London, one of the world’s Big Four fashion capitals, is getting more and more renowned for its successful fashion tech startup scene. And it fully deserves its geek-chic crown.
En vogue companies like Asos, Farfetch, Net A Porter, Lyst and Thread, all born and bred in London, have proven that success comes from adopting tech innovation that leads to great user experience.
E-commerce companies based in London don’t have an affinity for competing on discounts, but on convenience - ranging from fast delivery and diverse products to visual search and discovery, connecting content with commerce, and personalisation through data crunching.
And while there is more hustling than science behind providing good logistics and large assortment, let’s zoom in on the technology trends that make these fashion tech startups glow.
1. Visual search and discovery
Fashion items are not easy to describe in words. These highly visual products need a search and discovery technology to allow users to look out for items in an online shop by simply using an inspirational photo instead of keywords.
With tech advancements in deep learning and computer vision, this became a possibility in recent years. And it allows users to either upload a photo as a search query or browse through integrated social media feeds or user-generated content to find similar items.
And perhaps the first fashion startup that used visual search as a main differentiator was SnapFashion in 2012. Not surprisingly, its founder was not your typical fashionista, but a British computer science graduate.
Asap54 followed suit in 2014, with a fashion app centered around visual search as well. Founded by the wife of Farfetch CEO, the company developed the technology in-house, just like SnapFashion.
Then came Comb in 2015, with a social fashion app centered around visual search and discovery.
Unlike the others, they didn’t develop the technology in-house, but instead went for a ready-made SaaS machine intelligence solution provided by ViSenze, so they can focus on the core of the fashion business and its social elements.
This solution helped them to automatically find visually similar items in terms of shape, style, pattern and color across all their categories ranging from apparel and shoes to bags and accessories - all in less than a second.
Besides these newcomers, Net-A-Porter also adopted the trend in their new app, Net Set, which claims to be world’s first shoppable social network. Their implementation though is more limited, finding only products of the same color, but not of the same type, style or anything else, as reported by econsultancy.
Through this increasing exposure to visual search technology, users will soon start forming a habit and might expect the option from all other e-commerce platforms. As the investor of Buzzfeed, Kickstarter, Pinterest and Skype said it, “the next big thing will start out looking like a toy”.
2. Shoppable fashion content
Fashion Week comes twice a year to London, and people flock to get tickets. At least that was the tradition.
While this openness might be a fad or a true trend, what is for sure a reality is that video and livestreaming is here to stay, and fashion content shared with communities is a true hook.
And the next logical step is how the world of fashion content will collide with e-commerce. The same visual search mentioned previously has the power to make all content shoppable, regardless of whether it comes in the form of photos or videos.
The proof came from Pinterest recently, when launching the visual search functionality on all their pins, helping users to get inspiration from their content stream and find similar products to buy.
Now imagine a world where you sit on your couch watching a catwalk on your tablet and being able to immediately buy anything by clicking on products in the video.
3. Personalization through the power of data
Two London-based fashion e-commerce players that make the headlines lately, Lyst and Styloko with its WantList app, have found another sweet spot for a great user experience: algorithms to provide personalised shopping experience through intense data analysis.
Lyst, a company founded in 2010 that recently raised 40M series C, has half of its staff in data science roles. And their data-driven DNA made Balderton Capital, one of the most renowned fashion tech VCs, invest in them: “Their “fashion is data” vision has allowed them to develop a dynamic platform that provides each customer with a unique shopping experience, driving more customers and offering deeper customer insights to brands and retailers.”
They use data to show users the most relevant products, based on previous purchases and other insights. And they are very knowledgeable about their users, to the point of spotting trends like “Purchases made on a Monday have the highest rate of return. Clothes of red colour have the highest rate of return.”
Styloko launched WantList, a “Tinder for fashion”, and take a similar approach. They build an in-depth style profile for their users based on how they browse through: what they click on, how long they spend looking at an item etc.
Intelligence is definitely a powerful way to wow shoppers through personalisation, improve business metrics, and spot bigger fashion and shopping trends that can attract lots of industry players.