Speed to Sellout: Comparing Zara vs H&M

Speed to Sellout: Comparing Zara vs H&M

Written by Leong KexinJuly 30, 2020

Speed to Sellout: Comparing Zara vs H&M

Understanding speed to sellout is crucial, as it helps with the observation of stock movements and market demands. Here, we pit two fast fashion giants to find out which brand has the supreme sell-out performance: Zara vs H&M.

A speed to sellout rate is derived by comparing the first sellout date of an item against its launch date.

High speed to sellout rate – When a specific item goes out of stock quickly upon its release, indicating warm response from consumers. However, an item could sell out in a short span if the retailer chooses to set a limited amount of SKU, creating a false sense of high demand.

Low speed to sellout rate – Generally suggests lower interest levels from consumers. However, a longer speed to sellout rate could also suggest higher efficiency in inventory management.

In isolation, a quick sellout rate is ultimately ideal. However, when cross analysed against different metrics and factors, jarring results may be uncovered.

Zara vs H&M: Sell-out Rates

We used two of the biggest names in fast fashion, Zara and H&M, to analyse the difference between their trade performances and how it affected their speed to sellout rate.

Zara vs H&M's trade performance by week.
Zara vs H&M’s trade performance by week. Source: Omnilytics Dashboard

The chart above depicts Zara vs H&M’s sell-performance against total number of SKUs within a month.

Evidently, Zara had a higher sell-out rate compared to H&M. The Swedish brand was unable to lift its sell-out beyond 15% at its peak, whereas Zara reached 30% by the fourth week despite both brands having a similar number of SKUs.

In an ideal situation, high sell-out rates against the total assortment indicate a healthy sell-through.

However, this only paints the surface of what speed to sellout initially signifies when viewed from a singular point of view.

We can achieve a more accurate portrayal of speed to sellout rate when cross analysed against different variables.


Zara vs H&M: Stock movement of a product from Zara.
Stock movement of a product from Zara. Source Omnilytics Dashboard

The image above illustrates the stock movement of an item from Zara, with frequent replenishments with multiple sizes.

At the beginning of 2020, the product had sold out twice but was replenished and remained in stock until mid-February. From February onwards, the intervals between ‘out of stock’ and ‘replenished’ tend to be close together.

The time between the product is available until it is out of stock is how we begin to decipher the product’s speed to sellout.

The product then continues to go in and out of stock until April, where it was unavailable for nearly a month before its replenishment.

In other words, even though the product had been launched prior, it was still able to sell out quickly.

This indicates a high speed to sellout rate and healthy replenishment cycle.

Stock movement of a product from H&M
Stock movement of a product from H&M. Source Omnilytics Dashboard


Next, we analyse the stock movement of a product from H&M.

In contrast, the intervals between the product’s availability are wide. Throughout the period of analysis, the product had only gone out of stock thrice.

At the start of the product’s cycle, it achieved a relatively high speed to sellout rate as it went out of stock twice within the first two months. However, after its replenishment in December, the product’s stock movement went stagnant.

Furthermore, in the first chart, H&M’s low overall sell-out rate indicates signs of overstocking – a crisis H&M is no stranger to.

The key difference between the two fast fashion retailers is the end result of their respective stock movements. After cross analysing the overall sellout rate, replenishment strategy and stock movement of key products, it’s clear that H&M had a poorer sell-out performance when compared against Zara.


Understanding the implications behind a speed to sellout rate is crucial.

This allows retailers to gain a clearer understanding of how competitors are performing – and how to effectively plan for product launch strategies with accurate insight.

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