At the end of Q4 2017, H&M announced that sales had dropped by 2%. In order to move stock, H&M had to further discount their products, which thus narrowed their operating margins.This strategy proved unsuccessful and at the end of Q1 this year, H&M had amassed $4.3 billion dollars worth of unsold stock.
Once a disruption of the fashion retail industry, H&M’s weaknesses are now in the spotlight, highlighting the disparities fast fashion retailers are facing in comparison to ultra-fast fashion retailers that are overtaking them in sales and consumer reach.
The data below details H&M’s sellout rates in comparison to ultra-fast fashion retailer Asos over the Q1 period of 2018. Both Asos and H&M were analysed in the UK market as it is a significant market for the two brands.
As it can be seen in the chart above, throughout Q1 of 2018, Asos’ sellout rates were significantly higher than that of H&M. Whilst neither of the brands managed to reach an average sellout rate of 50% for the months of January and February, this could be a result of slower sales following the holiday period when consumers were less likely to spend.
A deeper look at H&M and Asos’ performance showed that H&M’s discount range was at 50-54% in January and February of Q1, as shown in the tables below. Despite marginally high discounts, this still yielded a very poor sellout rate.
On the other hand, though Asos’ discount range was higher at 60-64%, it was still met with a better sellout rate than H&M, indicating a better response from consumers for Asos rather than H&M. Highlighting consumer’s preferences for Asos over H&M, in March of Q1, Asos had a sellout rate of 61% with a tiny 20-24% discount range on offer whereas H&M only managed a 56% sellout rate with a discount range of 50-54%.
Reasons for Fast Fashion’s Lag
Analysts attributed three main factors to H&M’s current predicament. The first being that H&M, as a fast fashion retailer, operates a “Pull” inventory system which now proves to be slower in comparison to the “Test and Repeat” model used by ultra-fast fashion retailers.
The second factor has been the proximity of their supply. Fast fashion retailers often have production offshore whereas ultra-fast fashion retailers source produce their items onshore. By producing items overseas to the design site (offshore) the supply chain time is elongated in comparison to producing locally to the design site (onshore) which shortens the supply chain time.
The third factor is that fast fashion has been slow to enter the e-commerce market in comparison to ultra-fast fashion retailers, which has resulted in loss of sales as consumers chose online shopping over shopping at brick and mortar shops.
The Leanest Inventory System Wins
The “Pull” Model is an inventory method that determines listed stocks through observing consumer preferences via in-store sales. Through this system, retailers are able to insure sales by meeting an already established demand. This is in contrast to the “Push” method traditional retailers used such as designer labels, whereby they create products and push the items to the consumers.
Source: DeClustered Marketing
The Pull Model has proven to be a very successful method for fast fashion giants such as Zara, H&M and Uniqlo. The pioneering use of consumer data to aid sales enabled these retailers to maintain a lean inventory system, which meant stocking just the right amount available when needed. By maintaining the right levels of stock, fast fashion retailers were able to sell items at full price and reduced the amount of markdowns needed to rid themselves of stock at the end of a quarter.
The “Test and Repeat” Model has taken the “Pull” Model a step farther. Used by ultra-fast fashion retailers such as Boohoo and Asos, this model works by retailers creating a small selection of offerings which they then monitor. Only the product offerings that perform the best amongst consumers are then re-stocked. This entire process is amplified by the use of artificial intelligence, which scans social media websites such as Instagram to gauge what consumers preferences are and giving ultra-fast fashion retailers instantaneous data in comparison to the “Pull” model.
These minuscule differences in the “Test and Repeat” model such as smaller inventory and the use of AI over in-store data has yielded massive results for ultra-fast fashion retailers.In fact, perhaps if the “Test and Repeat” model had been employed by H&M they could have had better understanding of their current consumers needs and avoided their massive stockpile.
Click here to read more about these models and their implications to retail.
Sourcing Your Products is all about Location, Location, Location
The trademark characteristic of fast fashion has always been the speed in which they are able to deliver products from design-to-sale. When they were kings, Fung Global Retail reported that Zara boasted a 5 week cycle, releasing 20 collections a year and H&M from a few weeks to six months, releasing 16 collections a year. These were stark comparisons to traditional retailers which only released 4 collections a year!
Previously, an important factor for fast fashion retailers was the ability to ensure that their thousands of stores worldwide were up-to-date with their ever-changing collections. This meant sourcing products from all over the globe to aid in faster supply and replenishment of stock.
Now however, ultra-fast fashion retailers have proven that the closer to home your supply is, the faster your design-to-store cycle which will continuously keep the distracted buyer entertained with your collections. By the use of onshore supply, labels such as Boohoo, Missguided and Asos are able to quickly offer new styles, shortening lead times.Boohoo has a design-to-store cycle of 2 weeks, Asos boasts 2-8 weeks and according to Missguided, they are able to go from concept to sale in as little as a week.
Source: Company data,Goldman Sachs global Investment Research via Quartz Media
Thus, the resulting effect of offshore supply, though perhaps being cost effective, has elongated lead times for fast fashion retailers in comparison to ultra-fast fashion retailers, ultimately impacting sales and their ability to retain the consumer’s attention.
Fast Fashion’s Digital Delay
The decay of the brick and mortar store has had rippling effects through the fashion industry. As digitisation reigns supreme, those who have moved online may survive longer than their offline competitors. With footfalls decreasing in stores even during sales events like Black Friday, the online store has become an important avenue for retailers to maintain their sales.
The key difference between ultra-fast fashion retailers and fast fashion retailers has been their adoption of e-commerce. In fact, Asos, a pure play online retailer, started using e-commerce a whopping 10 years before Zara went online in 2010 and thus has aided in its ability to remain relevant to the millennial consumer.
Source: Asos Homepage
In fact, despite fast fashion’s target audience preferring online shopping today, these retailers have been slow in their usage of online channels. Asos, which is light years ahead of Zara and H&M, has a dedicated shopping app that contains features such as visual search . Whilst Asos’s projected earning from online sales are at 30-35% growth per year, H&M is just beginning to anticipate a 25% growth rate as they begin to invest in the expansion of their online channels.
As a result of their delay in entering the digital sphere, fast fashion retailers are now playing catch-up to ultra-fast fashion retailers who are excelling in their online retail.
The Lessons Learnt
So what’s the key take away? In the fashion industry, remaining agile and relevant are of utmost importance to aid in your survival. A retailer must master the ability to maintain a lean inventory and supply chain, maximise the use of their online channels and most importantly, they must remain on top of the rapidly changing desires of consumers.
Want to know how you can use intelligence to manage your inventory? Drop us an email at email@example.com and we’ll be in touch!
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The data above was obtained from Omnilytics, real-time market data platform. The numbers and statistics may vary, as the platform is updated every day. The time period of the information taken was between 1st January, 2018 to 31st March, 2018.