With endless sources of inspiration and merchandise online, it would seem that today’s fashionistas have it easy. In truth, navigating from Instagram to Pinterest inspiration boards to brand websites, all in search of the perfect dress or pair of shoes, can be a headache—especially if there’s a specific look in mind. But savvy clothing brands are finding ways to help consumers discover their best looks through smart, data-driven marketing.
Stylight, an e-commerce fashion aggregator, pools selected items from more than 100 online stores onto its platform. What makes it unique is the way it refines recommendations: users ‘favorite’ items they like, which further informs what else they are shown. The success and popularity of this model inspired the company to extend its data-driven recommendation system into its cross-channel marketing efforts more broadly.
Because Stylight can’t examine every search request or guess what the market will do next, it began to automate campaigns and expand paid search to more than 140 million keywords based on customer shopping habits. The company also used real-time feedback to adjust bids and media accordingly—in essence, dressing their media in the same way they dress their consumers.
On the consumer side, this means that Stylight serves different campaigns to different audiences. Their marketing team uses Adobe Media Optimizer to break down campaigns by segments and categories. This allows them to find customers looking to spend with specific brands or in certain product divisions and styles. Not interested in a cashmere sweater? Great. Stylight’s optimized media will ensure that you never see any.
And once a campaign is working well on one channel, be it search, display or social, Stylight can see and predict—also in real-time—how that campaign might do across the board. For example, if a campaign is doing well in search, Stylight can simulate how that same campaign will perform when the budget is doubled; or, it can utilize search data to optimize display retargeting. As a result, there’s a greater chance that these consumers will find the perfect pair of shoes or handbag on Stylight.
The more data Stylight collects from campaigns over time, the more likely it is that those campaigns will make a positive impact—both on consumers’ preferences and on the company’s bottom line. For some optimized campaigns, the cost per click decreases by as much as three to four cents after only a few weeks. Furthermore, by making the best of a campaign once it’s running, Stylight has seen conversion rates between 10 and 20 percent.
What’s great about Stylight’s strategy is that the company has made data the ultimate tastemaker. As their target consumers change their tastes and look for something a little different, the company’s data-driven processes help them find what they’re looking for without spending more resources to keep up.
