What characteristics do our highly profitable and/or highly loyal customers share?
That’s the question every organization needs to ask itself at every level of its operation, and at every juncture of its strategic planning process. Set your mission to both focus your attention on that existing cohort and tailor your marketing message to prospective customers who share those traits.
Sounds simple and obvious, right? Yet, often it’s attractive to reach for that gold ring of potential customers and retarget your message in ways that alienate or otherwise shortchange your base clientele. And even organizations with a sound approach find that clearly defining the attributes of their primary audience can be more difficult than they’d imagine.
Financial institutions have a leg up on the competition in this arena, because they have accumulated a wealth of data on their core supporters that would be the envy of many retailers. That data includes both personal and behavioral information, and in many cases extends back years, even decades.
It’s hard to put a price tag on the value of that information. And yet, too many financial institutions let this information sit idly or fail to maximize its use. Perhaps they don’t possess the proper analytics capability, or have the organizational infrastructure or agility to act upon the insights this information could provide.
To gain a holistic, comprehensive view of your customer base, report and place organizational focus on analytics metrics — at an internal and external level individually, and a step-back view of the aggregate.
The goal is to link customer accounts and data, such as loans, deposits, applications, and credit bureau data, providing a solid analytical framework from which you can conduct profitability analysis, fair lending analysis, risk analysis, marketing analysis, and other key analyses on an ongoing basis.
From there, your customer analytics platform should offer the capability to compare multiple data filters side-by-side, enabling you to create custom, intelligent lists for use in advertising campaigns, risk management, collections, and more.
Use the data to identify the behaviors you want to harness and develop products to beat those needs, advises Jake Fuentes of Level Money, a digital budgeting tool.
“Many financial institutions pack a bunch of features into a checking account or loan, then spend
hundreds of thousands of dollars to acquire customers. The product becomes secondary to the behavior that incepts it,” Fuentes said at the prestigious Money 2020 Conference in Las Vegas last fall. “Focusing on the behavior that leads to the account rather than the account itself is the way to go.”
Some of the customer analytics credit unions should report on are tied to individuals, such as:
Number and type of products and services. Define your strongest offerings among each segment of your membership. Determine the factors that most drive usage of those offerings. Cross-reference those findings against life-stage assessment facts on your members you gather and update at every touchpoint, so you can better predict outreach to people who might benefit from using these products and services.
Channel preference. How do people prefer to access your products and services? What level of satisfaction do they express with these channels? Does a gap exist between consumers’ preferred channel and their actual usage, indicating a shortcoming in your platform? Also ask for consumers’ preferred communication channel, so that your messages have the best chance of landing on fertile ground.
Transactional history. Think broadly here, surveying every piece of evidence you can corral, assessing members’ interactions within and outside your operation. Accumulate usage data for credit cards, debit cards, checking accounts, savings accounts, loan payments, insurance purchases, and more. Determine where, when, and how consumers use these tools. Think about how Amazon and Google translate transactional data into predictive analytics that indicate when and where a customer is ready to make a purchase.
Credit score migration. Over the last couple of years, this has emerged as one of the prime indicators of a loan’s stability — or instability, as the case may be. Credit unions must closely monitor movement in this area, not just to proactively address troubled loans or aid struggling members but also to watch for mass shifts that signal the need for changes in underwriting, service standards, and product development.
Some customer analytics metrics should be considered in the aggregate, particular factors involving service, such as:
Customer churn. Credit unions should track raw attrition and addition numbers among the membership on the whole and within segments. What audiences are you losing, and why? What types of new members are you attracting? What keeps existing members in the fold? Conduct surveys and informal interviews when possible, and use your intuition to recognize connections between various data, changes in underwriting, and new or discontinued products and services.
Customer experience analytics. Technology has upset the apple cart, and credit unions are scrambling to present a unified, consistent omnichannel approach. Ensure that all segments of your membership — young, old, non-native English speakers, tech-savvy and luddites — feel confident and comfortable accessing your services, regardless of the channel they use.
General satisfaction metrics, such as Net Promoter Score. These assessments are especially effective at shedding light on the gray areas of the customer experience not revealed through churn, separating the facets of your operation that drive loyalty from those members merely tolerate.