Financial services organizations, especially those in the consumer space, such as retail banks, credit card issuers, private wealth management firms, and insurance companies, need intelligence on their customers, markets, products, and more to run successful marketing campaigns, cross-sell and upsell, and effectively support customers throughout the relationship. Some use cases—for example, making product recommendations and predicting and responding to customer needs when resolving a satisfaction issue—require real-time intelligence. Others require gathering the right data for model learning to generate insights that can be used to improve marketing, sales, operations, and, crucially, the customer experience.
In all these use cases, customer knowledge is vital. The concept of a 360-degree view of the customer has developed significantly, evolving from a basic understanding of the many interactions a customer has with an organization to a deep, detailed understanding of each customer as an individual with unique behaviors, wants, and needs that extend beyond their financial services interactions. Today’s customers expect every interaction to be easy, convenient, and intuitive—whether they’re ordering takeout, filing an insurance claim, or opening a checking account—and they want a consistent, cohesive, seamless experience whether they’re interacting online, using an app, or in person.
Highly satisfied customers are two and a half times more likely to open new accounts or adopt new products with their existing bank than those who are merely satisfied. While banks continue to invest to meet their customers’ rising expectations, they’ve struggled to keep pace with other retail sectors, held back by legacy IT infrastructure and data siloes—and the data quality and lineage challenges they cause. Even for institutions that boast a better-than-average customer experience, typically only one-half to two-thirds of customers rate their experience as excellent.
To meet their customers’ expectations, financial services organizations must continue to address the challenges caused by siloed data in legacy infrastructure while simultaneously employing machine learning, artificial intelligence, and 360-degree customer data to shift from reactive to predictive engagement. For organizations that accomplish this, the rewards can be significant; according to a McKinsey analysis, US retail banks that ranked in the top quartile for customer experience have had meaningfully higher deposit growth over the past three years compared with their peers, thanks to their ability to attract new customers and strengthen relationships with their existing ones.
The following architecture demonstrates how Oracle Data Platform is built to help financial services organizations apply advanced analytics, machine learning, and artificial intelligence to all the available data to provide the necessary insights to create highly relevant, in-the-moment, personalized customer experiences. This enables them to focus on proactive engagement, helping them flawlessly execute every touchpoint across the entire customer lifecycle, from shopping and account opening to onboarding, relationship expansion, and service delivery or insurance claims processing.
There are three main ways to inject data into an architecture to enable financial services organisations to create a 360-degree view of their customers.
Data persistence and processing is built on three (optionally four) components.
The ability to analyze, learn, and predict is built on two technologies.
By leveraging all the available data across each customer’s lifecycle—including structured, semistructured, and unstructured data—and applying advanced big data analytics, machine learning, and AI to a complete record of previous customer interactions, financial services organizations can
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