Customer Analytics in Financial Services
Financial services institutions are under continuous pressure to grow their revenue and assets under management. New entrants with disruptive models threaten incumbents in competition for customers. Customer analytics using big data can create differentiation through personalized interactions and services that build trust and long-term value.
Build More Profitable Customer Relationships
Financial service institutions are looking at new channels to acquire customers while building more effective and profitable relationships with existing ones. Big data analytics can be used to build stronger relationships with customers that increase their lifetime value and grow loyalty.
Acquire, Engage and Retain Customers
Big data analytics help financial services firms in all three phases of the customer lifecycle: acquisition, engagement and retention. By building detailed profile and behavior models, personalized offers and strategies can be applied in all phases.
Customer Analytics is Part of a Big Data Journey
Customer analytics is part of a multi-pronged approach to big data analytics by financial institutions. Firms large and small are using big data analytics beyond customer analytics in areas such as risk management, regulatory compliance, fraud detection and security.
Protecting Customer Data
As more information about customers is captured and stored, securing Personally Identifiable Information (PII) is imperative. Does your analytics platform support critical security, privacy and encryption features to keep customer PII locked-down?
How to Engage Financial Services Customers Using Big Data
Increasing competition and new disruptive business models have shifted the battleground in financial services to building and maximizing customer relationships. Watch this on-demand webinar from Cloudera and Datameer to learn how you can use big data to broaden your customer footprint and create more valuable relationships with your customers.