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What’s in Store for the Future?


By Romina Florencia Arrieta


A TRANSFORMATIVE PERIOD


The retail industry is entering its most transformative and challenging period in recent memory. According to CNBC, over 2,880 U.S. stores have closed in 2017 thus far, compared to only 1,153 this time last year. The drastic number of store closures, in the first quarter of 2017 alone, has already topped the historically high figure for 2008. Credit Suisse estimates the number will reach over 8,000 store closures by year-end, impacting both the retail industry itself and the job market. However, Bloomberg analysts note that the economic landscape has been healthier than average, marked by low unemployment and low oil prices. In spite of this, the threats of bankruptcy and downsizing in the retail industry will remain consistent in the foreseeable future. Every segment of the retail industry is in danger: footwear and electronics retailers, as well as over forty other segments have encountered difficulties and are headed for a massive reorganization and it is still unclear what is causing these foreclosures.


The Emergence of E-Commerce


The retail industry has been facing unforeseen new pressures in recent years, particularly with regards to the emergence of e-commerce, which has directly caused the foreclosure of many stores. E-commerce has reduced the barriers to entry in the retail industry and has rendered the need to occupy a physical space a thing of the past. In April 2017, Forbes analysts found that the number of digital sales have been increasing by double digits yearly; in fact, the U.S. Census Bureau estimated the total e-commerce sales for 2016 to be $395 billion, representing a 15.1% increase from 2015. According to Forbes, the number of sales per square foot, the main productivity metric used by retailers, has been dropping due to the significant portion of consumers shopping online, which has caused stores to see their productivity dwindle. At the bottom of the barrel in terms of productivity metrics is K-Mart, whose sales per square foot hover at $97, ten times less than its rival, Costco.


Costco and K-Mart are similar in many instances, so what makes Costco more successful than K-Mart? Since both companies are wholesalers that have existed for decades, their business models may seem quite similar at first glance, yet the main difference between these two firms is not their market segment, but rather their strategic vision. K-Mart has been a victim of poor management which has lead to convoluted strategies and dwindled competitiveness. Analysts at Forbes concluded that companies like K-Mart are stuck in the past, trying to implement strategies that have been historically successful, when they should be looking forward and acknowledging the unique difficulties brought forth by e-commerce. By dismissing new technologies before fully understanding their potential and only focusing on incrementally increasing the benefits of their current product offering, K-Mart has been forced down a path of unsteady financials.


By incrementally increasing the benefits of their current product offering and dismissing new technologies before fully understanding their potential, K-Mart has been quickly forced down a path of unsteady financials. These shortsighted decisions led to a net loss of $748 million in 2016, and reduced the retailer’s credit line from $963 million to $174 million, in the span of one year. Meanwhile, Costco has regularly reinvented itself to stay competitive. Due to its management’s capacity to embrace and implement change, as reported by Fortune magazine, Costco adopted a new technology in the early 2010s which some say could save the retail industry: Big Data Analysis.


Big Data In Retail


As the number of online purchases grows exponentially, the amount of data collected by companies is increasing at a similar pace. For instance, Walmart collects 2.5 petabytes of unstructured data every hour, according to estimates by online publication Dezyre. A Global Customer Insights report estimates that companies are spending a whopping $36 billion on storage and infrastructure for those large data sets, and this amount is expected to double within the next three years. Harvard Business Review notes that this expense could be justified under the right management and analyst team, as Big Data has the capacity of increasing a store’s return on investment and its sales per square foot.


One of the main benefits of Big Data is that it enables retailers to predict demand more accurately than traditional methods. Research by Deloitte Insights showed that accurate demand forecasting can eliminate inventory shortages and lower the cost of holding assets in storage for extended periods of time. Keeping the right level of stock increases a firm’s revenue in addition to strengthening customer loyalty towards the brand. Most companies use time-series econometrics with historical data sets, which is a backward-looking method used to predict future demand. Methods such as this one work with a company’s cash cow product but fail to predict the demand of less popular products. These models can also prove problematic when there is a drastic change in the economic landscape, which affects the demand for a firm’s product.


The Benefits of Big Data


Big Data is able to predict demand in a forward-looking fashion by using aggregated anonymous web searches linked to every store’s location instead of historical data stored by the firm. This method is based on the assumption that a customer will search for a product online before making a purchase. Big Data makes companies more competitive with one another, since consumers are becoming more informed and are likely to look for the same product in different locations to obtain the best deal possible. Companies can also adjust their pricing policies based on demand forecasts for individual products; this will increase the asset turnover ratio on products that are less sought after by shoppers.


Big Data also allows companies to recommend products based on a customer’s previous purchases. By using data mining techniques, patterns in sales emerge, enabling retailers to make recommendations that are assuredly linked to a customer's interests or needs. This method has increased the conversion rate of clients for firms like Walmart and Amazon.


Moreover, these patterns are not obvious and could only be discovered through extensive analysis. For instance, analysts at Walmart found that the number of sales of strawberry Pop-Tarts increased by seven times before a hurricane, and customers who purchase diapers were also prone to buy cases of beer. Target used this analytical power to predict which of its female customers were pregnant by simply finding correlations in their purchasing patterns. This is particularly valuable for retailers since pregnant women are one of the most valuable customers, as they are not only shopping for themselves but also for their child. Target used this knowledge to send coupons and offer other deals on diapers and baby carriers to the women it deemed to be pregnant, resulting in increased revenues and customer loyalty.  The ability to find patterns in purchases enables firms to increase the share of wallet per customer and enhances customer loyalty. Purchasing patterns can also be used to determine strategic placement for related products, as for example, placing diapers near the alcohol.


As the popularity of social networks continues to grow, advertising on these platforms is very tempting for retailers. However, the fees to promote a product to a large number of potential customers can be higher than the revenues associated with the specific advertisement. In addition to finding patterns in customer’s purchases, Big Data can be used to recommend products to someone based on their Facebook liked pages and Pinterest pins. For instance, Walmart’s research and development team developed the Social Genome: a Big Data analytics solution. This analytical tool combines data from social media and customer purchases which then enables the company to reach out to their clients through social media.


What's in Store for the Future?


As the retail industry enters uncertain times, firms that use Big Data can increase their productivity and remain competitive in a new era where barriers to entry have been erased, and the number of competitors has reached pure competition levels. By accurately forecasting demand, finding patterns in customers’ purchases, and recommending products via social media, companies can overcome the new pressures inflicted upon them by the increasing popularity of e-commerce.

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