Powering Digital Banking with Data and Analytics

We’re all becoming accustomed to measuring aspects of our lives with data. Our fitness devices tell us our quality of rest when we wake up. We can make choices about how often we use our phones based on our weekly usage report. Or maybe you ignore all the data thrown at you on a daily basis. You may be able to make that choice in your personal life, but you can’t ignore data in digital banking.

Oracle found that 84% of surveyed executives agree their customers seek an individualized, tailored experience. Creating this kind of experience requires a deep understanding of users that only comes from interpreting user data intelligently. Despite the clear challenges, only 11% of banking leaders see turning data into actionable insights as their biggest challenge.

Banks and credit unions shouldn’t underestimate the lift required to put their data to work for them. But such a challenge isn’t impossible to overcome, even for community and regional institutions without the resources of the Megabanks.

Why Use Data in Digital Banking Anyway?

If you knew what happened in the past, you could accurately approximate what may happen in the future. Imagine what you could deliver to users if you knew their habits. Data can provide that insight. Data enables forecasting and allows you to make educated guesses.

By learning what happened in the past with descriptive data and applying diagnostic analysis to learn the root cause of user behavior, you can provide a personalized user experience. But data insights benefit more than the user. Banks and credit unions can develop targeted marketing campaigns that can save up to 15-20% of a marketing budget.

This is something the Megabanks do well, as they have been building technology for years that draws users away from their community banks and credit unions with personalized experiences. But the technology Megabanks use is only as good as their data. So, the more proficient you are with data, the higher performing you can be. You’ll just need a plan and the right tool to help make sense of the data you already have.

Planning for Data Insights

You may be the first person in your organization to use data, and that’s okay. Analytics don’t need to be attacked at a top down level. People have to see what good data insights can do before full adoption spreads. Your team can set the example.

Once you experience what data can do for you, imagine what a data proficient team can accomplish. A team that embraces data achieves better results. They plan accordingly because they’re starting with a measurable goal. When they’ve achieved their goal, they can prove success by walking back through the numbers.

A word of caution: avoid using data for the sake of data. Your data must inform a decision. Make sure your team presents only meaningful data that supports a proven insight.

One of those insights that will measure your success: user engagement. Since businesses have begun using data to connect to users, user expectations have changed. Now users expect campaigns that speak directly to where they are in their life. Typically, marketing campaigns focus on products that just need to be sold: delivered from the top down, funneling to all users regardless of their personal needs. Now that data plays an integral role in strategy, marketing campaigns can be tailored to user segments’ needs.

But before anything can be done, you need benchmarks to know where you stand before benefiting from data. With benchmarks to start from and measure against, you’ll have the ability to direct your team in key focus areas that will have the greatest impact, especially as they pertain to targeted marketing.

What Data in Action Looks Like

When you dive into your data and discover how much you have, how much of it is unstructured, and struggle to figure out what to do with it, you’ll gain a new understanding of the need for a data analytics engine.

As you search for a data analytics tool, look for qualities that can make sense of your data and deliver what you need in just a few clicks. To achieve this level of usability, your analytics tool will need to do a lot of work for you. That work can look like pre-built user lists you can use to quickly scan your user data for details like accounts, engagement, logins, and new users. Once you’ve chosen a list you’d like to target (you should also be able to manually select who you want to target, like a certain demographic), your analytics tool should be able to deliver content relevant to that list’s needs.

Consider not only how powerful but also how simple an analytics tool can be. With the right data analytics tool, you can systematically gather and sort unique data points to create automated targeted lists and market toward any unique target or segment. An investment in transforming your data into insights and engagement will prove valuable to your growth.