How to build your organization’s data analytic proficiency; Part Two
In 2019, information as an asset is still in the earliest stages of adoption. However, a financial institution’s ability to compete in the growing data economy will be a primary competitive differentiator in the years to come. In part two of our series, we investigate the methods banks and credit unions can implement now to gain valuable insight using existing customer relationship data and how to position in order to take full advantage of the data-driven digital economy.
Financial institutions maintain an abundance of data about every customer and every transaction with millions of data points collected each day. Each one of these banks and credit unions have a rich, and most likely, untapped competitive advantage in their existing customer base. The opportunity to retain and expand relationships that already exist is a source too often left untouched due to the antiquated and siloed systems that result in fragmented customer data.
Customer behavioral insight is fundamental to understanding the flow of money, currency transaction, and suspicious activity reporting; data analytics tools enable bankers to dig deeper into these behaviors to uncover each customer’s needs and how to strategize and customize the focus of sales, marketing and customer retention efforts. With behavioral analysis information available immediately, executives can make timely decisions, sales and marketing can be up-to-date and relevant, and branch staff can deliver an improved customer experience by personalizing conversations, messaging and cross-selling efforts towards products and services customers actually need.
Ultimately, having this information at your fingertips will reinforce face-to-face sales efforts and personalize marketing efforts – especially for those customers who seldom visit a branch office. But without a strategy your bank’s results may not yield optimal results. Below are some of the first things a financial institution can do to tap into its wealth of customer data and build a strong and competitive analytics strategy.
1. Recognize that your bank has a competitive advantage where your existing customer base is concerned.But without a data analytics system in place, your bank will not be able to make the most out of this distinct competitive edge. The best data analytics tools provide multiple visuals with a significant amount of potentially actionable data instantaneously. Self-service data removes reporting roadblocks bankers typically experience when attempting to get the most current information. With a data analytics tool, bankers are no longer hindered by lost time waiting for a report to be written and generated.
2. Evaluate existing systems and report writing tools. The data you need lives in your core and ancillary systems. The sheer volume of data contained in a bank’s core system is overwhelming and the amount of time it takes to mine the data accurately into a multitude of reports can be very labor intensive. At this stage, determine whether your bank has a viable means for extracting the data and transforming it into meaningful and actionable information.
3. Review your bank’s strategic objectives and align the focus of data mining to support these goals. For example, if core deposit growth is a strategic objective, ensure you have the capability to mine data that identifies loan customers who do not have deposit accounts. Similarly, if checking account retention is a goal, you will want to easily identify and track accounts that do not have a direct deposit or are not using ACH, debit card, mobile banking or bill pay services.
Check out the third and final installment of our three-part series that illustrates the remaining steps a financial institution can take to fully participate in and benefit from the digitization of the economy.
Jerry Bradley, CPA is the chief operating officer of Roanoke, Va. based KlariVis, a unique and proprietary data analytics solution developed by bankers for bankers. KlariVis allows financial institutions to quickly aggregate and visualize their previously siloed and disparate data in one place with unparalleled ease for data-driven decision making.