{"id":28670,"date":"2018-11-12T15:51:09","date_gmt":"2018-11-12T20:51:09","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/extreme-makeover-home-edition-wayfair-optimizes-your-home-decor-shopping-experience-through-machine-learning\/"},"modified":"2018-11-12T17:11:07","modified_gmt":"2018-11-12T22:11:07","slug":"extreme-makeover-home-edition-wayfair-optimizes-your-home-decor-shopping-experience-through-machine-learning","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/extreme-makeover-home-edition-wayfair-optimizes-your-home-decor-shopping-experience-through-machine-learning\/","title":{"rendered":"Extreme Makeover Home Edition: Wayfair Optimizes Your Home D\u00e9cor Shopping Experience Through Machine Learning"},"content":{"rendered":"<p>Though many shoppers view Wayfair as \u201cthat online furniture store,\u201d Wayfair identifies as a technology company that \u201creinvents the way people shop for their homes.\u201d<a href=\"#_ftn1\" name=\"_ftnref1\">[1]<\/a> In an information-abundant age, customers are king, and with a few keystrokes gain access to the world\u2019s markets. Sellers, then, face fierce competition for clicks, carts, and conversions. At Wayfair, machine learning is central to improving the customer experience and driving sales. Specifically, Wayfair employs machine learning at the top of the sales funnel to optimize product listings, better matching results to customers\u2019 intent and historical preferences, as well as eliminating duplicative listings.<\/p>\n<p>Since customers rarely browse beyond the first page of search results, Wayfair employs machine learning to surface products customers are most likely to select. \u201cIf customers can\u2019t find something easily, they are probably not going to buy it,\u201d says software engineer Suyash Sonawane.\u201d<a href=\"#_ftn2\" name=\"_ftnref2\">[2]<\/a> The first tool, Wayfair\u2019s <em>Query Intent Engine<\/em>, breaks down words in a customer\u2019s query to determine their true meaning (for example, whether \u201cglass\u201d refers to a material or a product type), then maps the meaning against known attributes, and lastly directs the 性视界 Engine to show products whose attributes match the query. In addition to product data provided by suppliers, the algorithm uses historical click data to learn what products have met the needs of customers with similar queries.<a href=\"#_ftn3\" name=\"_ftnref3\">[3]<\/a><\/p>\n<p>A second machine learning algorithm, the <em>Product Reranker<\/em>, builds on the <em>Query Intent Engine<\/em>. The reranker optimizes results for a specific outcome like \u201cAdd to Cart\u201d; it takes items deemed \u201crelevant\u201d by models like the intent engine and ranks them according to their likelihood of delivering the desired metric. <a href=\"#_ftn4\" name=\"_ftnref4\">[4]<\/a> Wayfair also deploys the reranker outside of search, including on customized landing pages and remarketing newsletters. As both algorithms receive new data every day, they build on themselves and each other. Looking ahead, Wayfair aims to make better use of product reviews, which often reveal attributes not already provided by the supplier, as inputs to these models.<a href=\"#_ftn5\" name=\"_ftnref5\">[5]<\/a><\/p>\n<p><a href=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Wayfair_Intent-Engine.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-28661 size-large\" src=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Wayfair_Intent-Engine-1024x466.png\" alt=\"\" width=\"640\" height=\"291\" srcset=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Wayfair_Intent-Engine.png 1024w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Wayfair_Intent-Engine-300x137.png 300w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Wayfair_Intent-Engine-768x350.png 768w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Wayfair_Intent-Engine-600x273.png 600w\" sizes=\"auto, (max-width: 640px) 100vw, 640px\" \/><\/a><\/p>\n<p style=\"text-align: center\"><em>This diagram outlines how various data inputs inform what product listings are returned to a user.\u00a0\u00a0<\/em>Source: Suyash Sonawane, &#8220;How We Use Machine Learning and Natural Language Processing to Empower 性视界.&#8221;<\/p>\n<p>Wayfair also leverages machine learning to improve the search experience by eliminating duplicative listings. Since Wayfair does not own inventory and instead acts as a platform connecting suppliers with customers, it does not control product listings at their inception. Unique suppliers often list identical products at different prices or under different names, creating confusion for customers.<a href=\"#_ftn6\" name=\"_ftnref6\">[6]<\/a> In response, Wayfair uses a <em>Duplicates Review Tool<\/em> to consolidate listings. This algorithm compares product attributes (like weight, manufacturer, and number of parts) to calculate a \u201csimilarity metric\u201d for a pair of items. Pairs with a high-enough score are then reviewed by a human and consolidated if the algorithm correctly identified a duplicate.<a href=\"#_ftn7\" name=\"_ftnref7\">[7]<\/a> For greater impact, Wayfair hopes to increase the number of attributes fed into the model and deploy it earlier in a product listing\u2019s lifecycle so that duplicates never hit a consumer-facing catalog.<a href=\"#_ftn8\" name=\"_ftnref8\">[8]<\/a><\/p>\n<p>As a data-rich e-commerce destination, Wayfair already leverages machine learning to reduce friction in customers\u2019 shopping experiences. But to further address the issues discussed, Wayfair will need to increase the speed at which it integrates data. With its customer and supplier counts growing daily, Wayfair must deploy algorithms more quickly to prevent data processing from becoming a bottleneck (at the current pace, it takes some algorithms three weeks to cycle through the catalog).<a href=\"#_ftn9\" name=\"_ftnref9\">[9]<\/a> In addition, the firm should be wary of asking too many teams to pilot machine learning solutions simultaneously. A common problem among growing technology companies, this can lead to overlapping work and algorithms interfering with one another on site.<\/p>\n<p>A question readers can consider when evaluating machine learning at Wayfair is whether these algorithms should be fiercely protected or, conversely, commercialized. Advertisers already partner with Wayfair to leverage their data for targeted advertising, but would the value of Wayfair\u2019s machine learning tools ever be such that Wayfair stands more to gain from commercializing the predictions than from than protecting them as a competitive advantage? This shift would make clear that Wayfair realizes its identity as a technology company, rather than a digital furniture store.<\/p>\n<p>(784\/800)<\/p>\n<p>&nbsp;<\/p>\n<p><strong>References<\/strong><\/p>\n<p><a href=\"#_ftnref1\" name=\"_ftn1\">[1]<\/a> &#8220;About Wayfair.&#8221; Wayfair. Accessed November 12, 2018. https:\/\/www.wayfair.com\/about\/.<\/p>\n<p><a href=\"#_ftnref2\" name=\"_ftn2\">[2]<\/a> Sonawane, Suyash. &#8220;How We Use Machine Learning and Natural Language Processing to Empower 性视界.&#8221; Wayfair Tech Blog. October 15, 2018. Accessed November 11, 2018. https:\/\/tech.wayfair.com\/2018\/10\/how-we-use-machine-learning-and-natural-language-processing-to-empower-search\/.<\/p>\n<p><a href=\"#_ftnref3\" name=\"_ftn3\">[3]<\/a> Sonawane, \u201cHow we Use Machine Learning and Natural Language Processing to Empower 性视界.\u201d<\/p>\n<p><a href=\"#_ftnref4\" name=\"_ftn4\">[4]<\/a> Kirkwood, Rachel. &#8220;Information Retrieval and Machine Learning at Wayfair.&#8221; Wayfair Tech Blog. July 6, 2018. Accessed November 11, 2018. https:\/\/tech.wayfair.com\/2018\/07\/information-retrieval-and-machine-learning-at-wayfair\/.<\/p>\n<p><a href=\"#_ftnref5\" name=\"_ftn5\">[5]<\/a> Sonawane, \u201cHow we Use Machine Learning and Natural Language Processing to Empower 性视界.\u201d<\/p>\n<p><a href=\"#_ftnref6\" name=\"_ftn6\">[6]<\/a> Maunu, Ryan. &#8220;The Duplicate Review Tool: Incorporating Visual 性视界 into Merchandising Operations \u2013 Wayfair Tech Blog.&#8221; Wayfair Tech Blog. May 25, 2018. Accessed November 11, 2018. https:\/\/tech.wayfair.com\/2018\/05\/the-duplicate-review-tool-incorporating-visual-search-into-merchandising-operations\/.<\/p>\n<p><a href=\"#_ftnref7\" name=\"_ftn7\">[7]<\/a> Manu, \u201cThe Duplicate Review Tool.\u201d<\/p>\n<p><a href=\"#_ftnref8\" name=\"_ftn8\">[8]<\/a> Manu, \u201cThe Duplicate Review Tool.\u201d<\/p>\n<p><a href=\"#_ftnref9\" name=\"_ftn9\">[9]<\/a> Manu, \u201cThe Duplicate Review Tool.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Though many shoppers view Wayfair as \u201cthat online furniture store,\u201d Wayfair identifies as a technology company that \u201creinvents the way people shop for their homes.\u201d[1] In an information-abundant age, customers are king, and with a few keystrokes gain access to [&hellip;]<\/p>\n","protected":false},"author":11636,"featured_media":28679,"comment_status":"open","ping_status":"closed","template":"","categories":[1036,49,346],"class_list":["post-28670","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-digital-retail","category-e-commerce","category-machine-learning","hck-taxonomy-organization-wayfair","hck-taxonomy-industry-technology","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - 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