Evolution of Commerce: How AI is Disrupting the Fashion Industry

Karl Lagerfeld’s tech-inspired collection took the fashion world by storm in Paris Fashion Week last year. Chanel’s creative director transformed the runway into cyberspace and showcased fashion’s way of acknowledging the role played by technology in the world of fashion. Fashion-tech, as industry pundits call it, has not just begun. Fashion has embraced almost every tech trend — be it wearable tech, social media and influencer marketing, or even VR and augmented reality. Fashion is constantly evolving and so are the technological trends that aid trade in this ephemeral industry. The impact of AI and technology in fashion, however, is a lot more enduring than the fugacious puffed sleeve or the suave smart watch (even Hermes couldn’t save this one). AI in fashion, has simplified the lives of brands and buyers alike. This post shows you how.

What’s All This About Machine Learning in Fashion

When Uber was introduced a few years back, it represented a whole new way of travelling minus the traditional hassles associated with hiring a cab. A few years later, with a slew of improvements (and competitors) in tow, the Uber-way is the new normal. AI in fashion is headed in the same direction. A number of fashion brands already use machine learning to enhance predictions and improve the search functionality on their sites. Whether this intelligence is built in-house or facilitated by a third-party, it involves training algorithms to predict shopper behaviour with a high level of accuracy based on its understanding of a set of largely consistent and recurring parameters. As more and more sites focus on the need to be super efficient in fetching products, recommending suitable alternatives, and improving the overall shopping experience, customers are almost tuned to expect this to be the default setting. As I started writing this post, I found myself wondering how can fashion — an industry famed for its transience — put neural networks to use?

How can you teach algorithms to accurately predict behaviour in an industry characterised by impermanence?

While 1:1 personalization might not be feasible unless you have years worth of data for each customer, there are a number of factors that are sureshot indicators of a customer’s preferences. Factors such as the number of visits to a website, type of device used for purchase, or geographical location are largely permanent characteristics of a shopper that help brands in gauging their preferences and accordingly tailoring experiences on the site. It will be a while before the Amazon-way becomes the new normal but until then, retailers and online sellers are realising the pressing need to segment and target their customers by efficiently utilising the enormous data goldmine they are sitting on.

Realising the Potential in Visuals

One of the most remarkable AI contributions to fashion was the evolution of visual search. Think about it — your experience while looking for products on sites that offer visual recommendations during search is so much better than on the sites that don’t. An obvious advantage in presenting visual suggestions is that it reduces the number of clicks needed to reach a desired product and enhances the overall shopping experience on the site. However, offering visual recommendations for text based searches relies on the ability of a shopper to accurately word a product query. How about the scenario where visual search presents the right products even without an aptly worded query? Brands like ASOS and Neiman Marcus use visual search to help customers find the products they desire in the absence of a rightly worded shopper query. Customers that find a product they like — either on a website, a catalog or even on the street, snap a picture of it and upload the image to the site. The algorithm then sieves through the product catalog to find appropriate matches for the uploaded image or product. Visual search provides an impetus to mobile commerce as well since they are almost directly connected. Shoppers use their smartphones to click pictures of the products they desire and eventually proceed to purchase. In fact, Asos claims 70 percent of global traffic and over 50 percent of its orders come from mobile devices.

About Chatbots and Round-the-Clock Personal Stylists

A step up to visual search recommendations are chatbots that intelligently advise the shopper about products most suited to them. The high levels of customer engagement made possible with the 24/7 support offered by chatbots is one of its biggest advantages. Luxury brands like Burberry and Tommy Hilfiger were some of the first fashion brands to experiment with chatbots. Bots like Levi’s AI-powered virtual stylist not only offer style advice but also simplify search by intelligently understanding plain text to deduce what a customer is looking for (with search). Product suggestions thrown up by the bot are in sync with the product catalog to ensure all products shown to the customer are part of the existing inventory. Chatbots are immensely more scalable in the current scenario when your customers are not limited to a particular time zone. An additional benefit with chatbots is in the obvious data advantage — the AI continues to learn with each customer interaction, improving the algorithms and making predictions a lot more accurate. Live chat has always been an attractive feature to have for any online business. Research shows customer satisfaction levels are highest with live chat (73%) when compared to email or phone.

Selling to the Connected Customer

The internet and social media explosion contributed significantly to the evolution of fashion shopping as we know it today. Customers can no longer be categorised as exclusively online or all retail, as they operate somewhere in between. A commercial you watch on television can spark an interest in a product, enticing you to visit the brand’s website while you’re on your way to work, and ultimately purchase the product because a post on Instagram tipped you over. Whether they are online or offline, new age customers are ultimately looking for a way to improve their shopping experience. A recent study by NRF revealed the features that tilt the scale in favour of online shopping are buy online and pick up in store options (68%), in-app store navigation (66%), and reliable mobile payment (65%). Enhancements such as visual search, voice search, augmented and virtual reality — though attractive — are still not absolutely critical when it comes to shopping. The learning for brands is this — listen to what your customers are not telling you. While it is necessary for brands to follow their customers, understand that customers trail brands, too. There is a wealth of information hidden in your customer data. The successful sellers of the future will be those that intelligently decipher these clues, whether online or offline, to understand a bit more about their audience. Companies that are capable of catering to the connected customer are the ones that will remain relevant in this new era of commerce. The advances made possible because of the impact of AI in fashion are evidence of how trends evolve to keep up with the changing sentiments of the audience it caters to. Evidently, fashion is no longer just updating its style quotient, it’s taking it up a notch in smartness as well. Find out how AI and personalization can help you serve your customers better through strong site-search, category pages, and accurate recommendations. Get a free, custom-made report that shows how Unbxd can enhance the shopping experience on your site.  Sign up, today!