Banking data analytics can improve customer relationships

Gathering valuable customer insights from banking data analytics does not require spending millions on machine learning algorithms and complex predictive models to generate referral offers. In fact, these types of big data initiatives have little to do with a human-centered approach to data analytics.

Instead, human-centered data analytics is about examining customers’ unique data to learn about their behavior, challenges, and needs. With a data lens on their customers, banks can proactively offer solutions to problems and strengthen customer relationships.

For example, if banks have a deposit relationship with customers and access to their transaction histories, they can make an incredible range of observations and highly educated guesses about their customers.

Recurring purchases and expenses, patterns in income and expenses, and periods of high or low activity can help banks learn about their customers – including what kind of work they might do, when they experience important life events such as changing jobs or remodeling a home, and what hobbies and activities they enjoy. By using deeper insights from customer data, banks’ business lines can initiate connections with customers and offer them banking services that might solve specific problems.

Many banks might assume that they need more resources and technical infrastructure before they can launch an initiative to garner deeper customer insights from data. In most cases, that isn’t true.

Human-centered data analytics in banking doesn’t require large-scale, cost-prohibitive data analytics solutions or cutting-edge applications of machine learning. Banks can examine their data, identify stories, and use those stories to foster more meaningful connections with customers.

To get started, a bank should first take stock of existing technology tools and expertise and ask how the organization can use them to learn more about customers. If the bank currently has a customer relationship management platform that only tracks sales, it could use that platform to mine customer data across the entire organization in order to learn more about its customers and search for opportunities.

More than technology, human-centered data analytics requires creativity and freedom to fail. The organization’s data specialists need room to build and test hypotheses, some of which might not pan out. But when the organization stays committed to the process of examining data sets, asking critical questions, and applying an agile, efficient process to test ideas, then brand loyalty and an invaluable competitive advantage can follow.