{"id":34407,"date":"2018-11-13T19:58:56","date_gmt":"2018-11-14T00:58:56","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/machine-learning-in-retail-an-editd-approach\/"},"modified":"2018-11-15T18:40:20","modified_gmt":"2018-11-15T23:40:20","slug":"machine-learning-in-retail-an-edited-approach","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/machine-learning-in-retail-an-edited-approach\/","title":{"rendered":"MACHINE LEARNING IN RETAIL: AN EDITED APPROACH"},"content":{"rendered":"

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

Over the last several decades, apparel retailers have struggled with the complexities of increasingly varied product assortments and changes in long established seasonal shopping patterns. As retailers implement new strategies to stay ahead of the demand for an omnichannel shopping experience with emphasis on digital interactions, \u201cthey introduce new labor-intensive tasks to sort through all the data they\u2019re collecting\u201d[1].<\/p>\n

EDITED, the data analytics company named to Fast Company\u2019s 2014 Most Innovative Companies List [2], tackles this problem through machine learning.<\/p>\n

EDITED\u2019s platform crawls brand and retail websites across the globe, monitors consumer opinions on social media, and analyzes output from key industry events, blending machine-learning with human editing. In seconds, it takes vast amounts of real time, measurable data \u2013 a task that would be completely unsustainable for a team of human analysts \u2013 and turns it into the kind of actionable information that can give brands and retailers a competitive edge when making inventory, pricing, and merchandise management decisions [3].<\/p>\n

Since launching in 2009, EDITED\u2019s system has \u201clearned\u201d to recognize apparel products in images and natural language processing software, producing a searchable database of organized, structured information on upwards of 60 million products collected from brands in 30 countries and over 35 languages [4]. Everything from SKU and color options to price and stock levels (as well as many other details) contributes to the formation of one complete data point captured by EDITED\u2019s systems. Processed at a rate of 7 million per day [5], these data points result in a complete profile that allows users to track the evolution of individual items over time.<\/p>\n

So what does a brand do with all of this market data and competitor analysis? In the short term, EDITED\u2019s focus is to work with new and existing clients, \u201cstart[ing] by analysing their competitors\u2019 historical pricing and assortment data to make more strategic decisions, ultimately leading to better sales, stronger inventory management and less discounting\u201d [6].\u00a0<\/strong>Their goal is to build a real time view of the global market leading to more scientific commercial decision-making in the fashion industry.<\/p>\n

But the potential for machine learning\u2019s impact in this space extends beyond pricing and promotion. Co-founders Julia Fowler and Geoff Watts acknowledge that their technology has much wider application. In the next decade, EDITED\u2019s machine learning and data analytics will not only enable brands to be first to market with accurately predicted trends, but it will also play a major role in reducing waste industry-wide. The company\u2019s goal is to leverage machine learning to \u201ctransform the retail industry by empowering it with the tools to become better, faster, and more efficient\u201d [7].<\/p>\n

Long term, EDITED is working to partner with additional brands and retailers to leverage data that will deliver to customers the right products at the right prices at the right time \u2013 thereby eliminating over-discounting, promoting customer loyalty, and spurring greater returns and growth.<\/p>\n

EDITED has significant potential to increase cost savings,\u00a0enhance decision-making\u00a0and\u00a0encourage process automation. I would also encourage the company to consider how it can go beyond analysis of the current market and leverage its data collection techniques to better understand not only the competitive set, but also the consumer.<\/p>\n

In today\u2019s digital economy, customers constantly express their opinions and indicate their preferences online. A tweet. An Instagram like. Clicking add to cart. Each of these presents a host of data points and an opportunity for a brand or retailer to get to know their customer a little better. There has never been more accurate, factual information available with which to measure an industry that is notoriously deemed \u201cfickle.\u201d If EDITED can utilize its platform to \u201clearn\u201d about customer-brand interaction in addition to market analytics, the result will be a more dynamic retail landscape that both predicts and addresses consumer needs.<\/p>\n

I would also recommend that EDITED consider the significance of its impact on defining the future merchant role within a retail organization. As EDITED continues to focus on machine learning to produce real-time data, it will not only reduce inventory waste, but will also create greater process efficiencies, the results of which will make for leaner, more streamlined merchant organizations that can do more with less [8]. Over the next several years, the company should work with retailers to leverage the evolution of the merchant role to inform it\u2019s future data collection. Together, EDITED and its partners can best identify what processes might become fully automated and proactively use machine learning to highlight areas where merchants need to focus their time.<\/p>\n

But these recommendations demand we reconcile a number of questions surrounding the impact of data-driven intelligence on the fashion industry in the years to come. To what degree should data influence and drive design, buying and merchandising decisions? Should data-driven intelligence ever completely replace human intuition in the fashion retail space? Or is there an optimal mix?<\/p>\n

(800 words)<\/p>\n

 <\/p>\n

Sources<\/p>\n

[1] Wilson, J., S. Sachdev, and A. Alter.”How Companies Are Using Machine Learning to Get Faster and More Efficient.” 性视界 Business Review Digital Articles<\/em>.\u00a0 May 3, 2016. Accessed November 2018.<\/p>\n

[2]\u00a0“The 2014 Top 10 Most Innovative Companies by Sector: Style.” Fast Company. January 01, 2000. Accessed November 2018. https:\/\/www.fastcompany.com\/most-innovative-companies\/2014\/sectors\/style.<\/p>\n

[3]\u00a0Kansara, Vikram Alexei. “How Realtime Data Is Reshaping the Fashion Business.” The Business of Fashion. August 2011. Accessed November 2018. https:\/\/www.businessoffashion.com\/articles\/long-view\/the-long-view-how-realtime-data-is-reshaping-the-fashion-business.<\/p>\n

[4]\u00a0Kansara, Vikram Alexei. “How Realtime Data Is Reshaping the Fashion Business.” The Business of Fashion. August 2011. Accessed November 2018. https:\/\/www.businessoffashion.com\/articles\/long-view\/the-long-view-how-realtime-data-is-reshaping-the-fashion-business.<\/p>\n

[5]\u00a0https:\/\/edited.com\/data\/. Accessed November 2018.<\/p>\n

[6]\u00a0Abnett, Kate. “Is Fashion Ready for the AI Revolution?” The Business of Fashion. April 07, 2016. Accessed November 2018. https:\/\/www.businessoffashion.com\/articles\/fashion-tech\/is-fashion-ready-for-the-ai-revolution.<\/p>\n

[7] “Data Enablers: Apparel Retailers Dress for Success With EDITED Analytics.” PMNTS.com. May 18, 2017. Accessed November 2018.<\/p>\n

[8] Begley, Steven, Rich Fox, Gautam Lunawat, and Ian MacKenzie. “How Analytics and Digital Will Drive Next-generation Retail Merchandising.” McKinsey & Company. August 2018. Accessed November 2018. https:\/\/www.mckinsey.com\/industries\/retail\/our-insights\/how-analytics-and-digital-will-drive-next-generation-retail-merchandising.<\/p>\n

 <\/p>\n

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