Analytics: The engine driving personalization
As banks look to offer a customized digital experience, they can start by making better use of the customer data they already have in hand.
The best banking is personal banking—or at least that’s what financial institutions want their customers to think. In this digital world, consumers demand a seamless, omnichannel experience that is (or at least seems to be) tailor-made just for them.
The financial services industry looks at data as being the key piece of the puzzle needed to customize services and develop long-term relationships that keep customers around when they could easily jump to a competitor.
So where can brands start? “Any digital experience can, and should, be driven by analytics,” says Joshua Rix, a director with Woodhurst Consulting, a digital consultancy focused on the financial services industry.
“Understanding your data is key,” wrote business intelligence provider Tableau in a white paper. “When you’re able to harness data and segment customers in new and exciting ways, target the right markets and offer relevant products at the right time, you can drive new opportunities, generate new revenue streams and increase the value of every customer.”
Tableau offers this four-step sequence as a model:
- Lead with data: Use a data-first mindset to gather customer information from every touchpoint, including websites, mobile apps, branches, call centers and social media, to help improve the customer experience.
- Understand the customer’s desires: Banks should use financial and non-financial data to build relevant experiences for customers. When that data is leveraged for personalization, banks can reinforce their role as trusted advisors.
- Tap data to identify opportunities: With advanced tools and technologies, banks can build valuable, near real-time analyses across a variety of data types to create sales opportunities and to design desirable digital experiences for customers.
- Utilize segmentation: Deeper insights can help financial services providers develop valuable customer segments. This can help build more accurate lending models, create more relevant products, identify irregular activity, and anticipate customer behavior.
Using data is easier said than done. “In many cases, the value is locked into proprietary systems,” says Clayton LiaBraaten, chief revenue officer at pureIntegration, a consultancy that offers data aggregation solutions. “Data discovery exercises meant to consolidate information into data warehouses often lack governance. Data lakes become data swamps.”
Issues surrounding the successful use of information are more prevalent at smaller financial services organizations. “Regional and community banks are still very siloed,” says Jonathan Looney, a product designer with Nexus Cognitive Technology Services, which builds enterprise digital products and technology solutions.
And on top of that, one can’t ignore increased concerns about privacy. “Customer data privacy and communication preferences need to be respected, and customer data needs to be protected when it’s both at rest and in transit,” says Michael Haney, head of digital core at Technisys, a digital banking technology company.
There are three main challenges to making the most of data troves to better influence the customer experience, says Rix from Woodhurst Consulting.
- While the technology to capture data and analyze it in real time is available, many organizations don’t yet have in place the necessary data pipelines and analytical tools.
- Many firms don’t have sufficient in-house data science, data analysis or data engineering functions.
- Banks must have a customer-centric culture in which data drives product decisions. Without this, Rix says, “it doesn’t matter how good your technology is, you won’t shape the (customer experience) in the most meaningful, impactful way.”
In some ways, the nimblest actors in this space are startups free of legacy burdens, and smaller community banks and credit unions. “Larger banks have the advantage of larger IT budgets and the fintechs/neobanks have the advantage of newer tech stacks, but all parties struggle to attract the necessary talent needed to leverage these capabilities,” Haney says.
Indeed, at the heart of this issue is employees, says Jill Homan, president of DeepTarget, a solutions provider that utilizes data mining and machine learning.
“It’s more about people than systems,” Homan says. “If a smaller [financial institution] has someone who is technically capable and champions a better CX, they can still accomplish great things… It doesn’t make up for the 400 staff that drive the (JP Morgan) Chase platform, but they can still be very successful.”
Dawn Wotapka is a BAI contributing writer.