Designing a big data customer analytics platform for customer centricity requires more than acquiring data sets, dumping that data into a data lake, and setting those predictive analytics tools loose. In practice, those within the organization must be able to transform the existing operational environment into one that is receptive to (and facilitates) analytics as part of the customer lifecycle processes, and must guide the business analysts to determine where the business opportunities exist and how they can be addressed through this transformation.

This tutorial introduces the concept of the customer lifecycle, and explores how the stages of the customer lifecycle rely on high-quality synchronized customer data. We will examine how the organic evolution of the application landscape has created roadblocks to successfully implementing a customer-centric analytics environment, both from a data architecture and a process perspective. The tutorial then looks at envisioning a customer data architecture designed to align with the different stages of the customer lifecycle and ensure that the shared customer view is synchronized to prevent conflicting views across business functions and processes. We will discuss ways to transition from the existing architecture to the envisioned approach. Finally, we examine how analytics methods can be applied at different customer touch points to improve overall outcomes.

Attendees will learn about:

• The stages of the customer lifecycle.
• How siloed customer data sets create confusion and inconsistency.
• Data architecture for customer information.
• Aligning the customer data domain with lifecycle touch points.
• Identifying opportunities for improvement in customer centricity.
• Using analytics to improve outcomes.