(Header image courtesy of Amazon media vault.)

Refreshed for 2022, our Friday analysis selects one or more news stories from the week in fashion technology, and presents The Interline‘s take on why they matter to our global brand and retail audience. As always, this analysis is also delivered to Interline Insiders by email – and signing up continues to be the best way to get a fresh look at the fashion technology news, completely free, in your inbox every Friday.

Following on from our last weekly analysis, which pulled together a selection of stories from across the industry to explain why fashion brands and retailers require historic levels of confidence in the products they’re sourcing and making, this week’s news bore out just how difficult it is to make the fashion recipe work in such challenging conditions. And it also contains some indicators for how at least one technology giant is taking that recipe back to the source to re-architect the way we think about gathering the data to really quantify demand.

First up was the news that fashion retail has faced a, perhaps unexpected, difficult start to the year. Despite the largest economic upturn since the 1980s (at least in the USA) household names in fashion have seen double-digit falls in their share prices since the start of the year – and even high-performing brands haven’t been spared a similar fate. So while broad economic indicators look positive, industry-specific ones are far less rosy.

This leads to one of two conclusions. Either fashion’s “post-pandemic” recovery was a hollow bounce rather than a sustained upswing. Or, at a more fundamental level, the careful balance of fashion was simply hard to get right before the pandemic, and it remains hard to get right today – but with the added complications of COVID driving up material prices and shipping costs, and casting an ongoing pall of unpredictability over consumer behaviour.

In The Interline‘s opinion, the latter is probably the most true. While it’s likely to take years, if not decades, to unpick the pandemic’s impact on buying behaviours and discretionary spending, the stark fact remains that fashion has always been a complex business to make work, and under the added strain of supply chain disruption, the aforementioned price hikes, and other external forces, it’s increasingly uncertain whether long-established brands will be able to make it work again.

This level of uncertainty is, in at least some cases, what’s driving the slump in fashion stocks. Generally speaking, financial markets don’t like uncertainty, and a near-term horizon that’s difficult to parse, followed by a longer-term horizon that’s equally opaque, isn’t the kind of combination people will rush to stake money on.

As with any uncertainty, though, one way around the problem is to latch onto whatever certainty you can find. And in retail, that certainty comes from reliable, time-honoured metrics: sell-through rates, markdowns, returns, and customer satisfaction. Focusing on these, combined with either manual or data-driven forecasting, has always created the greatest chance of a brand successfully positioning its next assortment.

But even as the theoretical ability for brands and retailers to gather those metrics has risen through the roof with the rise of eCommerce, the sheer volume of information out there has overwhelmed many brands’ abilities to make timely use of it. And while this week saw a landmark ruling against Fashion Nova, with the company becoming the first to fined for blocking negative reviews of its products (it disputes this characterisation), most brand and retail businesses would prefer not to bury consumer insights, but rather to collect them in a more curated way, and to make more effective use of the insights they can generate.

For purely online channels, that data gathering is straightforward. Brands can capture a lot of information from social engagements, basket sizes, drop rates and so on within their own spheres. And from a competitive analysis point of view, there are several different service and platform providers that offer the ability to browse competing brands’ promotions, the rate and the nature of their new product introductions by category, and other indicators that will allow anyone to formulate a retail strategy that will respond to what other businesses are doing.

Where the potential for data gathering falls down, though, has long been in physical retail. Some brands continue to rely on admittedly effective human-gathered intelligence, with store managers providing twice-daily reports, while others have integrated point of sale systems with inventory allocation and other back-end platforms. But these typically fall short of the level of insight it’s possible to obtain online.


Enter Amazon, whose ambitions in physical retail The Interline has covered numerous times before – from both a practical perspective, and from the perspective of what those ambitions represent when one of the world’s biggest unicorns is behind them. Amazon hit the headlines recently after revealing its first physical fashion store: Amazon Style. And from an in-store technology point of view, The Interline actually found it a little underwhelming. Everything you might expect from a technology-led store is there: app integration, QR codes, touchscreens in fitting rooms. But nothing about the announcement was revolutionary from a tech-only perspective.

That was until we considered the store not as a showpiece for bringing Amazon’s considerable software and cloud architecture expertise to bear in a physical premise, but as a laboratory for shopper behaviour. Viewed through that lens, what Amazon is doing not only makes a great deal for the retail giant itself, but also as a way to beat physical retailers at their own game by gathering an unassailable variety of data at the source.

Think about it this way, and the strategy falls into place. What seems at first like a scattershot assortment of brands, price points, and categories quickly crystallises when we look at it as a way for Amazon to capture in-store behaviours – from fixture dwell time to fitting room interactions – across a complete range of segments. And, being Amazon, the chances are good that this set of heterogenous information is going to be used in the development of highly competitive own-brand alternatives in the very near future.

Amazon Style, then, could become a beacon for a model of retail that’s built on greater certainty. Not by being a proving ground for new technology, but by being a research lab for people – the richest source of data there is. And brands who are currently facing a downturn despite their best efforts would do well to realise that the major play here is not for square footage, but for the information density that physical space can pack in.

The Interline Podcast: Coming Next Week

Next week we’ll be unveiling the first episode of The Interline Podcast – which will be available through Apple Podcasts, Spotify, or wherever else you listen to podcasts, as well as being available to listen to right here on The Interline. Serving as a capstone to our January focus on Intelligent Retail (which concludes on Monday with another brand-new exclusive) our premiere episode sees our Editor and an industry guest discussing the topics we’ve analysed here this month, and mapping out how the near (and longer) term future of retail will affect not just what we sell, to whom, and where and why, but the entire concept-to-consumer lifecycle.

New episodes of The Interline Podcast will release monthly from now on, timed to coincide with the end of each month’s editorial topic. Look for Episode 1: Intelligent Retail next week, and Episode 2: The Data-Driven Supply Chain at the end of February. And if you enjoy the show, be sure to follow it on whatever platform you use to listen to audio shows. We look forward to welcoming you as a listener as well as a reader!

For more on The Interline Podcast, and how our editorial strategy is evolving as we expand, read our introduction here.

And the best from The Interline this week:

This week we published an exclusive collaboration, a brand-new deep-dive on the role of Generative Adversarial Networks (GANs, a type of deep learning setup) in fashion, and a set of further predictions for the year from a trend analyst.

First, The Interline and TUKATECH partnered to look at how historic disruption has created a potentially once-in-a-lifetime opportunity to re-evaluate how the apparel industry thinks about production.

This piece centres around an idea that was pioneered more than a decade ago: the communal microfactory, which is a small-footprint, high-capability production facility situated in or near a consumption market, and able to respond to a wide variety of orders quickly. Crucially, the communal microfactory should also be accessible – giving emerging designers and small brands equal access to the same facilities that larger brands can tap into to fulfil regionalised demand.

With the fashion retail industry now wrestling with what a sustained recovery actually looks like, a significant part of catering to micro-fluctuations in demand and capacity will be having the right data to make quick, informed decisions – hence our coverage of intelligent retail this month – but how smart those decisions are won’t matter if the production infrastructure isn’t there to support them. Which is why turning a new page in the story of retail should mean rediscovering proven ideas about manufacturing that new technology has rendered even more compelling than they were before.

Next up this week was a look forward at what the rest of 2022 is likely to hold for fashion. Our Editor has previously published his own thoughts on what’s brands and retailers should be asking from technology this year, but this piece presents trend analyst Elizabeth Shobert’s take on which three forces will be driving fashion forward this year.

Two of those forces are ethical – sustainability and accountability – but one blends the moral and the commercial equal measure: returns. At once a source of huge profitability erosion at the category and collection level, and a significant contributor to fashion’s outsize environmental footprint, the returns problem captures a vital nexus where consumer behaviour, product decision-making, and sustainability all meet.

Finally, this week saw us publish the first exclusive from new Junior Contributor Emma Feldner-Busztin. That piece presents a unique look at a particular tranche of machine learning (GANs) and uses it as a lens through which to examine fashion’s relationship with AI – a street that’s practically paved with friction.

How that friction might – or might not – be eased is just one of the topics that Emma tackles in an article that cuts to the heart of not only how one of the most promising slices of machine learning works, but how creativity and technology are going to interact in the future.