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By Dave Willoughby | Last updated October 11, 2018
AnalyticsAPI ManagementMicroservicesPlatform as a ServiceSystemsCloudRetail
Today’s retailers need to gather insight from their data to provide a unique shopping experience for their customers. This pattern shows developers how to create retail applications that leverage data from enterprise IT infrastructure using APIs in a hybrid cloud environment without requiring specific knowledge of the underlying infrastructure.
The retail industry is fundamentally changing, and retailers need to provide unique in-store experiences to survive. According to one Accenture Interactive study, “56% of consumers are more likely to shop at a retailer in store or online that recognize them by name.” Stores providing unique and personalized in-store experiences will continue to thrive, but they need to take advantage of their existing enterprise IT infrastructure and new systems of insight. By being agile and flexible in how they gain insight, retailers will be better able to adapt to changing environments and grow their business.
Today, most retailers host their core business support systems and applications on mainframes such as IBM Z, including 18 of the top 25 retailers. To stay ahead of their competitors, they need to digitally transform their business operations to increase revenues and improve the buying experience. API-centric commerce platforms and microservices, enterprise IT infrastructure, and personalization engines are now necessary components of digital business, delivering growth and innovation in a digital commerce platform.
So what’s your role in retail’s digital transformation? In this developer pattern, you’ll access retail data located in data centers hosted by core business support systems on IBM Z, which also hosts sales data, inventory, and all historical data. Supporting applications are hosted on IBM CICS® and IBM z/OS® thru APIs.
You’ll use an app to access data on IBM Z through APIs, and you’ll analyze buying patterns and generate recommendations to customers on their mobile phones. These in-store recommendations increase revenues, and personalized touch points greatly improve the customer experience. The pattern will show you how to access enterprise data using IBM API Connect to track personalized buying patterns. You’ll then be ready to use IBM Watson Analytics™ and machine learning algorithms to write new applications to increase in-store revenues.
Ready to put this code pattern to use? Complete details on how to get started running and using this application are in the README.
August 28, 2018
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