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—so much so, in fact, that the CEO of J. Crew, Mickey Drexler, has long been known as the “Merchant Prince” for his ability to see the future of fashion and sell it to the American people .
Retailers have always captured some data on product performance, tracking sales by department over time and discounting merchandise based on history and customer psychology . 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 . By tracking over 330 million SKUs at 90,000+ online retailers and brands with a sophisticated network of web crawlers (akin to Google’s 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.
Screenshot from EDITED’s Dashboard Platform
Through assessment of competitors’ ecommerce sites, merchants can obtain a clearer, real-time picture of what their market looks like—understanding who is carrying what products and when gives retailers ideas about potential under- or over-stocking issues . EDITED’s 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.
By scanning websites of both brands and retailers, EDITED can generate a price map that indicates where a merchant’s 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 . 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.
As the world’s 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’s platform ingests these images and text from sources like Instagram, Facebook, and style blogs that it then feeds into its system . 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 en vogue.
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 . 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.
- 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.
- Focus on the future: EDITED’s product is great for determining what to do in the present situation—e.g. what are today’s 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—how 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 .
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 N. Paumgarten, “The Merchant,” The New Yorker, September 20, 2010. http://www.newyorker.com/magazine/2010/09/20/the-merchant, accessed November 2016.
 V. A. Kansara, “The Long View | How Realtime Data is Reshaping the Fashion Business,” August 3, 2011. https://www.businessoffashion.com/articles/long-view/the-long-view-how-realtime-data-is-reshaping-the-fashion-business, accessed November 2016.
 https://edited.com, accessed November 2016.
 K. Abnett, “Is Fashion Ready for the AI Revolution?,” April 7, 2016. https://www.businessoffashion.com/articles/fashion-tech/is-fashion-ready-for-the-ai-revolution, accessed November 2016.
 K. Noyes, “What’s on trend this season for the fashion industry? Big Data,” Fortune, September 22, 2014. http://fortune.com/2014/09/22/fashion-industry-big-data-analytics, accessed November 2016.