Using Big Data to Attract and Retain the Digital Banking Customer

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Financial services customers are using digital communication channels more and more. Today, they transact across the web, mobile, social, and other self-serve channels. As they interact with financial services companies through these applications, consumers are sharing more information and generating big data about their needs, risk tolerance, and personal profile than ever before.

Meanwhile, customer expectations also continue to rise, shaped by experiences outside banking. Companies such as Amazon and Google have raised the bar for customer experience. And, consumers are better informedas they use internal and external channels to research products and services. They look beyond traditional financial services companies to fulfill their needs, engaging players such as Google Wallet, PayPal, Mint.com and even Costco and Wal-Mart.

Thanks to the volumes of transactional and behavioral data that customers are sharing through their multichannel interactions, there are tremendous opportunities for banks to identify new ways to drive internal efficiencies and to optimize the customer experience. Still, the torrent of both structured and unstructured data available to financial services companies poses heady challenges for bankers to gather the data, analyze it, and act on it. This includes the organizational data silos that exist between product groups (e.g. checking, mortgage loans, IRAs, etc.) and restrict bank executives from gaining a complete view of each customer and their current and long-term value.

We've identified several effective techniques that can be used to address these challenges and capitalize on the opportunities to strengthen existing customer relationships and attract high-value prospects. For starters, the use and sharing of big data across business units and functions needs to be championed by senior management, preferably the CEO. For big data strategies to gain traction and succeed, senior management needs to clearly and regularly communicate the business rationale for using big data to help foster an analytical culture.

As part of these efforts, a high-ranking big data champion who has the CEO’s backing should also work with key stakeholders to break down the product and channel-centric silos that exist. Sharing data across these channels will enable the bank to gain a more complete view of each customer, thus enabling product leaders to use blended customer data and analytics to identify the most relevant up-sell and cross-sell opportunities with micro segments. When big data is collated across channels and product areas, it can also provide decision makers with deeper insight into the next-best action to take with a particular customer in real-time (e.g. signaling a propensity to churn).

As these organizational silos are brought together, it’s also imperative to pay careful attention to data and systems integration efforts. Because of the expanding digital landscape and the various touch points where customers are sharing information, banks need to draw upon streams of structured and unstructured data sources, including information that’s being shared via mobile and social channels. We recommend starting with small-scale pilot projects, conducting post-mortems for lessons learned, and then building upon these efforts.

Once banks begin collecting and analyzing big data streams, this can help bankers identify opportunities to improve customer-facing processes and applications. This is a frequently overlooked area for applying customer intelligence and for improving the customer experience that bank leaders should pay heed to—as the quality of customer experience can have a significant bearing on loyalty and profitability.

Big data can provide banks with a wide range of business benefits. For instance, gaining a deeper understanding of mobile customers’ needs will provide bankers with opportunities to deliver the types of experiences mobile customers are looking for, thus attracting new waves of digital consumers. To that end, bankers can also use customer history, needs, and behavior data to identify which communication channels are the most effective for interacting with certain sets of customers. Additionally, big data can guide bankers in knowing how to deliver on customer expectations and how to optimize the customer experience in specific communication channels based on how different customer groups use those channels. Moreover, banks can synthesize what they know about customers in order to deliver the right offers to the right customer sets at the right time through the right channel.

Demonstrating to customers that the bank knows who they are and that the bank has some understanding of what their needs and interests might be can help companies strengthen customer loyalty and increase customer lifetime value.