Sephora is the world’s leading specialty beauty retailer, and a pioneer in the field of predictive analytics, topping the Retail Personalization Index two years in a row. The brand’s experience, famed for its playful and customer-centered approach, spans online and offline channels, with 2.3 million visits monthly to its site, a top-ranked Shopping app in the App Store, over 1 million downloads in the Google Play store, and 2,300 physical stores worldwide. But as a an offline-born brand under LVMH’s traditional management, its position as machine learning leader was not always guaranteed.
Founded in 1998, Sephora began to embrace artificial intelligence in its digital product development in the early 2000’s. From personalized email offers, to “you might also like” product carousels on its product display pages, the company committed early on to an experience interwoven with machine learning, giving it an edge over department stores and vertical competitors like Ulta. Beauty, the second-most penetrated category in online shopping after fashion & apparel, was well-suited to the online channel, given consumers’ propensity to test a variety of brands, and rely on peer reviews. Sephora’s clientele, largely female, also reacts well to high-service shopping experiences, which a personalized website emulates. The challenge in beauty – a category with fast replenishment – is to predict what consumers want before they buy it from a competitor.
Today, Sephora’s digital experience is highly individualized, with product-discovery tools and loyalty rewards that are customized 1:1. The site homepage showcases products “recommended for you”, a selection chosen based on a mix of past purchase and browsing activity [Exhibit 1]. Another tab of the homepage curates newly-released products in a “new for you” feed of recommendations. On an individual product page, the inquisitive user can choose between“similar products,” “you may also like” suggestions, and “recently viewed” SKUs, of which the former two are predictive guesses at what she might be seeking [Exhibit 2]. Sephora also recognizes various loyalty tiers based on how much consumers spend per year, and sends customized streams of email with product recommendations based on purchase patterns from this “inner circle” [Exhibit 3]. These efforts have garnered success, with 80% of surveyed Beauty Insiders professing complete loyalty to Sephora .
In the long-term, Sephora’s strategy is to make the entire online UX revolve around individual preferences. In a recent talk, e-Commerce Director Lorenzo Peracchione likened the company’s quest for better personal predictions to a hunt for “gold.” This year, the team re-organized its personnel and customer data architecture around an omni-channel customer strategy. Over the next decade, their aim is to use analytics to understand how users travel from offline to online and back in one continuous journey, and then engineer or stimulate that behavior to make existing customers more valuable to the brand 
Numerous user experiences are ripe for the application of this new strategy. In-store, customers can opt for a Pantone ColorIQ test, in which a specialized camera device snaps a picture of their skin tone, matches the exact color from the Pantone library, and recommends products that suit their unique coloring.  In the long-term, one can imagine that mobile phones replace the special device, and Sephora analyzes ColorIQ inputs in concert with individual shopping behavior and omnichannel trends to determine product suggestions. Another innovative feature from the brand is Virtual Artist, which enables users to apply makeup in AR, and seamlessly shop products [Exhibit 4]. Today this tool is for individual experimentation, but long-term there are clear AI applications, in which the screen could pre-populate with looks that someone with similar hair color/skin tone/or style preference (natural vs. glam) has ordered in the past.
Of course, there is even more Sephora could do to take existing user data and project buying behavior. A recent bevvy of competitors have developed personalized beauty products with individually-formulated skincare and haircare offerings, based on user attributes. In a future state, Sephora could suggest a specialized formula made on-demand rather than recommending SKUs from a lineup of existing brands.
Outstanding questions about the future of Sephora’s tech-enablement and forays into machine learning linger, particularly as it bridges emerging technology and in-store experience. Sephora recently rolled out a program with Google Home Hub enabling users to play Sephora YouTube beauty tutorials on demand. How could Sephora integrate this new data stream into its UX personalization efforts? Does watching blogger content influence average order value, or brand loyalty/frequency? On the flip side, how can Sephora begin to individualize its brick & mortar experience? Is there a larger role for VR/AR to play in bringing the in-store experience and e-commerce experience together? Or could stores and offline marketing materials like their holiday gift guide benefit from an approach fueled by predictive analytics? (784 words)
Source: Sephora iOS app and desktop website in logged in user state, accessed 11/13/2018
Source: Sephora iOS app in logged in user state, accessed 11/13/2018
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Source: Sephora iOS app in logged in user state, accessed 11/13/2018
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