{"id":33332,"date":"2018-11-13T19:26:04","date_gmt":"2018-11-14T00:26:04","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/beauty-in-the-age-of-personalization-sephoras-ai-approach\/"},"modified":"2018-11-13T19:26:04","modified_gmt":"2018-11-14T00:26:04","slug":"beauty-in-the-age-of-individualism-sephoras-data-driven-approach","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/beauty-in-the-age-of-individualism-sephoras-data-driven-approach\/","title":{"rendered":"Beauty in the Age of Individualism: Sephora\u2019s Data-Driven Approach"},"content":{"rendered":"

Sephora is the world\u2019s leading specialty beauty retailer, and a pioneer in the field of predictive analytics, topping the Retail Personalization Index two years in a row.[1]<\/a> 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[2]<\/a>, a top-ranked Shopping app in the App Store, over 1 million downloads in the Google Play store[3]<\/a>, and 2,300 physical stores worldwide.[4]<\/a> But as a an offline-born brand under LVMH\u2019s traditional management, its position as machine learning leader was not always guaranteed.<\/p>\n

Founded in 1998, Sephora began to embrace artificial intelligence in its digital product development in the early 2000\u2019s. From personalized email offers, to \u201cyou might also like\u201d 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[5]<\/a>, was well-suited to the online channel, given consumers\u2019 propensity to test a variety of brands, and rely on peer reviews. Sephora\u2019s clientele, largely female, also reacts well to high-service shopping experiences, which a personalized website emulates.[6] <\/a>The challenge in beauty – a category with fast replenishment – is to predict what consumers want before they buy it from a competitor.<\/p>\n

Today, Sephora\u2019s digital experience is highly individualized, with product-discovery tools and loyalty rewards that are customized 1:1. The site homepage showcases products \u201crecommended for you\u201d, a selection chosen based on a mix of past purchase and browsing activity [Exhibit 1<\/strong>]. 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\u201csimilar products,\u201d \u201cyou may also like\u201d suggestions, and \u201crecently viewed” SKUs, of which the former two are predictive guesses at what she might be seeking [Exhibit 2<\/strong>]. 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 \u201cinner circle\u201d [Exhibit 3<\/strong>]. These efforts have garnered success, with 80% of surveyed Beauty Insiders professing complete loyalty to Sephora\u00a0[1]<\/a>.<\/p>\n

In the long-term, Sephora\u2019s 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\u00a0[7]<\/a><\/p>\n

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. [8]<\/a> 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<\/strong>]. 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.<\/p>\n

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.[9]<\/a><\/p>\n

Outstanding questions about the future of Sephora\u2019s 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.[10]<\/a> 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)<\/p>\n

 <\/p>\n

Exhibit 1<\/strong><\/p>\n

Source: Sephora iOS app and desktop website in logged in user state, accessed 11\/13\/2018\"\"<\/p>\n

Exhibit 2<\/strong><\/p>\n

Source: Sephora iOS app in logged in user state, accessed 11\/13\/2018<\/p>\n

\"\"<\/a><\/p>\n

Exhibit 3<\/strong><\/p>\n

Source:\u00a0Sephora.com. (2018).\u00a0About Beauty Insider<\/i>. [online] Available at: https:\/\/www.sephora.com\/about-beauty-insider [Accessed 13 Nov. 2018].<\/p>\n

\"\"<\/a><\/p>\n

 <\/p>\n

Exhibit 4<\/strong><\/p>\n

Source: Sephora iOS app in logged in user state, accessed 11\/13\/2018<\/p>\n

\"\"<\/a><\/p>\n

[1]<\/a>\u00a0Sailthru. (2018).\u00a0Sephora Retail Personalization & Customer Experience Score<\/i>. [online] Available at: https:\/\/www.sailthru.com\/personalization-index\/sephora\/ [Accessed 13 Nov. 2018].<\/p>\n

O’Brien, M. (2018).\u00a0Sephora Retail Personalization Analysis<\/i>. [online] Sailthru. Available at: https:\/\/www.sailthru.com\/marketing-blog\/sephora-personalization-index\/ [Accessed 14 Nov. 2018].<\/p>\n

[2]<\/a> SimilarWeb. (2018).\u00a0Sephora.com Traffic Statistics<\/i>. [online] Available at: https:\/\/www.similarweb.com\/website\/sephora.com#overview [Accessed 13 Nov. 2018].<\/p>\n

[3]<\/a>\u00a0iTunes App Store. (2018).\u00a0\u200eSephora: Beauty, Makeup & Hair<\/i>. [online] Available at: https:\/\/itunes.apple.com\/us\/app\/sephora-beauty-makeup-hair\/id393328150?mt=8 [Accessed 13 Nov. 2018].<\/p>\n

Google Play Store. (2018).\u00a0Sephora – Makeup, Skin Care & Beauty Shopping 💄<\/i>. [online] Available at: https:\/\/play.google.com\/store\/apps\/details?id=com.sephora&hl=en_US [Accessed 13 Nov. 2018].<\/p>\n

[4]<\/a> Sephora.com. (2018).\u00a0SEPHORA \u2261 Maquillage \u22c5 Parfum \u22c5 Soin \u22c5 Beaut\u00e9<\/i>. [online] Available at: https:\/\/www.sephora.com\/about-us [Accessed 13 Nov. 2018].<\/p>\n

[5]<\/a>\u00a0Nickalls, S. (2018).\u00a0Infographic: How Women Want Tech to Shape the Shopping Experience<\/i>. [online] Adweek.com. Available at: https:\/\/www.adweek.com\/digital\/infographic-how-women-use-tech-to-shop\/ [Accessed 13 Nov. 2018].<\/p>\n

[6]<\/a> Brennan, B. (2018).\u00a0How To Win With Women Consumers In 2017<\/i>. [online] Forbes. Available at: https:\/\/www.forbes.com\/sites\/bridgetbrennan\/2016\/12\/31\/how-to-win-with-women-consumers-in-2017\/#3d01c7c85122 [Accessed 13 Nov. 2018].<\/p>\n

[7]<\/a> Milnes, H. (2018).\u00a0Why Sephora merged its digital and physical retail teams into one department – Digiday<\/i>. [online] Digiday. Available at: https:\/\/digiday.com\/marketing\/sephora-merged-digital-physical-retail-teams-one-department\/ [Accessed 13 Nov. 2018].<\/p>\n

Tay, V. (2018).\u00a0Sephora marketer on the 3 musts on mobile<\/i>. [online] Marketing Interactive. Available at: https:\/\/www.marketing-interactive.com\/sephora-marketer-on-the-3-musts-on-mobile\/ [Accessed 14 Nov. 2018].<\/p>\n

[8]<\/a> Holson, L. (2018).\u00a0How Sephora Is Thriving Amid a Retail Crisis<\/i>. [online] Nytimes.com. Available at: https:\/\/www.nytimes.com\/2017\/05\/11\/fashion\/sephora-beauty-retail-technology.html [Accessed 13 Nov. 2018].<\/p>\n

[9]<\/a> Pearl, D. (2018).\u00a0Why Customizable Hair Care, the Latest Trend in Beauty, Is All Over Your Feed<\/i>. [online] Adweek.com. Available at: https:\/\/www.adweek.com\/brand-marketing\/why-customizable-hair-care-the-latest-trend-in-beauty-is-all-over-your-feed\/ [Accessed 13 Nov. 2018].<\/p>\n

[10]<\/a> Williams, R. (2018).\u00a0Sephora, Google dive deeper into voice with Home Hub tutorials<\/i>. [online] Mobile Marketer. Available at: https:\/\/www.mobilemarketer.com\/news\/sephora-google-dive-deeper-into-voice-with-home-hub-tutorials\/541709\/ [Accessed 13 Nov. 2018].<\/p>\n","protected":false},"excerpt":{"rendered":"

Sephora harnesses user data to offer pinpointed product recommendations, offers, and loyalty incentives<\/p>\n","protected":false},"author":11606,"featured_media":34305,"comment_status":"open","ping_status":"closed","template":"","categories":[4365,186,49,346,2671,344,16,2506,392],"class_list":["post-33332","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-artifical-intelligence","category-beauty","category-e-commerce","category-machine-learning","category-mobile-app","category-product-development","category-retail","category-sephora","category-shopping","hck-taxonomy-organization-sephora","hck-taxonomy-industry-beauty-and-cosmetics","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nBeauty in the Age of Individualism: Sephora\u2019s Data-Driven Approach - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/beauty-in-the-age-of-individualism-sephoras-data-driven-approach\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Beauty in the Age of Individualism: Sephora\u2019s Data-Driven Approach - 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