Banking data analytics can improve customer relationships

Mohammad Nasar
Banking data analytics can improve customer relationships

Banking technology and data should foster human connections, not eliminate them.

Until the beginning of the 21st century, most banks relied on one-to-one personal connections to build and strengthen customer relationships. But over the past 20 years, consumer behavior has reduced face-to-face banking interactions and pushed the financial services industry largely into the digital realm.

So, what has replaced bank branch visits and in-person conversations for relationship-building opportunities with customers? For much of the banking industry, the answer is nothing – yet.

As switching banks becomes increasingly faster and easier, banks need to ask themselves how they can limit customer attrition and build lasting relationships through digital interactions and limited employee-to-customer touch points.

For many banks and other financial services companies, the answer might lie in a new, more human-centered approach to data analytics in banking.

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To build better relationships, banks must draw on data to present customers with answers instead of offers.

To build better relationships, banks must draw on data to present customers with answers instead of offers.

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.

These four scenarios demonstrate how banks might use human-centered data analytics to address customer needs.

The following four scenarios demonstrate how banks might use human-centered data analytics to address customer needs.
  1. Transaction activity shows that bank customers in one neighborhood tend to frequent a local business, which also happens to work with the bank. What if that bank could contact the local business to share collected intelligence about customers in the area and offer an opportunity for the business to buy into the bank’s rewards program? The bank could reinforce the existing relationship between the community and business by offering additional rewards and incentives for bank customers who purchase from that business.
  2. Banking data reveals that a customer spends more on a household service (like waste management or lawn care) than his or her neighbors. Could the bank promote one of its small-business customers that offers better rates and even work with that company to offer a discount to new customers?
  3. A customer’s credit card activity shows spending almost every weekend at home improvement stores. Could the bank offer the customer a short-term home improvement loan that delivers a better interest rate than the credit card?
  4. A bank sees an uptick in a business’s automated clearing house payments and intuits that the company might have hired new employees. What if a banking agent or personalized marketing message could highlight insurance products that might help the business retain those employees?

There is a common element among these examples: Rather than building strategy around the best incentive or the best rate for itself, the bank is focused on identifying the customer’s challenges and using insights from banking data analytics to help solve problems.

Limited resources shouldn't stand between your bank and practical, actionable insights from data.

Limited resources should never stand between your bank and practical, actionable insights from data.

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.

Ready to dig deeper into human-centered data analytics?

Download this free guide that provides additional examples and steps to overcome common challenges. Created by Crowe banking data management specialists, the guide includes additional examples of human-centered data analytics in action, questions banks can ask to find opportunities in customer data, and a five-step methodology to address common data analytics challenges.

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Crowe specialists work at the forefront of data management and data analytics in banking. Let’s talk about your needs and figure out how we can apply your data to solve pressing business problems and strengthen customer relationships.
Mohammad Nasar
Mohammad Nasar
Principal, Financial Services Consulting