The 3 most frequent errors in assortment tracking

Claire Brunaud
4 hours ago
5 min read

Tracking the performance of an assortment may seem simple.

We look at the volumes. We identify the benchmarks that are increasing. We spot those that are declining. We compare with the previous year. Then we adjust.

But in reality, the analysis is rarely so straightforward.

A product line may perform very well overall, but only with a specific type of end customer. A product range may appear underperforming, even though it works very well in certain areas. An innovation may be considered disappointing, even though it simply wasn't rolled out to the right establishments.

For a Category Manager, this is a real issue.

Because monitoring an assortment isn't just about knowing what sells. It's also about understanding what works, where, to whom, and in what context.

Without this careful reading, decisions can quickly become too general: maintaining a reference that no longer really meets demand, withdrawing a product that nevertheless had potential, pushing the same offer everywhere or missing out on a very receptive segment of end customers.

Here are the 3 most frequent errors in assortment tracking, and how to avoid them through better use of sell-out data.

Mistake #1: Analyzing only overall volumes

Volume is often the first indicator looked at.

That makes sense. It allows you to quickly measure the weight of a reference, a category or a range. It gives an initial idea of commercial performance.

But the overall volume can also mask many things.

A product may show strong results because it is supported by only a few retailers. A category may appear stable while experiencing strong growth with some end customers and a decline with others. A product range may appear to be struggling nationally while showing very strong momentum in certain regions.

The risk is making decisions based on an average.

However, an average does not always reflect the reality on the ground.

To properly monitor an assortment, you need to look beyond the total volume. You need to examine performance by warehouse, by period, by product reference, but also by type of end customer.

The real question, therefore, is not simply: “How much did we sell?”

The real question is: “Who is buying, where, how regularly, and which benchmarks are actually driving performance?”

It is this reading that allows us to distinguish a solid product from a product simply supported by a few relays.

Mistake #2: Not segmenting end customers

Not all establishments have the same needs.

A commercial restaurant, a bakery, a community organization, a hotel, a caterer, or a snack food provider do not use products in the same way. Operational constraints change. Expectations change. Purchase frequencies change. Usage patterns change.

However, product ranges are still often analyzed in too uniform a way.

We look at the performance of a benchmark without always knowing what types of establishments it actually works with.

This is a significant limitation.

Because a product can be perfectly suited to one segment, but much less relevant to another. A product line might be very popular with fast food restaurants, but little used in institutional catering. A product range might be underutilized by caterers even though it perfectly meets their needs.

Without detailed segmentation, the Category Manager operates with a partial vision.

He knows what sells.

But he doesn't always know who it's being sold to.

And it is precisely this information that allows for the adaptation of product ranges, better targeting of activations, guidance of sales teams and the building of stronger recommendations for distributors.

A good assortment analysis must therefore incorporate the typology of end customers.

Not as a minor detail.

Like a central reading key.

Mistake #3: Confusing product presence in the assortment with actual performance

A product being included in the assortment is not necessarily a high-performing product.

It may be listed, available, sometimes ordered… but rarely activated. It may only be distributed to certain warehouses. It may only reach a limited number of end customers. It may exist on paper, but have low actual warehouse output.

It is a common mistake to assume that referencing alone is sufficient to validate the relevance of a product.

In reality, what matters is actual performance.

Is the product regularly being sold in warehouses? Is it progressing over time? Is it being adopted by different types of end customers? Does it truly contribute to the category? Is it supported by the right warehouses? Does it deserve to be strengthened, repositioned, or withdrawn?

Without sell-out data, this interpretation is difficult.

Sell-in data shows what has been sold to the distributor. However, it doesn't always reveal what happens next. It can give an impression of performance when the product remains in stock, or conversely, underestimate the actual dynamics within certain warehouses.

Sell-out data allows us to verify what is actually leaving the warehouses.

It helps to distinguish a reference that is merely present from one that is actually activated by the market.

How best to track your product assortments?

Good assortment monitoring does not rely on an accumulation of indicators.

It is based on the right questions.

Which benchmarks are actually progressing?

Which deposits drive performance?

What types of end customers buy the category?

Are some references dependent on a single segment?

Are there areas where the product range is under-exploited?

Do the innovations find their market?

Do the listed products actually leave the warehouses?

These questions allow us to move from descriptive monitoring to actionable analysis.

The goal is not just to know what works .

The goal is to understand why it works, for whom, and how to replicate this performance elsewhere.

KaryonFood: a more nuanced understanding of product ranges

In the Foodservice industry, distributor data is often difficult to use: heterogeneous files, different formats, lack of harmonization, and analyses that take a long time to produce.

KaryonFood allows for the centralization and harmonization of sell-out data to give Category Managers a clearer view of assortment performance.

Teams can analyze warehouse outflows by product reference, depot, period, and end-customer type. They can identify high-performing products, the most receptive segments, underutilized areas, and products that deserve to be strengthened or reworked.

Data then becomes a concrete support for adapting product ranges to market realities.

Conclusion

Monitoring product ranges cannot be limited to a general overview of volumes.

To make better decisions, a Category Manager must understand what sells, where, to whom, and with what dynamics.

The most frequent errors often stem from an overly broad analysis: focusing solely on overall volumes, failing to segment end customers, or confusing assortment presence with actual performance.

With sell-out data, it becomes possible to track assortments in a more precise, pragmatic and useful way.

And with KaryonFood, this data becomes readable, centralized and actionable.

Because a good assortment is not managed solely from what is referenced.

It is managed based on what actually works in the field.