Careers at Lyst
Lyst’s mission is to make shopping for clothes, shoes, bags, and accessories easier and more fun.
In 2010 Chris Morton was a young venture capitalist at London-based firm Balderton Capital. He shared an apartment with two female professionals who spent much time checking out premium fashion websites. At the time he was involved in the initial public offering of Yoox, an online luxury retailer, so the topic of fashion was prevalent in his life. He began thinkng about it more, and concluded that the process of shopping for clothing online had become impersonal and inefficient.
Morton believed that there should be customized sites for every online shopper, shaped by data. He studied the search technology utilized by travel websites that aggregated thousands of alternatives to help consumers make the best choice. He wanted to create his own site with a similar model, with product and brand inventory customized based on a shopper’s past preferences. He named the site Lyst. The name references how users can cite items they like on wish lists and share them.
In 2010 he left his job to work full-time on Lyst, along with partners Devin Hunt and Sebastjan Trepca. The site used algorithms to aggregate over 12,000 individual and multibrand fashion sites into a single customized location. He launched it later that year, and it became popular over time. Shoppers liked the fact that they did not have to visit numerous sites to find their favorite designs or labels. Within a few years Morton had a core customer base: women between their mid-20s and early 40s who spent between $500 and $1000 per month on clothes.
By 2012, Lyst was generating $1 million in monthly sales. That year, its success enabled it to raise $5 million in Series A funding. Traffic through mobile devices rose from 8% to 25%, encouraging the company to introduce its first mobile app in 2013. Also in 2013, Lyst unveiled a feature that enables customers to buy products from retailers directly through the site. That year, the firm was selected as one of the “UK Future Fifty“, a list of the 50 most promising and fast-growing companies in the country. Lyst now has more than two million users a month, originating from over 180 countries.
Benefits at Lyst
Business model of Lyst
Lyst has a mass market business model, with no significant differentiation between customer segments. The company primarily sells products in two categories: Men and Women.
Lyst offers three primary value propositions: customization, convenience, and brand/status.
The company offers customization by enabling shoppers to create lists featuring their favorite brands. Its technology remembers this content and uses it to make personalized style recommendations and notify users when items of interest go on sale.
The company offers convenience by aggregating over three million products from over 11,000 designers in one place. When it is time to make a purchase, customers just have to click an “Add to Bag” button, allowing them to place an order directly with the retailer through the site.
The company has built a strong brand as a result of its success. Its retail partners include some of the top fashion stores and brands in the world, including Burberry, Saks, J.Crew, Balenciaga, Neiman Marcus, Dolce & Gabbana, Bergdorf Goodman, and Lane Crawford. It is now one of the largest fashion tech firms in the UK, with more than 120 employees and a turnover of over $60 million.
Lyst’s main channel is its website, through which it acquires customers. The company also draws in users through its mobile app. Beyond these two, it promotes its site on its social media pages.
Lyst’s customer relationship is primarily of a self-service, automated nature. Customers utilize the website and mobile app while having limited interaction with employees. The company’s site includes a “Help & FAQs” section with answers to commonly asked questions. It also features a “Sizing Guide” that identifies specific measurements for different size categories (Small, Medium, Large, etc.) in different countries. Moreover, it offers an engineering blog for developers. That said, there is a personal assistance component in the form of phone and e-mail support.
Lyst’s business model entails maintaining a robust platform for its users. The platform includes its website and its mobile app (for the iPhone and iPad).
Lyst’s partners are the retail brands and designers it features on its website. To become a partner a company must request an invitation from the company and receive approval.
Lyst’s main resource is the proprietary software platform on which the site runs. The company also depends on human resources in the form of its technology and customer support staff. Lastly, as a startup it has relied heavily on funding from investors, raising $60.5 million from nine investors as of April 2015; investors include Accel Partners, Balderton Capital, Susa Ventures, and DFJ.
Lyst has a cost-driven structure, aiming to minimize expenses through significant automation and low-price value propositions. Its biggest cost driver is likely transaction expenses, a fixed cost. Other major drivers are in the areas of customer support/operations, administration, and marketing, all fixed costs.
Lyst has two revenue streams:
Transaction Fees – The company makes a commission off of all sales that occur through the site.
Information Fees – The company collects information regarding customer spending and general behavior (e.g., peak browsing hours) and sells it back to brands and retailers
info: Chris earned an M.A. in Natural Sciences from Cambridge University. He previously served as an investor at Balderton Capital and Benchmark Capital and a Business Development Manager at QinetiQ.
info: Sebastjan studied Computer Science at the University of Ljubljana. Prior to co-founding Lyst he served as the VP of Engineering at Noovo, a content-sharing and discovery startup, and as the Technical Lead at Social Expeditions.
info: Eddie studied Computer Science at Lancaster University. He previously worked in finance at Bloomberg. At Lyst, his responsibilities include training computers to recognize specific makes of clothes and to predict customers‘ clothes-buying preferences.
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