Two dimensions of an assortment
An assortment is described by two independent quantities.
Width is how many different product groups the store carries. It answers the question of how many different shopper needs the retailer covers.
Depth is how many variants exist inside a single group: models, brands, sizes, volumes, colourways. It answers how precisely the store covers one specific need.
Width = number of product groups (categories) in the matrix
Depth = Total SKUs / Number of product groups (average depth per group)
The average depth is useful only as a first bearing: it hides the skew. Real catalogs usually contain a few categories absorbing most of the SKUs and a long list of categories holding a dozen items each. So the distribution is read alongside the average — how many categories hold fewer than 10 items, and what share of SKUs sits in the five largest groups.
Working with both dimensions is the job of category management, but the consequences land on the site: it is the interface that decides whether a shopper can actually use the choice on offer.
Standard strategies
| Strategy | Width | Depth | Example | What it means for the site |
|---|---|---|---|---|
| Niche store | Narrow | Medium–high | A single-sport store, a mono-brand | Simple navigation, the weight sits on the product page and expert content |
| Category killer | Narrow | Maximum | A large chain in one category (electronics, DIY) | Facets and comparison are critical, search carries most of the traffic |
| General retailer | Wide | Medium | A hypermarket, a multi-category store | The homepage has to route rather than sell |
| Marketplace | Maximum | Maximum | A platform with third-party sellers | Duplicates and near-duplicates, relevance is decided by algorithms |
| Discounter | Medium | Minimum | A hard discounter with a limited matrix | Choice is narrowed deliberately, the interface is simple, filters matter little |
No strategy is inherently better than another — they distribute the load differently. A narrow, deep assortment shifts the load onto the instruments that narrow choice; a wide, shallow one onto routing the shopper between categories; and both dimensions at maximum demand each at once.
The paradox of choice
Growing depth brings an obvious benefit: a higher probability that the variant a shopper needs exists in the catalog at all. And an equally non-obvious cost: every new variant increases the work a shopper has to do in order to choose.
The mechanism is simple. People look at the first 10–20 items of a listing. If the assortment doubles in depth and the product order does not change, the shopper sees the same head — only now with twice as many unseen items behind it. Nominally choice has grown; in fact it has not.
The instruments that turn depth into genuinely accessible choice:
- Faceted search. Filters by brand, size, price and attributes are the basic way to narrow hundreds of items down to a dozen. The requirement for deep catalogs is complete and reliable attributes, otherwise the filters cut out relevant products.
- Meaningful sorting. Sort by popularity on a deep catalog preserves the head: the popular is shown because it has been shown.
- Listing personalization. Reordering products around one shopper’s preferences pays off in proportion to the depth of the category: in a category of 20 items the order barely matters, in a category of 2,000 it decides everything. In fashion cases such personalization delivers +12.3% AOV, in grocery +7.5% CR.
- A clear category structure. If the depth has accumulated inside one flat category, no sorting will rescue it: the category has to be split.
Before extending depth, check how many items in the current category receive at least one impression in listings. If a substantial part of the catalog is already invisible to shoppers, adding new SKUs increases the warehouse, not the choice.
What depth means for algorithms
A deep assortment changes what recommendation mechanics have to do.
First: the long tail grows — the share of products with few interactions. Those items carry almost no behavioural statistics, so algorithms trained on clicks and purchases systematically underrate them. It is a self-sustaining loop: the product is not shown because there is no data, and there is no data because it is not shown.
Second: catalog coverage falls — the share of products that make it into at least one recommendation. A metric of little consequence for a 500-item store becomes the key one at tens of thousands.
Third: the cost of uniform blocks rises. When a category holds 40 near-identical items, a block of five options from one brand in one price band does not help anyone choose. This is where recommendation diversity works — deliberately widening the block by brand, price and attributes.
| Category depth | What becomes critical |
|---|---|
| Up to ~50 items | A clear structure, basic sorting |
| 50–500 items | Faceted filters, attribute quality |
| 500–5,000 items | Listing personalization, recommendation coverage |
| Over 5,000 items | Long-tail work, block diversity, duplicate control |
The bands are indicative — the specific thresholds depend on the category and on how distinguishable the products inside it are to a shopper. The general pattern is stable: with every order of magnitude, responsibility for the choice shifts from the shopper to the algorithms.
How to assess an assortment from the site’s side
- Count width and average depth, then the distribution of SKUs across categories — an average without a distribution is useless.
- Measure the share of items that received at least one listing impression in a month, and the share with sales over a quarter.
- Check attribute completeness in the deep categories: incomplete attributes switch the filters off for part of the range.
- Assess recommendation coverage of the catalog — how many products make it into blocks at all.
- Compare conversion across categories of different depth: a slump in the deepest ones almost always points at navigation rather than demand.
- Before extending the matrix, answer which instrument the shopper will use to narrow the choice — and deploy it before the new batch of SKUs arrives.
- After the extension, re-measure the same metrics: a rising item count with a flat share of displayed products means only the warehouse has grown.