How cutting 70% of category inventory increased sales?

How cutting 70% of category inventory increased sales?
December 14, 2025 Omnibus

How cutting 70% of category inventory increased sales?

SITUATION

Too much stock, too much space, too little clarity

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.   

The starting point: a lot of shelf space for category

| 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.

What Analysis Reveals?

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 … 

SKUs vs Sales Volume distribution (source: Omnibus Data Engine)

… And there was more. 

Even with a simplified dataset (1 category, limited attributes), the Data Engine logic revealed where performance truly lived:

  • Sales extremely concentrated as the top 2% SKUs generate 22,4% sales volume
  • The long tail fills disproportionate stock and space – the last 30% of SKUs bring only 6% of sales 
  • Seasonality amplifies the imbalance – stock inflates during peak season but never really decreases afterward, long-tail becomes wide and heavy, heavy 

Aha insight

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. 

Shopper Logic applied

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. 

Role

Toys in grocery supermarkets are not a Destination category, but a Routine/Impulse add-on.

Strategy

Limit breadth and depth, focus on shelf productivity and impulse hit logic!

Shopper Logic

Parents and children buy impulsively; fewer SKUs make choice easier and increase the likelihood of conversion.

From Insight to Decision/Action

Insight 1

Top 10 SKUs generate

55% of sales

Move 1

Cut the long tail

  • rotation > 1
  • share of sales > 0,5%
  • stock clog removal
  • match seasonality peaks

Insight 2

Long tail items eat space & cash

  • The huge long tail with almost 0 rotation
  • 70% of stock sits at wrong place
  • Low rotation kills profitability

Move 2

Expand the heroes

  • more facings
  • stronger visibility
  • dual location & promo tie-in
  • match seasonality peaks

Insight 3

Seasonal spike but no exit plan

Move 3

Seasonal Exit protocol

  • 0 stock rule post-peak
  • 30-45-60 day liquidation windows
  • automated monitoring KPI

Before

After

Shelf Space - Before = 18 bays
Shelf Space - After = 8 bays

OUTCOME

STOCK

-70% DOWN

SALES

+10,4% UP

Why this works?

This is not a toy story only. The logic applies to any category with

  • strong SKU concentration
  • seasonality
  • long tails
  • limited space

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.

Next sensible step if this resonates?

New wave category management will lead you from insights to impact

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. 

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