Using Advanced Analytics to Inform and Transform U.S. Retail
Contributed by Sanjay Fuloria, James Pise
By tapping a flood of new data, U.S. retailers can better target shoppers to increase sales and profitability.
Indicators such as personal disposable income, consumption expenditure and consumer confidence all point to a modest recovery for retailers. However, the emergence of “spend shifters” – consumers who are buying less, choosing less expensive brands and saving more – makes it imperative for retailers to better understand and more precisely target their customers.
Tapping business analytics as a service can help retailers hold down costs while transforming data into actionable insights in this competitive market. Using this approach l, retailers pay only for the insights they use while reducing spend on dedicated hardware and software
Analytics Is Key
“Spend shifters” (i.e., consumers who are trimming their spending in the aftermath of the global recession) are typically tech–savvy and crave detailed information about product offerings and services before making purchases. Retailers that wish to serve this consumer segment will have to continuously listen, respond and innovate. With many retailers offering an equivalent range of products, using similar promotional campaigns and suppliers and targeting the same customers, success will go to those who make the best merchandising, marketing and pricing decisions.
Customer, employee and supplier data is plentiful. Still, survey after survey reveals that organizations are not effectively using the data they have. According to the study “CFO Insights: Delivering High Performance,”1 60% of workers feel overwhelmed by the amount of information they receive, and 43% of managers believe that too much information is a hindrance to better decision–making. By using analytics to harness this information, management can drive better and more informed decision–making (see Figure 1).
Role of the Cloud Broker
A cloud broker is an intermediary between the ISV and various cloud providers. It helps the ISV
choose the platforms that best suit its needs, deploy and integrate applications across
multiple clouds, and/or enable the ISV to move between cloud platforms. It can add value through vertical solutions marketed to specific industries, or through horizontal functions required across verticals such as entitlement, subscription management and billing.
Analytics has evolved from simple reporting to predictive modeling and optimization, spreading to several key business areas. These include identifying the most profitable customers, which maximizes a retailer's return on investment by aligning its offerings with the behaviors of the most profitable customer segments. For instance, when analytics told Best Buy that 7% of its customers accounted for 43% of sales, the consumer electronics retailer reorganized its stores to address the needs of these high-value customers.
Understanding customer behavior to classify groups of customers who exhibit similar behaviors can help target them more effectively. Such classification also fosters cross–sell and up–sell opportunities, as well as targeted marketing, with a class of techniques called cluster analysis and decision trees.
Assortment planning and optimization helps retailers identify important style and color combinations, as well as the quantity they should buy and uncover hidden buying trends. Walmart, for example, used exogenous demand models to identify that, along with flashlights and batteries, sales of Pop–Tarts surged when a hurricane was forecast. This type of market basket analysis can also provide insight into which products to display together to increase sales. Analytics can also be used to optimize pricing and to discover up–selling and cross-selling opportunities, which for Staples resulted in a 137% rate of return.
Procurement and spend analytics use data from suppliers to help retailers identify precise product costs. This helps identify savings across geographies, product categories, business units and procurement organizations. Walmart uses an inventory management system that enables its suppliers to see how many of its products are on every shelf of every store at any time, helping the retailer better manage its stocks.
As increasing amounts of data become available and analytics grow more sophisticated, retailers can benefit from a range of new techniques. These include operational analytics that drive automated decisions in near real–time, such as re–ordering to drive better inventory management or instantly offering promotions to customers based on their purchases.
Text analytics can help determine consumer trends and perceptions of their products and services – and more quickly discover problems by examining customer's comments on Web pages, blogs and other social media.
Analytics as a Service
As economic conditions drive retailers to pare back operational costs, trusted third parties can help retailers with complex tasks such as collating and analyzing data from multiple business units without adding headcount or reducing the productivity of existing departments. Analytics service providers can often provide pools of experts with an industry–wide view as a result of serving multiple clients, while avoiding the biases that may limit in–house analysis. Known as business process as a service (BPaaS), this approach can help retailers meet the twin imperatives of cutting costs and making better decisions.
For more information, read the white paper How Advanced Analytics Will Inform and Transform US Retail (PDF), and about Cognizant's services for the retail industry.
1Michael Sutcliff and Michael Donnellan, CFO Insights: Delivering High Performance, Wiley, May 24,