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 FashionWhen 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.