The U.S. retail industry has experienced a great deal of difficulty in recent years, caused in large part by a boom in e-commerce. According to a report from global marketing research firm Coresight Research, the number of store locations announcing closures in the first six months of 2019 increased 20% since the same period in 2018, with as many as 12,000 expected to close in total. At the same time, some companies, such as Walmart, have begun utilizing new tools in order to succeed despite this overall dismal outlook for the industry. Walmart’s use of big data in particular has allowed it to rewrite the global retail industry, and its huge data ecosystem has allowed Walmart to remain a retail giant.
Retail enterprises have begun to fully realize the strategic significance of big data analysis for business development and have adopted it as an important tool to help drive future growth. Retail enterprises can be expected to use big data in three major ways. First, retailers can improve operational efficiency in supply chains and use the results of big data analysis to help them make leadership decisions. Retailers can form independent big data products that can be provided to internal and external customers for free or for sale. Some retailers transitioning into e-commerce enterprises will use big data to build an enterprise ecological platform. At present, retail enterprises mainly use big data analysis for precision marketing and customer insight, which alone is an invaluable tool to allow retailers to take full advantage of big data.
There are three types of data analytics: descriptive analytics, predictive analytics, and prescriptive analytics. Descriptive analytics and predictive analytics are about “what” and “who.” Descriptive Analysis uses historical sales data, expected growth rate, or marketing expenditure to provide insight into the past. Retailers gather timely data on their past sales numbers, store closings, and customer information in order to understand what happened during the previous business period. Predictive analytics dives deeper into the data than does descriptive analytics, helping companies to understand the factors that determine a particular outcome and allowing businesses to make educated decisions about who their future customers will be and what products those customers will want to buy. Companies can effectively adjust these factors to influence sales outcomes.
Descriptive and predictive analytics help companies create better-targeted marketing, but they do not solve many of the problems facing retailers. Questions remain about what actions companies can take to survive in a market where retail is a struggling industry. Business decision-makers today should not be satisfied with merely answering questions about past performance. Rather, they should be probing deeper into the reasons for poor performance as well as how to improve it.
Prescriptive analysis focuses on the answers to these questions. It uncovers why customers behave in the way they do. For example, a retailer may discover that it is unpopular with young buyers. To figure out how to increase popularity among young generations, it must determine what is causing young people to dislike its stores. Is it the products it carries? Is it the location of the stores? Or is it due to the changing shopping behaviors of young customers?
In the past, business decisions have been based on intuition. Today, businesses are able to make decisions based on big data analytics. Access to data does not guarantee success. Rather, the key is an effective use of the data, which requires businesses to turn data into information and adjust business models accordingly.