CUSTOMER EXPERIENCE ENHANCEMENT IN BANKS USING

 

Data science is transforming the face of all industrial domain. Unlike other banking sectors has already stepped in towards the expandable possibilities of data science. Banking Customers now have access to accounts and can transact across mobile, social, and other self-serve channels. The branch’s role is changing to focus on more complex issues while consumers use Facebook, mobile apps, and virtual wallets to conduct financial business in a new ecosystem.

Today’s consumers share more information about their needs, risk tolerance, and personal profile than ever before. Their expectations are higher, shaped by experiences outside banking. They are better informed as they use internal and external channels to research products and services. They look beyond banks to fulfil financial needs, engaging players such as Google Wallet, PayPal, Mint.com, and even Costco and Wal-Mart. And consumers connect to brands and one another through social and mobile channels, communicating their experiences broadly. They’re willing to take advantage of low-cost channels if they find them valuable and relevant to their daily lives. Many actually prefer them.

Banks are beginning to explore the opportunity to differentiate with insight. For example, the business press reports that one of Capital One’s top priorities is to be a data-driven organization and use insight to differentiate in customer service and product development, though specifics are hard to come by. And it is not alone. Investment firm State Street Corp. is using semantic data models on the client-side to optimize investment strategies, while also improving regulatory reporting and risk calculation internally. Even a smaller player like Midwest regional bank Great Western Bank is leveraging predictive analytics for its marketing activities. And here comes the significance of data science in banking operations.

The industry is still in its nascent stage, however. According to a recent study by Celent, only 24 per cent of banks surveyed had implemented a Big Data solution, most commonly around risk and fraud monitoring or product and service marketing. But of those who have had a Big Data initiative in place for more than a year, 70 per cent had met or exceeded business expectations. And to highlight data’s potential, 90 per cent of those surveyed said they think that successful Big Data initiatives will define the financial services winners in the future.

outlined five steps to guide craft smart data strategies:

1. Elevate the importance of business-savvy data scientists. While there’s always a high demand for quantitative professionals to be part of the team, progressive banks are looking to complement that talent with creative business professionals who see business opportunities in trends that produce bottom-line results.

2. Organize for one version of the truth. Too many banks are still organized in the product- or channel-centric silos. The bank can’t knit together a comprehensive picture of the customer. Silos need to be redefined along with analytics practices. Online and offline data must be integrated into platforms across channels that facilitate a comprehensive understanding to enable predictive analytics and inform real-time interaction strategies.

3. Don’t underestimate the integration challenge. Today banks need to extract insights from structured and unstructured data, statistical data, social media streams, click stream data, smartphone data, videos, etc. Small-scale experiments using Big Data are recommended to start, with slow rollout from there.

4. Integrate intelligence into customer-facing business practices. The biggest opportunity for Big Data is the potential to identify and integrate insights into customer-facing applications in real-time. Analytics has come a long way, but many banks neglect to push the intelligence to front-line applications.

5. Use Big Data to accelerate the customer-centric transition. Customer centricity is no longer a nice to have a strategy for banks, it’s the only differentiator. And data is the backbone. It’s critical to think beyond technology and analytics to what organization, process, and people-related changes are necessary to really put the data and insights to work.

A successful Big Data strategy must be coordinated across the enterprise. It’s not the domain of one department or business unit. The chart above illustrates a hypothetical use of Big Data to harness and harvest customer data for an improved customer experience and greater efficiency.

Big Data shows so much promise for banks willing to consider it strategically. Those that do will be able to understand customers more intimately and act in real-time to meet their stated and perceived needs. On the efficiency side, they will harmonize channels by defining multichannel journeys that make sense to the user and eliminate redundancies. And overall, banks that create Big Data dominance will influence customer behaviour across channels to make interactions more effective and efficient, gaining loyalty and financial strength in the process.

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