  {"id":20135,"date":"2016-11-18T17:45:57","date_gmt":"2016-11-18T22:45:57","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/data-driven-dress-dealing\/"},"modified":"2016-11-18T17:45:57","modified_gmt":"2016-11-18T22:45:57","slug":"data-driven-dress-dealing","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/data-driven-dress-dealing\/","title":{"rendered":"Data-Driven Dress Dealing"},"content":{"rendered":"<p><em>Merchant Monarchs<\/em><\/p>\n<p>Since its early inception, the world of retailing has been dominated by a single role: the merchant. This person is not only responsible for selecting which products to manufacture and sell, but s\/he is also usually in charge of determining pricing and promotion cycles. Having an eye toward potential trends and being able to anticipate what the market will want (in addition to how much and when) are crucial skills that most merchants believe are innately imbued: one may able to hone them over time, but taste cannot be taught. In this world, the merchant reigns supreme\u2014so much so, in fact, that the CEO of J. Crew, Mickey Drexler, has long been known as the \u201cMerchant Prince\u201d for his ability to see the future of fashion and sell it to the American people [1].<\/p>\n<p><em>Digital Disruption<\/em><\/p>\n<p>Retailers have always captured some data on product performance, tracking sales by department over time and discounting merchandise based on history and customer psychology [2]. But with the advent of digital information storage and ecommerce, the availability and accuracy of this data has radically shifted. Everything from purchase patterns to product specifications is now trackable and analyzable, allowing companies like EDITED to capitalize on this information. This web-based platform creates tools and dashboards from information that retailers and merchants have long undervalued, utilizing a proprietary machine learning and natural language processing system [3]. By tracking over 330 million SKUs at 90,000+ online retailers and brands with a sophisticated network of web crawlers (akin to Google\u2019s Googlebot system), EDITED gathers the raw data that is used as inputs for its IBM-Watson-like system [3, 4]. This platform then produces dashboards that can be used by retailers to enable decision-making in three main areas: product assortment, pricing\/discounting, launch timeline, and trend analysis.<\/p>\n<p><a href=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/EDITED.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-20098\" src=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/EDITED.png\" alt=\"Screenshot from EDITED's Dashboard Platform\" width=\"800\" height=\"480\" srcset=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/EDITED.png 975w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/EDITED-300x180.png 300w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/EDITED-768x461.png 768w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2016\/11\/EDITED-600x360.png 600w\" sizes=\"auto, (max-width: 800px) 100vw, 800px\" \/><\/a><\/p>\n<p style=\"text-align: center\">Screenshot from EDITED&#8217;s Dashboard Platform<\/p>\n<p><em>Predicting Products<\/em><\/p>\n<p>Through assessment of competitors\u2019 ecommerce sites, merchants can obtain a clearer, real-time picture of what their market looks like\u2014understanding who is carrying what products and when gives retailers ideas about potential under- or over-stocking issues [3]. EDITED\u2019s tool allows for optimization of assortment to prevent either the cost of missed revenue from lack of supply or the cost of forced discounting to sell off excess volume.<\/p>\n<p><em>Profitability Plays<\/em><\/p>\n<p>By scanning websites of both brands and retailers, EDITED can generate a price map that indicates where a merchant\u2019s price is relative to the market. This gives the merchant the power to raise or lower prices and maximize profits in the long run. Not only can it help with initial pricing, but the system can also determine when the best time is to discount, instead of relying on antiquated seasonality-driven methods that often do not consider real-world circumstances or current demand [2]. Given the proliferation (and potentially reputation-damaging nature) of markdowns in the market, ensuring that retailers are not leaving money on the table by discounting too early is a critical measure of success.<\/p>\n<p><em>Trending Topics<\/em><\/p>\n<p>As the world\u2019s consumption of media in all its forms increases in both speed and volume, brands are now generating an overabundance of content to satiate their customers. EDITED\u2019s platform ingests these images and text from sources like Instagram, Facebook, and style blogs that it then feeds into its system [3]. The output is trend tracking and analysis that, when combined with human intelligence, can provide retailers with a better representation of what cuts or colors are <em>en vogue<\/em>.<\/p>\n<p><em>Optimization Opportunities<\/em><\/p>\n<p>There is no doubt that data will continue to play an ever-increasing role in the job of merchants and retailers, supplanting much of the intuition and art-based thinking that has guided these professionals in the past. However, fashion companies today have neither the in-house capacity nor the resources to develop these tools themselves [4]. Analytics firms like EDITED can continue to sell their SaaS platforms to retailers for the time being, but for their model to succeed in the future, they should focus on two points of differentiation.<\/p>\n<ul>\n<li>Determine data ownership: As with other applications of AI, data ownership and privacy concerns are high when utilizing a shared database system. EDITED currently works with publicly accessible data from websites, but to strengthen their product offering, they may want to consider approaching retailers for proprietary access. At that point, integration of said data into the system may present a conflict of interest that EDITED should figure out now.<\/li>\n<\/ul>\n<ul>\n<li>Focus on the future: EDITED\u2019s product is great for determining what to do in the present situation\u2014e.g. what are today\u2019s hot products and how should they be priced on the market today. However, a lot of the value of this data is in forward-looking analyses\u2014how can a fashion company know what future trends will be a hit and which SKUs will sell. This would also allow EDITED to position itself better against its main competitor in the fashion analytics space, WGSN [5].<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><strong>Word Count<\/strong>: 797<\/p>\n<p><strong>Sources<\/strong><\/p>\n<p>[1]\u00a0N. Paumgarten, \u201cThe Merchant,\u201d\u00a0<em>The New Yorker<\/em>, September 20, 2010.\u00a0<a href=\"http:\/\/www.newyorker.com\/magazine\/2010\/09\/20\/the-merchant\">http:\/\/www.newyorker.com\/magazine\/2010\/09\/20\/the-merchant<\/a>, accessed November 2016.<\/p>\n<p>[2] V. A. Kansara, \u201cThe Long View | How Realtime Data is Reshaping the Fashion Business,\u201d August 3, 2011.\u00a0<a href=\"https:\/\/www.businessoffashion.com\/articles\/long-view\/the-long-view-how-realtime-data-is-reshaping-the-fashion-business\">https:\/\/www.businessoffashion.com\/articles\/long-view\/the-long-view-how-realtime-data-is-reshaping-the-fashion-business<\/a>, accessed November 2016.<\/p>\n<p>[3]\u00a0<a href=\"https:\/\/edited.com\/\">https:\/\/edited.com<\/a>, accessed November 2016.<\/p>\n<p>[4] K. Abnett, \u201cIs Fashion Ready for the AI Revolution?,\u201d April 7, 2016.\u00a0<a href=\"https:\/\/www.businessoffashion.com\/articles\/fashion-tech\/is-fashion-ready-for-the-ai-revolution\">https:\/\/www.businessoffashion.com\/articles\/fashion-tech\/is-fashion-ready-for-the-ai-revolution<\/a>, accessed November 2016.<\/p>\n<p>[5] K. Noyes, \u201cWhat\u2019s on trend this season for the fashion industry? Big Data,\u201d\u00a0<em>Fortune<\/em>, September 22, 2014.\u00a0<a href=\"http:\/\/fortune.com\/2014\/09\/22\/fashion-industry-big-data-analytics\">http:\/\/fortune.com\/2014\/09\/22\/fashion-industry-big-data-analytics<\/a>, accessed November 2016.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Retailers have historically made decisions based on intuition, but what happens when companies like EDITED begin leveraging Big Data to track runway trends, analyze product movement, and recommend pricing and promotions?<\/p>\n","protected":false},"author":1841,"featured_media":20316,"comment_status":"open","ping_status":"closed","template":"","categories":[298,15],"class_list":["post-20135","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-big-data","category-fashion"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/digitization-challenge-2016\/","yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - 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