Wayfair has targeted the furniture mass market customer \u2013 traditionally underserved by brick and mortar companies \u2013 through its vast product offerings and personalized styles determined by its search algorithm. Wayfair\u2019s search algorithm uses natural-language processing to both identify the intentionality behind a customer\u2019s search and highlight top-rated products determined by its customer reviews [3]. Offering highly personalized matches and visual displays are essential to overcome the role in-person inspections and tactile feel have in the customer decision-making process.<\/span><\/p>\n Process flow diagram of Wayfair’s machine-learning algorithm<\/span><\/p>\n Despite the disruption in the space, there continues to be a sizable opportunity in the online home furniture market as online furniture sales (12%) lagged behind apparel (28%) and consumer electronics (34%) in 2017 [1]. Wayfair\u2019s competitive advantage, strategy, and innovation will have to further rely on its AI capabilities to convince digital users \u2013 and the increasing Millennial customer base \u2013 to purchase furniture prior to seeing the product.<\/span><\/p>\n How is Wayfair developing its AI capabilities?<\/b><\/p>\n Search has been at the core of Wayfair\u2019s business model since its inception in 2002. Wayfair has historically used its search algorithm to extract customer\u2019s style preferences from their search history. The challenge Wayfair has faced in search is that roughly 70% of user sessions only look at the first page of search results, indicating that customers are using the platform to look at specific products [3]. Wayfair has found alternative ways to increase customer search and customer engagement to better its predictive modeling.<\/span><\/p>\n In recent years, Wayfair has added to its search capabilities through visual search and augmented reality. In 2017, Wayfair introduced \u201cSearch with Photo,\u201d a new feature that leverages artificial intelligence to make it easier for shoppers to discover the furniture desired for their homes [4]<\/span>. Shoppers can take a photo of a furniture piece, and match it to a similar item in the Wayfair inventory of 8 million products, allowing them to replicate almost any style of home decor. Through visual search, Wayfair is able to quickly and conveniently match images with products while reducing the shopper\u2019s lag and search time\u00a0[5]<\/span>.<\/span><\/p>\n In 2017, Wayfair enhanced their AI capabilities through its \u201cView in Room 3D\u201d feature, an augmented reality (AR) tool that allows customers to visualize furniture pieces in their home\u00a0[6]<\/span>. This new AR capability helps address one of the biggest pain points consumers face which is physically needing to see the product before purchasing. The AR tool allows users to emulate the in-store shopping experience from the comfort of their home, further refining the search process and improving the likelihood of purchase [7]<\/span>.<\/span><\/p>\n Staying ahead of their competitors.<\/b><\/p>\n The home furnishing market is seeing increased competition as brick and mortar companies accelerate their digital presence and Amazon further invests in this market segment\u00a0[8]<\/span>. The challenge and opportunity for Wayfair is to offer a differentiated experience to create brand loyalty and avoid platform switching amongst its users.<\/span><\/p>\n The next step Wayfair can offer in its \u201cSearch with Photo\u201d feature is to provide a self-service design center. Many of the current offerings are tailored to specific product recommendations or virtual models of homes that may not resemble a customer\u2019s home. The revised \u201cSearch with Photo\u201d feature would allow customers to take a picture of their own current living space and be given a selection of themed product recommendations to decorate their virtual living space. The next evolution of machine-learning relies on the predictive power of its models, which in Wayfair\u2019s use case can be categorized as a themed lifestyle recommendation\u00a0[9]<\/span>. Giving product recommendations for an entire living space, under its current conditions, would give Wayfair additional functionality that its competitors do not have and can differentiate itself enough to incentivize customers to remain brand loyal in their shopping behavior\u00a0[10]<\/span>.<\/span><\/p>\n Challenges to address <\/b><\/p>\n Given the customer\u2019s desire to see home furniture in person prior to purchase, how will Wayfair manage its digital-only strategy? While it was announced several months ago that it will open a permanent store, will the omnichannel strategy change the digital customer experience [11]<\/span>?<\/span><\/p>\n What other markets can and cannot Wayfair enter (e.g. corporate and office furniture) with its machine-learning and visual search model?<\/span><\/p>\n (Word Count: 798)<\/span><\/p>\n <\/p>\n Sources:<\/b><\/p>\n [1]<\/span> Wayfair, \u201cWayfair Investors Presentation Q3 2018-vF\u201d. November 1, 2018. <\/span>https:\/\/s2.q4cdn.com\/848638248\/files\/doc_financials\/2018\/q3\/Wayfair_Investors_Presentation_Q3_2018-vF.pdf<\/span><\/a><\/p>\n [2]<\/span> Sam Ransbotham, David Kiron, \u201cAnalytics as a Source of Business Innovation,\u201d <\/span>MIT Sloan Management Review<\/span><\/i>, February 2017. <\/span>http:\/\/ilp.mit.edu\/media\/news_articles\/smr\/2017\/58380.pdf<\/span><\/a><\/p>\n [3]<\/span> Suyash Sonawane, \u201cHow We Use Machine Learning and Natural Language Processing to Empower Search\u201d. October 15th, 2018. <\/span>https:\/\/tech.wayfair.com\/2018\/10\/how-we-use-machine-learning-and-natural-language-processing-to-empower-search\/<\/span><\/a><\/p>\n [4]<\/span> Business Wire, \u201cWayfair Launches Visual Search, Lets Shoppers Instantly Find and Shop the Styles They See and Love\u201d. May 16, 2017. <\/span>https:\/\/www.businesswire.com\/news\/home\/20170516005302\/en\/Wayfair-Launches-Visual-Search-Lets-Shoppers-Instantly<\/span><\/a><\/p>\n [5]<\/span> H. James Wilson, Sharad Sachdev, and Altar Allen, \u201cHow Companies Are Using Machine Learning to Get Faster and More Efficient\u201d. May 03, 2016. <\/span>https:\/\/hbr.org\/2016\/05\/how-companies-are-using-machine-learning-to-get-faster-and-more-efficient<\/span><\/a><\/p>\n [6]<\/span> Sarah Perez, \u201cWayfair Takes on Pinterest with Its Own Visual Search Engine for Home Furnishings\u201d. May 16, 2017. <\/span>https:\/\/techcrunch.com\/2017\/05\/16\/wayfair-takes-on-pinterest-with-its-own-visual-search-engine-for-home-furnishings\/<\/span><\/a><\/p>\n [7<\/span>] Sarah Perez, \u201cWayfair\u2019s Android App Now Lets You Shop for Furniture Using Augmented Reality\u201d. March 20, 2018. <\/span>https:\/\/techcrunch.com\/2018\/03\/20\/wayfairs-android-app-now-lets-you-shop-for-furniture-using-augmented-reality\/<\/span><\/a><\/p>\n [8]<\/span> Pamela N. Danziger, \u201cFurniture Retailers Are Wary Of Wayfair, But Both Need To Watch Out For Amazon\u201d. May 19, 2018. <\/span>https:\/\/www.forbes.com\/sites\/pamdanziger\/2018\/05\/19\/who-can-disrupt-wayfair-in-furniture-retail-only-amazon-but-it-may-need-stores-to-do-it\/#69ba3688ee0f<\/span><\/a><\/p>\n [9]<\/span> Ajay Agrawal, \u201cThe economics of artificial intelligence\u201d. April, 2018. <\/span>https:\/\/www.mckinsey.com\/business-functions\/mckinsey-analytics\/our-insights\/the-economics-of-artificial-intelligence<\/span><\/a><\/p>\n [10]<\/span> Suman Bhattacharyya, \u201cHow Wayfair is Personalizing How You Buy Your Furniture Online\u201d. August 2, 2018. <\/span>https:\/\/digiday.com\/retail\/wayfair-personalizing-buy-furniture-online\/<\/span><\/a><\/p>\n![\"\"](\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/ea107ddf-picture2-1.png\")
<\/a><\/p>\n<\/a>Example of “Search with Photo”<\/span><\/p>\n<\/a>Example of “View in Room 3D”<\/span><\/p>\n