{"id":5400,"date":"2020-01-29T00:00:00","date_gmt":"2020-01-29T00:00:00","guid":{"rendered":"https:\/\/hedgehoglab.com\/why-retailers-are-taking-fashion-advice-from-ai\/"},"modified":"2023-11-30T14:55:58","modified_gmt":"2023-11-30T13:55:58","slug":"why-retailers-are-taking-fashion-advice-from-ai","status":"publish","type":"post","link":"https:\/\/hedgehoglab.com\/why-retailers-are-taking-fashion-advice-from-ai\/","title":{"rendered":"Why retailers are taking fashion advice from AI."},"content":{"rendered":"\n

The fashion industry is big business\u201a one of the biggest out there in fact. Worth $2.4trn globally<\/a> at last count\u201a the fashion industry would be bigger than all but six of the world\u2019s largest economies in terms of GDP\u201a beating out the likes of France\u201a Brazil and Canada.<\/p>\n\n\n\n

With such economic might it\u2019s unsurprising that\u201a much like its fellow superpower economies China and the USA\u201a retailers in the industry have been investing heavily in artificial intelligence (AI).<\/p>\n\n\n\n

In an era of fast fashion\u201a knowing what the customer wants and getting it to them as quickly as possible is vital to staying competitive\u201a and AI can help retailers do this more efficiently than ever.<\/p>\n\n\n\n

Upstarts like ASOS and subscription delivery services like Thread and Stitch Fix have been shaking up the status quo thanks to their embracing of this new technological frontier and have got the incumbents rattled.<\/p>\n\n\n\n

Whether its powering a product recommendation engine or utilising machine vision to help users find the style they want\u201a AI is changing the way we shop in ways that are even seeping out into real-world retail sites.<\/p>\n\n\n\n

<\/p>\n\n\n\n

AI-powered recommendations<\/h3>\n\n\n\n

One of the trailblazers in applying AI to fashion retail has been UK fast-fashion giant ASOS. The retailer began experimenting with AI and machine learning back in 2016 before quickly integrating the tech throughout its operations and services. Chatbots and visual search utilising a smartphone camera are just two fruits of the organisation\u2019s AI drive that customers can currently benefit from.<\/p>\n\n\n\n

In more subtle ways data and machine learning are also quietly transforming the way ASOS\u2019s customers interact with the site and choose their own styles. With over 5\u201a000 new products added every week\u201a the retailer is waging a constant battle to balance choice with the need to not overwhelm its customers \u2500 especially when 76 percent of visits are coming from mobile devices.<\/p>\n\n\n\n

To combat this problem the company has been honing its algorithms in an attempt to only show customers items that they want to see. AI-powered recommendations are complemented by data crunching of customer orders and returns to gauge appropriate sizes and fitting.<\/p>\n\n\n\n

Its visual search feature\u201a which it launched in 2017\u201a allows users to snap a photo of an item they\u2019re interested in so that the ASOS app can recommend similar pieces. As with all machine learning algorithms it\u2019s learning all the time\u201a so the more images that are fed into the system the better it gets at recommending relevant items of clothing.<\/p>\n\n\n\n

Of course\u201a ASOS aren’t the only ones in the sector to be dabbling in this sort of tech. German fashion retailer Zolando\u00a0signalled its move into AI<\/a>\u00a0at the end of 2017 with personalised product recommendations based on whatever a user had in their basket. Meanwhile\u201a Italian giant Yoox Net-a-Porter recently\u00a0cut the ribbon<\/a>\u00a0on a new 500-headcount ‘Temple of innovation’ in London whose remit has AI at its core.<\/p>\n\n\n\n

<\/p>\n\n\n\n

Wardrobe in a box<\/b><\/h3>\n\n\n\n

Shopping patterns have morphed over the years as ecommerce has come to dominate retail around the world. Although the degree to which online spend has overtaken in-store spend differs from country-to-country\u201a the trend towards online shopping is set to continue inexorably in the coming years; reaching $4.8trn globally by 2021<\/a>.<\/p>\n\n\n\n

Convenience is one of the obvious drivers of this growth alongside cost and range. Pushing this idea of convenience even further are subscription services which are stripping back the browsing experience to the point where shoppers aren\u2019t even doing any browsing at all.<\/p>\n\n\n\n

Services such as Thread in the UK and Stitch Fix in the US provide users with customisable boxes of new clothes which users have the option of either keeping or returning. Customers are given questions to determine their preferences\u201a asked to send in images of themselves along with items they already own and their budget. A personal stylist then sends recommendations and tips based on their selections.<\/p>\n\n\n\n

\"[object<\/figure>\n\n\n\n

Photo by <\/b>Katie Laird<\/b><\/a> on <\/b>Flickr<\/b><\/a><\/b><\/p>\n\n\n\n

Every user is assigned their own \u2018virtual stylist\u2019 but it would be unrealistic to assume that each personal shopper could sift through the preferences and wardrobes of the thousands of customers that are placed in their sartorial charge.<\/p>\n\n\n\n

Take Stitch Fix. The publicly-listed company claims it has shipped<\/a> to 2.5m customers in the past year and boasts a line-up of around 3\u201a000 stylists. A rudimentary calculation would suggest that each stylist is responsible for around 833 Stitch Fix customers\u201a so how does the platform provide the personalised experience it prides itself on?<\/p>\n\n\n\n

Considering the thrust of this post\u201a you won\u2019t be surprised to discover that it\u2019s algorithms and data analysis that are fuelling much of the personalisation that\u2019s going on in the background.<\/p>\n\n\n\n

Using customer data\u201a machine learning algorithms suggest selections that it thinks suits each user\u2019s preferences and the stylist handpicks the five most suitable to send as part of a delivery. <\/p>\n\n\n\n

\"[object<\/figure>\n\n\n\n

Photo by <\/b>rawpixel<\/b><\/a> on <\/b>Pixabay<\/b><\/a><\/b><\/p>\n\n\n\n

Data collection and analysis doesn\u2019t just end with garment selection either. Which items a customer keeps or returns are also monitored and fed into the system to inform even better choices next time and drive down the volume of returns.<\/p>\n\n\n\n

As with all machine learning systems\u201a the system is learning all the time and the more customers that are filtered through the better it gets at predicting what a customer will like. At least in theory (the company doesn\u2019t provide figures on customer returns).<\/p>\n\n\n\n

It\u2019s feasible that within a few years human stylists could be done away with entirely as increasingly sophisticated algorithms take charge of everything from garment selection to filtering and fine-tuning. Combined with other data stores such as your entire purchase history or even your own personal calendar\u201a the chore of buying clothes could quickly become a thing of the past.<\/p>\n\n\n\n

<\/p>\n\n\n\n

Alexa: Your personal stylist<\/h3>\n\n\n\n

This utopian vision of a future free of browsing through sales racks\u201a jostling with bargain hunters or incessant scrolling is nearer than we might think.<\/p>\n\n\n\n

In early 2018\u201a Amazon heralded the full consumer launch of its Amazon Echo Look device which combines its personal assistant Alexa in conjunction with a 5-megapixel depth-sensing camera that is capable of taking photos and videos.<\/p>\n\n\n\n

Alongside the Amazon Look app\u201a the new device uses computer vision (CV) and machine learning to suggest potential outfits and combinations based on the user\u2019s body shape as well as offering a style check feature which rates what you are currently wearing.<\/p>\n\n\n\n

Reaction was mixed when the device was originally unveiled in 2017 but viewed in conjunction with the recent launch of the ecommerce giant\u2019s \u2018try before you buy\u2019 fashion service Prime Wardrobe in the US\u201a Amazon\u2019s intentions become abundantly clear.<\/p>\n\n\n\n

\"[object<\/figure>\n\n\n\n

Photo by Amazon<\/b><\/p>\n\n\n\n

Despite lacking the complex personalisation options and range of styles offered by personalised shopping platforms (customers currently select their own items and fill their own baskets on Amazon)\u201a it\u2019s clear that the new service is a testing ground for a potential challenge to the likes of Stitch Fix and Thread.<\/p>\n\n\n\n

It\u2019s also a glimpse into a future where AI doesn\u2019t just take our own preferences or the things we say we like into account\u201a but makes its own decisions based on what it sees mixed with current trends.<\/p>\n\n\n\n

It might seem that all these shiny innovations are always directed at getting people to spend more money on more stuff\u201a particularly new releases from Amazon. That\u2019s definitely at play here\u201a but the new style match feature also poses the interesting possibility of suggesting new combinations of clothes you already own- potentially cutting down spend and getting more mileage of your current threads. \u00a0<\/p>\n\n\n\n

<\/p>\n\n\n\n

Smart mirrors and in-store recommendations<\/h3>\n\n\n\n

Our focus so far has been targeted at online shopping but\u201a despite tales of its demise\u201a 88 percent of UK retail sales and 92 percent of US sales are still at brick and mortar retailers. Evidently\u201a many people still prefer the in-store experience where they can feel and see what they are buying. And if recent experiments by Alibaba in China or Amazon in the US are anything to go by\u201a in-store retail won\u2019t be immune to AI disruption either.<\/p>\n\n\n\n

In 2017\u201a tech giant Alibaba pioneered new smart mirror technology as part of Singles Day\u201a a major shopping event in China that occurs every November. Rolled out across 13 retail stores across the country\u201a the firm\u2019s FashionAI platform is built into screens fitted within changing rooms. Any items a shopper brings into the changing rooms are automatically scanned so that the screen can provide product recommendations based on their selections.<\/p>\n\n\n\n

\"[object<\/figure>\n\n\n\n

Photo by <\/b>Becca McHaffie<\/b><\/a> on <\/b>Unsplash<\/b><\/a><\/b><\/p>\n\n\n\n

Also built into the smart screen is an orange button which shoppers can press to summon a shop assistant with the relevant items suggested by the platform. The system melds online convenience with the benefits of in-store shopping and points towards a future where the lines between in-store experience online are increasingly blurred.<\/p>\n\n\n\n

The logical destination of smart mirror technology is to allow customers to try on clothes without ever having to step into a changing room. In future\u201a consumers will be able to cycle through a list of clothes\u201a even clothes that aren\u2019t available in-store\u201a and algorithms will adapt their reflection to instantly map the chosen items on the consumer.<\/p>\n\n\n\n

<\/p>\n\n\n\n

Cutting out the noise with AI<\/b><\/h3>\n\n\n\n

The increasingly widespread adoption of AI in fashion retail is driven\u201a like most innovations\u201a by one overriding driver: convenience.<\/p>\n\n\n\n

It\u2019s the convenience of being given the most relevant recommendations at the right time\u201a it\u2019s the convenience of being shown only the most appropriate selections that are specifically relevant to you and it\u2019s the convenience of being suggested complementary items that go with your current outfit without you even having to think about it.<\/p>\n\n\n\n

\"[object<\/figure>\n\n\n\n

Photo by Classic Films on <\/b>Flickr<\/b><\/a><\/b><\/p>\n\n\n\n

To paraphrase (or bastardise) the words of Ray Liotta\u2019s character in late-80s classic Field of Dreams<\/i>: \u201cIf you make it convenient\u201a they will come\u201d. The simple fact is\u201a in the age of the smartphone\u201a consumers are all about instant gratification and convenience. AI and machine learning innovations are capable of providing both.<\/p>\n\n\n\n

The size and competitiveness of the fashion retail market has proven a fertile testing ground for AI innovations\u201a with retailers trying out compete each other on features\u201a personalisation and usability.<\/p>\n\n\n\n

As the technology becomes even more advanced and intertwined in our lives you\u2019d do well to keep an eye on the fashion industry\u201a which is proving itself to be a real AI bellwether. Not to mention bang on trend.<\/p>\n","protected":false},"excerpt":{"rendered":"

The fashion industry is big business\u201a one of the biggest out there in fact. Worth $2.4trn globally at last count\u201a the fashion industry would be bigger than all but six of the world\u2019s largest economies in terms of GDP\u201a beating out the likes of France\u201a Brazil and Canada. With such economic might it\u2019s unsurprising that\u201a much like […]<\/p>\n","protected":false},"author":2,"featured_media":3099,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[61],"tags":[191],"acf":[],"yoast_head":"\nWhy retailers are taking fashion advice from AI. | hedgehog lab<\/title>\n<meta name=\"description\" content=\"Artificial intelligence is changing the retail fashion industry. 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