One industry that is deriving great value from big data is retail sales. Online retailers evaluate shopper visits to their sites—what pages they look at, where they linger, how long they stay, and where they go next. These retailers depend on integrating unstructured social media data with structured transaction data to obtain a retail 360° customer view that is useful in personalizing their customers online experience. A large global retail company was interested in benefiting in such a view of their customers. However, its existing data infrastructure made this goal so difficult that it led them to seek consulting expertise to provide a solution that would provide the needed analysis of structured and unstructured data.

Centizen proposed a solution with AWS as the primary platform along with Hadoop, Cloudera and related toolsets that replaced a legacy Teradata warehouse. This solution would allow the integration and analysis of structured and unstructured data to deliver the 360° views of customers the client required to evolve the user experience of each customer.

The existing IT infrastructures and data warehouse made it a struggle to capture and analyze the unstructured clickstream data together with the structured data at the necessary speed and scale to be able to implement a real personalized experience online for each of the retailer’s customers. Additionally, the retailer needed a better way to respond in terms of the volume of and speed of incoming updated data.

The retail client now has a single source of customer data for all groups to understand 360 degree views of its customers. The company can track data to examine how many times a customer visits the web site based on an IP address or site log-in to determine which platform, mobile versus desktop, produces the biggest sales. This helps them to more effectively determine where to spend advertising dollars.The customer satisfaction team studies data on how long it takes a shopper to successfully make a web purchase to ensure overall satisfaction. If trends are noticed, such as shopping cart abandonment, the data is further examined to determine if the reasons are because of website or server issues so they can be immediately rectified.