from unchallenged abundance or be it overstock.
At Omnibus, we work with retailers and suppliers to turn data signals into actionable insights — and insights into actual category decisions.
This includes Data Engine prototypes, New Wave Category Management programs, and hands-on workshops focused on real pilots, where theory is checked on-the-go.
Frameworks, tools, and workshope designer for real retail impact.
A short conversation to see if a pilot or workshop makes sense.
Challenge. Learn & improve on pilot projects. Then scale for real impact.