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Well known examples of retailers using machine learning include those in the subscription business, where the entire value proposition typically relies on a machine learning algorithm to consider users\u2019 preferences in customization. However, more traditional retailers are also starting to embrace machine learning in different capacities, including but not limited to product development, inventory management, sales forecasting and supply chain management.<\/p>\n
Walmart is a prime example, where employing machine learning for process improvement is critical to remaining competitive in the fast-changing retail environment. As the second largest online retailer, Walmart is competing head-to-head with online retail giant Amazon. Amazon has been using machine learning for years to provide and refine product recommendations on its website and in Kindle[1]<\/a>. It uses purchase and browse history, as well as customer demographics and socioeconomic characteristics to provide relevant, customized product recommendations on an individual basis. Walmart recognizes that any failure to meet customers\u2019 changing expectations while adjusting for technology investments and developments in its competitors operations could place them at a competitive disadvantage[2]<\/a>.<\/p>\n As such, if the company wants to maintain its position in the eCommerce space, while maintaining a significant brick-and-mortar presence, the most important near-term applications of machine learning will include optimizing the customer experience online and in mobile to increase conversion through personalization, as well as employing the technology to improve processes that directly affect the customer. Laurent Desegur, Vice President of Customer Experience, has echoed this priority, stating that Walmart is using machine learning used to create a model of personalization and recommendation1<\/sup>.<\/p>\n In the short term, in addition to using customer data to build a model of personalization and recommendation for its customers through optimizing product recommendations on site and mobile. It is also using it to remove bottlenecks at check-out, and is the technology behind self-serve kiosk towers, drive-up delivery and mobile check-out1<\/sup>.<\/p>\n In the longer-term, Walmart plans to develop a \u201ccloud factory\u201d to accelerate digital innovation. Specifically, it will leverage its recently announced partnership with Microsoft, aimed at enabling the companies to collaborate on machine learning and other forms of artificial intelligence[3]<\/a>. Many applications of the technology will come from co-innovation around big-data and real-time analytics. For example, Walmart will be deploying Microsoft machine learning to launch a language processing platform to make sense of up to 40 terabytes of unstructured text. This will provide insights and educate business decisions in real-time[4]<\/a>. Additionally, operating on a global scale on Microsoft\u2019s cloud-based platform, Azure, will enable machine learning when routing thousands of trucks in the supply chain[5]<\/a>.<\/p>\n Because the essence of machine learning involves identifying insights from big-data, the technology is only as good as the data a company has, and as the people who interpret it. Therefore, in order to optimize the use of this technology, Walmart should emphasize quality as well as quantity in the collection of data. The company could provide incentive for customers to create and login to an account, where they will store personal information, preferences and other relevant data points. As such, Walmart can more accurately collect data \u2013 including click, browse and purchase data \u2013 on individual customers, all while providing a better customer experience through personalization.<\/p>\n In addition, while we can rely on automation to some degree, human intervention and judgement remains critical. Human judgement comes with its own set of biases that machine learning can overcome to a certain degree, however, the human element is important to ensure that critical thinking, and cross-validation is used when interpreting the data[6]<\/a>. Walmart should invest in hiring highly-skilled data scientists to properly and effectively use the predictive technology.<\/p>\n With so many potential uses of machine learning in the retail space, how should Walmart prioritize applications of the technology going forward? Should it continue to focus on customer-facing process improvement, or shift to internal processes like forecasting and supply chain management? Further, how can it employ machine learning to gain competitive advantage, instead of using it to play catch-up with competitors like Amazon? (767 words)<\/p>\n [1]<\/a> Paredes, Divina. \u201cMachine Learning Is the \u2018New Normal.\u2019\u201d\u00a0CIO (13284045)<\/em>, Apr. 2018, p. 8.\u00a0EBSCOhost<\/em>, ezp-prod1.hul.harvard.edu\/login?url=http:\/\/search.ebscohost.com\/login.aspx?direct=true&db=bth&AN=129130726&site=ehost-live&scope=site.<\/p>\n [2]<\/a> Walmart 10K: http:\/\/d18rn0p25nwr6d.cloudfront.net\/CIK-0000104169\/a25e7acb-aa07-49f3-8c0c-0c69e5a8d372.pdf<\/p>\n [3]<\/a> Yeomans, Mike. \u201cWhat Every Manager Should Know About Machine Learning.\u201d\u00a0性视界 Business Review Digital Articles<\/em>, July 2015, pp. 2\u20136.\u00a0EBSCOhost<\/em>, ezp-prod1.hul.harvard.edu\/login?url=http:\/\/search.ebscohost.com\/login.aspx?direct=true&db=bth&AN=118667151&site=ehost-live&scope=site.<\/p>\n [4]<\/a> Ho, Vanessa. \u201cA New Walmart ‘Cloud Factory’ Will Accelerate Digital Innovation, Boost Business Efficiency.\u201d\u00a0Stories<\/em>, 2 Nov. 2018, news.microsoft.com\/transform\/new-walmart-cloud-factory-innovation-business-efficiency\/.<\/p>\n [5]<\/a> Walmart Media Relations. \u201cWalmart established strategic partnership with Microsoft to further accelerate digital innovation in retail.\u201d July 16, 2018. \u00a0https:\/\/news.microsoft.com\/2018\/07\/16\/walmart-establishes-strategic-partnership-with-microsoft-to-further-accelerate-digital-innovation-in-retail\/<\/a><\/p>\n