Customer analytics for banking

Customer analytics for banking

How well do you know your customers? Ask that question of leaders at most banks, and they’ll likely answer “pretty well, thanks.” But is that enough? Not these days — at least not for banks looking to stay a step ahead of the competition. Customer behavior and preferences are changing at a more rapid clip than at any moment in recent history propelled by changes in technology and the economic environment. Banks that have built their organizations with a product-oriented focus (“if we build it, they will come”) should consider shifting to a more customer-centric footing in order to stay competitive. One way to effectively make that transition is to cultivate the seemingly mythical 360-degree view of the customer — one that accounts for their current value as well as their potential lifetime value.

The truth is, banks often have deep insights into their customers — in fact, compared with many other industries they are generally ahead of the curve. The issue lies in the fact that these insights are often not shared across the organization. While regulatory requirements can constrain the widespread sharing of customer data, there is still much that can be done to pull customer information up and out of silos to begin creating a more broad, centralized, enterprise-level view of customers.

Finding and sharing the required data at the relevant levels is only the start. The question then becomes: How can you use the data to discover customer insights? The systems and processes in place throughout most banks are generating more data than ever before, and it’s only growing. There’s a very good chance that after pulling together these different strands of data, the organization won’t know how to effectively use it. At least not by using the same old data and information management approaches.

After years of investing heavily in business intelligence, it’s fair for many banks to wonder if there’s really a difference between real business analytics and the capabilities they’ve developed as part of their business intelligence (BI) investments. The good news is that BI still has an important foundational role to play in analytics — in simple terms, it’s great for slicing and dicing data to understand what happened in the past and monitor key performance indicators. Core platform implementations, back-office consolidations, transaction pricing assessments — these are prime examples of BI applications. The limitation is they stop short of anticipating what may be just around the corner.