Fashion houses maintain databanks about customers’ shopping habits, for example, in exchange for bespoke service, targeted messaging or streamlined shopping experiences such as those on sites run by Tommy Hilfiger or the aforementioned personal styling service Stitch Fix, which rely on AI algorithms to divine consumer cravings and inform tailored retargeting campaigns.
Generative AI Fashion Week from the French fashion designer Cyril Foiret is just one example of a brand using AI technology to accurately predict trends, create better-fitting garments, reduce returns and waste, magnify marketing campaigns and fatten its bottom line. Community-driven product-making platforms like Clothia are also harnessing AI to revolutionise how clothing is created.
Design
AI can be used by brands to deliver a hyper-personalised shopping experience to their customers, where AI models recommend products to a user based on their purchase history and browsing journey across ecommerce websites, chatbots and in-store displays. This allows brands to offer highly bookmarked content to shoppers while guiding them through their shopping journey and increasing conversions.
By using artificial intelligence, retailers can offer some consumers personalised discounts and product bundles that entice them to acquire more merchandise. AI can also help omnichannel operations by using consumer data to maximise pricing and inventory planning.
Because of its real-time adaptable responsiveness, AI personalisation makes shoppers feel recognised and catered to by companies. And people who feel less frustrated, less time-constrained, less inadequate, more efficient and more understood tend to become more pleased, loyal customers. Imagine a fashion ecommerce site that uses AI for personalisation, allowing it to show shoppers items they might be interested in and recommend something directly to them based on the fact that they have looked at a certain product. You don’t have to search endlessly for your white suit or your white shirt from last year. If you look at it, in theory you will receive an email about it. And if you don’t really like that shirt but you’re interested in something similar – let’s say a white tank-top by the same brand – then you would also be recommended that. And moreover, thanks to Bought Together algorithms, your cart will have gone up from £250 – maybe you weren’t particularly keen on the trousers with the shirt – to £350 or £400 because you’re now buying the tank-top.
Merchandising
AI is essential asset for tomorrow, but already it’s becoming a game-changer for retail today as it helps tackle the challenges of today’s retail, as well as enhancing the customer experience. We’re seeing AI making significant inroads across all aspects of a fashion business, and the rich data that it generates has given rise to increasingly powerful predictive tools, enabling informed decision-making on inventory, pricing and more.
Through AI, fashion brands could find a way to optimise pricing, in addition to automating the supply chain, merchandising, and other operational processes that frequently clutter stores with too much inventory, leading to waste.
Advanced AI systems can even offer more personalised forays into online shopping. The business capability of AI, including its capacity to capture and interpret human language, can allow for AI to read through your site’s product catalogue to deliver results of search queries more in tune with the customer’s search interests – a growing focus as customers insist on marketing messaging matching their intent.
Marketing
From autonomous buying agents to intelligent self-checkout systems, AI is changing fashion retailing in the areas of development, manufacturing, processing, logistics, marketing, sales and customer care. In the short term, AI promises fast growth by reducing costs of operation and increasing competence and efficiency in fashion retailing. AI offers exciting opportunities that fashion retailers cannot ignore, but they must be cautious not to exploit the repercussions resulting from development and usage of data and algorithms, and the unintended consequences of AI automation.
Styling and Visual Merchandising: machine-powered digital styling solutions such as the platform Stylitics help shoppers to choose attires, items and bundles that convert and upsell. They use machine learning algorithms to identify and process customer data into a style profile for each individual consumer – then use that research to recommend appropriate or personalised outfits, products, landing pages, emails or marketing campaigns.
And there is now software that acts as an invaluable collaborator in the design process, suggesting styles, algorithms that help maximise the yield on a bolt of fabric, and minimise its material wastage and the production line bottlenecks. Photo recognition software allows fashion retailers to offer search facilities on their sites and apps whereby customers can supply an image of the item they’re looking to buy, or give a broad description verbally.
Personalized Shopping Experiences
By providing highly personalised interactions at scale, fashion brands offering these experiences have the opportunity to build greater experiences with customers incrementally, over time. Artificial Intelligence is a pathway to this, whether in product recommendations or virtual try-ons across the customer journey.
And fashion brands can start to embrace intelligent search on their websites, equipped with semantic interpretation capabilities, that ranks results based on context instead of keywords so that customers can get to products matching the style and fit of their desire right away.
AI can even help to conduct market research through trends discovered in social media and forums, resulting in a more competitive and strategic approach. Mobile Tailor generates 3D human body models and more than 80 precise measurements in seconds, which can remove the guessing game around clothing sizes in order to improve online shopping experiences.