On Target: Leveraging the Retail Website through Data Science In the mid-1990s, Target was a discount superstore behemoth. As a nationally branded general merchandise retailer, it sells products through digital channels since 1999. The retailer had set itself apart from chief rival Walmart with a focus on more upscale but wallet-friendly fashion and lifestyle lines, spurring double-digit growth by double-digits each year for more than a decade, from 1996 to 2008. That fruitful streak came to an abrupt halt with the United States financial crash in the fall of 2008. Target was hit hardmuch harder, in fact, than Walmart. Five years later (2013) the company was still struggling. Today, the company hosts over 30 million shoppers at its 1,800+ stores per week. With more than 1,800 stores and a relatively new e-commerce site, Target was collecting reams of data about its online customersproducts purchased, browsing habits, items abandoned in shopping cartsyet it wasnt fully leveraging all that information. The company began to see this huge pile of e- commerce data as the needle-in-a-haystack key to driving higher sales, says Harvard Business School Professor Srikant M. Datar. Target had to make this big shift from thinking only about retailing to also thinking about data. And to do that, data had to become the big asset they needed to develop to provide new opportunities, Datar says. Winners in the retail market are putting data to work. In the case of a national chain the size of Target, that means keeping tabs on an inventory of around 1 million products, then using data to ensure their availability. Even today, not all retailers have embraced data fully to the point where they think of themselves as data companies, and it might be why many companies are suffering. Several traditional retailers are indeed tripping over the digital divide. JC Penney lost its market value significantly, with shares trading at $1.50 mid 2018. And in October 2018, Sears filed for bankruptcy protection, following the dark road paved by Borders, RadioShack, and Toys R Us. However by 2018, Target managed a startling recovery from its five- year slump . In third quarter of 2019, total revenue grew 4.7% during the quarter to $18.67 billion from $17.82 billion a year earlier, beating expectations for $18.49 billion. Sales at Target stores open for at least 12 months and online were up 4.5%, better than expected growth of 3.6%. The company said digital sales surged 31% during the quarter, with its same-day delivery options including buy online, pick up in store and curbside pickup accounting for 80% of digital sales growth. Fascinated with Targets stunning turnaround, Datar studied the calculated steps it took to fuel its success. Hire data experts In 2013, the one bright spot in Targets otherwise bleak financial picture was its then-small e-commerce arm. Although overall sales declined, online business soared nearly 30 percent between 2012 and 2013. Target was awash in customer data from these online sales, but to make sense of it, the company needed to bring in the right people. Paritosh Desai joined Target in August 2013 as vice president of business intelligence, analytics, and testing, and he then went on a hiring spree, growing the analytics team with data scientists and others trained in computer science, math, statistics, and physics, including many who held doctorates. To attract the best people, Target knew it had to keep at least part of its data operation in Silicon Valley, even though the companys corporate offices were in Minneapolis. It was a big decision to stay in Silicon Valley, Datar says. The demand for data-science professionals is through the roof, so you have to go where the experts are. Desai credits the success of data science at Target to this team. Target leverages data science to help improve on-shelf availability, reduce inventory levels and create operating efficiencies, according to Paritosh Desai, SVP and chief data and analytics officer, speaking at The AI Summit in New York 2019. "We're dealing with problems that are extremely large," said Desai. "And we're not just solving for today but for next week and next month." To solve its complex data science problem in the context of retail, leadership blends traditional data science methods with modern techniques like neural networks to improve performance of demand- forecasting models. Experiment and execute quickly Desai created an entrepreneurial culture, knowing experimentation would be critical to discovering how data could be woven into the companys business practices. His colleagues followed this mantra: develop, test, measure. Yet they couldnt just continue with experiment after experiment without applying what they learnedand quickly. If you just keep experimenting, people [in the company] will say, when do the sales come in? You dont have that much time to keep trying, Datar says. The only solution is to learn fast, take action, and continue to build on your learning. Deliver a mobile response in milliseconds Desai knew from previous experience leading data science at Gap Inc. that consumers get frustrated with slow mobile apps. To him, the most important engineering requirement was providing users with a response to their search in milliseconds consistently. Just as important, the response had to be relevant to that customer. If a consumer searched for sneakers, the site should not only provide a list of sneaker-like shoes, but at the top of the list should be the particular brand the user purchased in the past. Measure success in narrow terms A successful customer interaction on Target.com was narrowly defined: Only when a customer searched for a product, received recommendations, and actually purchased the product would the company bother to drill deeper to test which banner designs worked best to push a sale through. And if a customer didnt buy after browsing, the team asked questions: What information was the customer missing? Was there something in the customers purchase history that Target should have known that would have blocked the sale? Only searches that led to sales of recommended products in the same session were considered statistically significant and quantifiable, Datar says. By setting the bar that high, you remain humble about what you can learn from both successful and unsuccessful transactions. Managers should ask data-related questions Desai wanted the team to help managers in the field make smart business decisions based on data. Those managers were encouraged to develop questions that could produce value if analysts could massage the data to provide accurate answers. For example, a manager might ask: Did a dish detergent promotion boost sales, or would customers have bought the detergent anyway? The folks who had the data did not go to the managers and say, I have all this data. Let me tell you how to run your business now. They said, Look, let me understand the key questions you would like to have answered with data, Datar says. As the team worked with analysts and managers, the questions became so sophisticated that an engineer might be required to develop a tool to answer them. The engineer had to work quickly. Retail is always changing so fast that you cant wait three weeks to get an answer, Datar says. The engineer might have to build the tool and provide the answer in an hour. Allow managers to analyze data The alliance between data experts and managers was a good start. But Desai knew that if managers had to wait in a queue for an expert every time they had a question, they might grow weary of asking. The solution: allow managers themselves to work with the data. That meant creating a flexible analytics system that could not only adapt to real-time business changes but one that managers felt comfortable using. He didnt want to be the bottleneck. It took vision and humility to say, The answer doesnt have to come through me and my data science team, Datar says. It was a bold decision because it was much costlier and more complicated to design flexible architecture that managers could easily interact with. Take a calculated approach to using data In the early days of e-commerce many retailers made the mistake of treating their online unit as a mere add-on to the store. Target, by contrast, spent much time focusing on how data could be used specifically to help build its web arm. And the retailer was careful to establish the value of data, analytics, and algorithms for the e- commerce site first before scaling up its capabilities to make decisions and solve problems in other areas of the business, such as marketing, store sales, and the supply chain. In its approach to modernization, Target has learned to lean on the physicality of retail to achieve better results. But when the time was right, this data-analytic approach would help dictate a variety of decisions for Target, including which products earned precious shelf space inside its brick and mortar stores. "A lot of people have predicted that stores will not have a place in the future with online shopping and e-commerce taking over," Desai said. "What we say is that stores are our most important asset." With 70% to 80% of Americans living within 10 miles of a Target store, the chain opted to rely on the stores as hubs to help fulfill digital demand. Services such as buy online, pick-up in store (or BOPIS, per industry speak), and Drive Up add complexity and size to the math the data science team is solving for. "When you have so many ways to fulfill demand, it's important to have the product in the right place at right time," Desai said. In 2017, Target began testing algorithms to increase fulfillment velocity in its supply chain. With initial improvements proving valuable, the company is planning further efforts around distribution center automation and design, said John Mulligan, Target EVP and COO, during its Q1 2018 earnings call. Across our supply chain, we're testing and rolling out new processes designed to make us faster, more nimble, more accurate and reliable. Last year, we told you about our new facility in Perth Amboy, New Jersey, which provides a clean slate where you could develop and refine a completely new way to replenish stores, and the results have been impressive. Out-of-stocks on items in the store served by the Perth Amboy facility have been running about 40% lower than our previous benchmark. These results were accomplished by applying a new inventory positioning logic, developed by our data and analytics team that allows us to send the right quantity in the right unit measure much faster than our other facilities. While last year was about developing and testing these algorithms, this year is focused on beginning to scale up the physical movement of inventory. And by early next year, another 50 Target stores will be served by this new model Target realized the importance of devoting the time, attention, skill, and strategy to developing data and analytics competencies in a critical part of the businessits e-commerce sitebefore rolling out these capabilities more broadly, Datar says. I think thats a big reason why Targets adoption of a data-driven approach has been so successful. CASE REVIEW QUESTIONS 1. When the growth of Target sales began suffering and how long did the slowdown of last? Which year was the turning point and what was one of the reasons for the recovery of the company? 2. Based on the study of Harvard Business School Professor Srikant M. Datar, please list and elaborate on the strategic moves Target has put into action for its turnaround.