Banking Analytics are Evolving

Goals matter

Banking Analytics are evolving and our regional banking customers agree wholeheartedly on one big thing: using information effectively has to start with your organization’s goals. No surprise here.

What is surprising is that banking executives find it necessary to make this point aloud and repeatedly. Could it be possible that they and their organizations find themselves distracted or unintentionally diverted from what they set out to do? Colleen Medeiros, CIO of Bank Newport, explains that the bank has deliberately revamped its program management procedures to combat this tendency. Each initiative now has to articulate explicitly which of the bank’s strategic goals it is designed to advance. No clear goal; no project.

Four strategic postures

Banks’ goals vary. Our banking customers have described themselves in four entirely different strategic postures, each with its own particular objectives.  We’ve depicted these as a stage model in the schematic below. And each of these postures has its own priorities for analytics.  (See the schematic below.)


Banking Analytics Chart

Portfolio of Services

Regional banks in the “Portfolio of Services” category are working on pulling together a comprehensive suite of services—from credit cards and online banking to commercial loans and trust services. We observe some aggressive acquirers getting stuck in this posture because so much of their IT resource is absorbed in simply integrating systems.


Banks have long realized that the customers using multiple services tend to be stickier and more profitable. To increase their share of the customer’s banking “wallet,” these banks implement house-holding and customer tracking, often using CRM systems. They also collect data from across the assorted product systems to get a comprehensive view of the customer’s relationship with the bank. By sharing this among all their banking lines, they optimize their opportunities to cross-sell.

Customer Intimate

Banks with a customer intimate posture have shifted their view to the future and toward helping customers through major financial events instead of selling to them. These organizations use a raft of detailed information about the customer—from credit card purchases to social media—to predict upcoming customer needs. On the retail banking side, these financial events could be weddings, home purchases, or retirement plans. Commercially, these events could be business model changes, acquisitions, or new product introductions. By getting in front of these needs, the bank can help the customer focus on, say, having the wedding rather than paying for the wedding. In that posture, the bank has already won the customer’s business.


Embedded banking buries the financial aspect of a customer’s activity deeply inside the activity itself. Jeff Dennes, a noted digital banking innovator, says, “Customers don’t want to buy car loans; they want to buy cars. To make that as convenient as possible, the bank has to help the customer ‘build’ the car they want. The financing just comes with it.” This posture repeats the success of examples such as Dun and Bradstreet’s embedding its credit checking service inside corporate procurement systems and IT vendors’ providing customer-specific intranet catalogues for employees to make pre-approved, no-bid hardware purchases.

We’ve arrayed these four banking postures as a stage model because they argue strongly for explicit progress in access to and use of analytics. A regional bank’s analytics evolution might go like this:

  1. Choose a core processor[1] and a set of third party systems to support the bank’s portfolio of services. Use the core processor’s reporting and analytics as well as industry-expert third party reporting services.
  2. Combine core banking data with line of business and CRM data to get a more complete picture of the customer’s total relationship with the bank. Wayne Dunn, Senior Vice President and CTO of HarborOne Bank, says simply, “The whole trick is to take an enterprise view. The systems come in silos, and we have to take a holistic approach and tie the data together.”
  3. Establish strategic access to the bank’s third party provider data—core systems, credit cards, trust, mortgages, etc.–and develop a data warehouse, again incorporating customer data as well as other sources such as social media. Develop unique analytics tuned specifically to the bank’s strategy. For example, mine payment data to identify customer financial events.
  4. Establish partnerships with gateway service providers or proprietary service offerings that make the financial transaction a bundled, inseparable feature of the customer’s purchase. Negotiate deep information access with partners—including clickstream,[2] customer service, and marketing data as well as comparative data across financial partners– to retain a comprehensive, predictive view of customer behavior and bank performance.

[1] A core processor is a system (and its associated vendor) that processes and clears checks and runs all of the standard banking and accounting functions, such as the deposit, loan and liabilities systems for time deposits. This includes platforms for typical cash management services, such as ACH processing and wire transfer.

[2] A clickstream is the series of mouse clicks that take an online user from web page to web page. By analyzing this stream, one can learn, for example, which websites one’s customers come from and go to and where they spend their time on one’s own site.

Things to Consider

One very viable analytics strategy is simply to hang on for the ride while focusing your own resources in other strategic areas such as acquisitions or partnerships.

However, if your own strategy isn’t right down the center of the core processor’s path, you should plan on getting near-native access to the data. You’ll need that to tune your analytics to your own needs as your bank separates itself from the pack.

If your approach involves self-service analytics for your bankers, make sure your data is clean, complete, organized and well-explained before turning users loose on it. Plan also to have access to information design expertise for key visualizations. Bank IT leaders agree wholeheartedly that their executives want data now, but don’t fully understand the legwork required to get the data into a usable, digestible format.

As the analytics targets evolve, the organization must build its analytics “literacy” in parallel.  Jeff Murphy, SVP and Director of Enterprise Strategy for Renasant Bank says, “Traditional bankers may not fully grasp that today’s software can give us a timeline forecasting that this person will be buying a car in three years. The learning curve is steep for bankers in the industry today, but predictive banking analytics will make us much more profitable as we build our analytics muscles.”

For more information, please contact Dr. Jane Linder at NWN Corporation: or 781-472-3498.

Download the PDF