Everyone likes to look good. Ideally, we’d each have a personal stylist—an expert who knows our style, taste, and size, who rapidly surveys all the latest collections and picks out pieces they know will flatter us.
While we can’t all have a personal stylist, we can all use Lyst, an online platform designed to do everything a stylist does and much, much more.
This London-based company, founded in 2011, offers “the world of fashion in one place.” That means building a massive data aggregator with millions of products, from 11,000 designers and brands, all curated just for you. Lyst’s personalized recommendations show customers what they want to see, while the handy “universal cart” feature allows shoppers to place orders with multiple retailers in a single transaction.
And place orders they do. The company now attracts 60 million global shoppers annually. To manage all those customers while still delivering high-quality service, Lyst wears the New Relic Digital Intelligence Platform with pride.
In the beginning, Lyst had no performance monitoring solution in place. For Lead Operations Engineer Igor Serko and his colleagues, that created a lot of extra work. “We relied on parsing website logs and trying to identify requests that were taking too long,” Igor recalls. “We didn’t have a way to be more proactive about identifying and fixing performance issues.”
Thankfully, deploying New Relic APM quickly lightened the load. “As soon as we began using New Relic, all of our performance issues were suddenly very visible,” says Igor. “We could then go down the list and mitigate or fix them.”
Separate and simulate
Two years ago, Lyst decided to shift away from its monolithic application architecture, splitting features into separate microservices in order to speed developments and improve reliability. “New Relic helped us by not only tracking each one of them separately, but [also letting] us visualize the different services together and monitor performance across the entire environment.”
Meanwhile, on the frontend, Lyst engineers rely on New Relic Synthetics to help them improve response times and optimize the digital customer experience. For example, Lyst uses Synthetics to continually conduct simulated sign-ups and sign-ins. Any issue will trigger an alert, allowing Lyst to take care of the problem long before it can impact users.
“We are heavy users of New Relic’s alerting,” says Igor. “We have alerts set up to track most of our servers, each microservice, and key transactions. We want to know the minute anything goes berserk.”
Clearly, Lyst is working hard to ensure an optimal experience for its customers. But, like fashion designers determined to make each collection more memorable than the last, Igor and his colleagues are always looking for new ways to improve. So, what’s this season’s focus? “We want to deliver the best mobile customer experience possible,” says Serko.
Lyst knows New Relic makes the perfect accessory, and that optimizing performance will never go out of style. To learn more, try the full Lyst customer case study on for size: Lyst Curates the Latest Styles with Help from New Relic.