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In most cases defining the problem and showing the clear path to a solution is the hardest thing. The rest is execution.
Each partner is unique.
The retail challenges can be categorized in main groups, but the solutions to most challenges need to be tailored. Synchronisation with the power of brand is essential.
With this in mind, our consulting is tailored to the particular needs of the partner.
But there’s one rule that could be seen as universal. Defining the problem and the clear path to a solution is the hardest thing. The rest is execution.
The simple way to start is the following:
After that, I offer a 30-minute personal discussion via Zoom service
(yes, it’s free!).
Based on that I’ll make a detailed proposal – a path to the solution of the problem.
If you accept the proposal, we can steer the project for you. Or you can do it yourself – depending on your needs and resources. Whatever suits you best. If you simply cancel it, you’ll at least have a relevant opinion. Try it out!
Consulting and the corresponding materials are available in English, Slovenian, Croatian or Serbian and German language.
When 5 > 600. How many products does the shopper really need?
After my talk at the Prodaja na policah conference, I often heard one sentence in conversation: “We’re cutting our range too.” Or: “We know we have too many products in our portfolio.” At a time when discounters are advancing relentlessly, in Slovenia too, that is understandable.
Yet cutting the range only makes sense if it lets the remaining products flourish. And if it frees up space for new successful products.
Cutting the range is not the end goal, but a lever for reaching the real one: a really good offer that shoppers like to come back to.
Why are sales falling?
This case comes from a real store. A supermarket, a seasonal category, 1,259 products with sales. Sales value over the last twelve months: −7.3%.
Every organisation has a ready answer to a signal like this: a promotion, a lower price, a new supplier. Or, more and more often lately, a shorter range. We first looked at how the category was actually built.
Five hits and 600 products in the tail
The five best-selling products sell roughly as much as the 600 weakest ones combined. On average, one hit sells as much as about 115 products from the tail. The bottom 30% of products bring in only 6.2% of unit sales.
This is the insight that works like a key. It reveals the hidden potential for category growth. But it doesn’t yet answer why sales are falling. It shows how the category is built, not what to do with it.
The first reflex: cut the tail?
Faced with a picture like this, almost everyone has the same reflex. Pareto says the tail doesn’t work, so we cut it. In range management this is what a price cut is in sales: quick, proven, and often prescribed blindly.
Cutting blindly has a cost you won’t see in the spreadsheet. The tail also holds products that are the very reason shoppers come to you. Cut them, and it’s not the spreadsheet that misses them. The shopper does.
Where is the potential hiding?
So we put the data next to two other views. The store, seen through the shopper’s eyes, showed:
a) an extended range with very poor rotation, a dusty category tail (and dusty quite literally),
b) hit products in the wrong place,
c) poor visibility during the main season, when the category generated most of its turnover.
And in this category the shopper doesn’t decide from a list, but when looking at the shelf, mostly in a short seasonal peak.
Only together did the three views reveal the real problem: the hits were invisible exactly when shoppers were looking for them. A problem worth solving first.
What did we propose to this retailer?
Not a single measure, but a set of four coordinated levers. A shorter tail frees up space. Stronger heroes fill it: more space, a second placement, reliable stock. A sell-through protocol with clear steps makes sure no stock is left once the season ends. And seasonal visibility makes the hits visible when the opportunity is greatest.
Each lever on its own would only do half the job. A cut without stronger heroes just empties the shelf. Heroes without the season go unnoticed. Only connected measures move the category.
What happened after the reset?
Fewer products, less stock, more sales. With 40% fewer products, sales grew by 10.4%. Sales per product rose by 84%, and stock fell by 70%.
The result matters, but the recipe isn’t universal. In this pilot the set of decisions worked because it was tailored to the role of this particular category at this retailer. A low starting level of order (the “dust”) also helped. That is exactly why the next question matters more than the result:
What if we looked at the same Pareto curve in a discounter, a supermarket or a specialist store? Would we make the same decision?
Note: Stock estimated from shelf measurements.
Would it work for every retailer?
Not necessarily. The same Pareto curve can justify three different right decisions for three different retailers. Picture the following:
The discounter cuts hard and bets everything on the heroes, while also making the most of the season.
The supermarket, as in our case, strengthens the heroes and trims the tail, but keeps its relevant part.
The specialist keeps the tail, because it offers certain kinds of products you won’t find elsewhere. Its risk is offering almost the same as the supermarket – and with that, losing its relevance for the shopper.
Same data. Different shopping missions and strategies. And so, different decisions.
The data tell you where the problem is. Which lever to pull is a decision no dashboard can make.
And what about suppliers?
If the retailer’s strategy drives the decision, the supplier has to know that strategy before coming with a proposal.
The same supplier will propose fewer SKUs and stronger heroes to the discounter, heroes plus the relevant part of the tail to the supermarket, and to the specialist a breadth the shopper can see and understand. A proposal that would suit every retailer usually suits none.
When the retailer announces the cut
Picture a conversation between a retailer and a supplier.
Retailer: “We’re reducing the range in your category. We’re delisting 30% of the products.”
At that point, the supplier has two paths.
Does the supplier’s proposal fit the retailer’s strategy and KPI?
Where is your brand?
The wave of range cuts won’t stop. The question isn’t whether your retailer will cut, but how. And whether you’ll take part, to strengthen the category.
Don't cut before you know what you can strengthen.
● FREE WEBINAR
How to find the right lever for category growth?
The right diagnosis. The right levers. A big shift.
Retailers are cutting their ranges. But a category’s greatest potential may lie not only in the products we remove, but also in the ones we can strengthen. Using the real 5/600 case, we’ll show how data, a look into the store and an understanding of the shopper take us from KPI diagnosis to concrete levers and measurable results. What can your brands contribute?
Leave your name and company in the contact form, mentioning the webinar
How cutting 70% of category inventory increased sales?
SITUATION
Cut Inventory. Increase Sales.
How data, shopper logic and decisive assortment action reduced stock without weakening category performance.
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 Sortimana (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: Sortimana by Omnibus)
… 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
Sortimana 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
Sortimana Insight 2
- 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
Sortimana 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?
Sortimana will help you move 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 Sortimana (Data Engine), including 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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