July 19, 2022
As we’ve explored in previous blogs, more regional and community banks are aspiring to become “Data First”—a modern bank that manages customer data as a business asset, rather than as a business byproduct. Once a bank has laid the groundwork to do this effectively, it can mine and use data as a raw material to produce business intelligence about its customers. It can then use this insight to provide more personalized customer experiences.
Here, we’ll explore different types of data your bank should be leveraging, and how these data types offer unique value at each phase of the customer journey.
Before an individual ever comes into contact with your organization, you can start to get to know them by leveraging the wealth of data available from their online footprint. By aggregating demographic and geographic data with market insights, you can conduct smarter prospect analytics that will help you tailor your marketing and product offerings.
Data types that drive the Lead phase:
Lookalike data. By analyzing browsing history and other online behavior at scale, your bank can more easily identify consumers who, demographically, look and act just like your target audiences. This enables you to segment prospects more intelligently and market to them with highly targeted campaigns.
Market insights. Market and marketing data can help you identify additional leads. For instance, market insights can tell you when and where your competitors are closing brick-and-mortar branches. Your bank can then use this information to geotarget consumers who will likely be impacted and in need of new banking options. Meanwhile, marketing data can help you target customer profiles associated with your competitors.
Life-stage data. This indicates what a consumer’s financial priorities are likely to be based on their current stage of life. Life-stage data augments the demographic insights of lookalike data by looking at specific activities a user has performed on other fintech platforms or ads they have clicked on for other banks’ products. These can be signals of recent or upcoming life events—like getting married, saving for college, or retiring—so your bank can promote products that align with their needs.
In this phase, a lead has expressed interest in your bank’s product or service, but they’re not a customer yet. At the same time, they’re no longer just a persona—they’re now a person your bank knows by name. This is where your bank can start getting to know them on a deeper, individualized level and become acquainted with their personal preferences and financial history.
Data types that drive the Acquisition phase:
Engagement data. Monitoring a prospect’s engagement with your website, webinars and other resources can tell you a lot about financial topics that interest them. Their level of engagement can also indicate their propensity to convert. By knowing who’s highly engaged and why, your bank can focus its acquisition efforts on the most promising prospects.
Third-party data. This external data augments what you’ve learned about a prospect via engagement data. It can include mortgage application history, out-of-wallet information, credit reports and more—adding an extra layer of insight that helps you pre-qualify prospects and personalize your communications.
Omnichannel analytics. This is organically collected data that reveals a prospect’s demonstrated need and how they prefer to engage with your organization. Whether they submit an online form or seek support at a local branch, the content and manner of their inquiry tells you a lot about how you can offer them value and personalize your customer service.
This phase includes onboarding, which may be the most data-intensive stage of the customer relationship. At this point, your bank is gathering and analyzing vast amounts of disparate data to perform due diligence, originate accounts, and assess individual risk. Because all of these data points are highly individualized, they allow your bank to take an individualized approach to working with each customer. Additionally, they allow you to produce a complete and accurate customer picture—which is critical to complying with KYC and AML requirements, adapting to customer preferences, and making underwriting decisions.
Data types that drive the Nurture phase:
Interaction data. This data offers a 360° view of all customer touchpoints, so you can see where and how your customer interacts with your organization. Whether they’re calling the contact center, walking into a branch, using online self-service, or have multiple products at your bank, this intelligence helps you make informed decisions about where to focus your resources to best serve your customer.
Transaction data. With access to a customer’s transaction history, your analytics can predict when a customer is most likely to need certain types of support. Knowing a customer’s direct deposit schedule and amount, when their car loan started or credit card promotion is about to end can help you anticipate their needs and identify new opportunities to support them.
White space analysis. Reviewing the customer’s interaction data and transactional data can show you where they have held-away accounts with other institutions. Are they paying a mortgage to another bank, investing with another wealth management firm, or sending money to a high-yield savings account at a competing bank? This data gives you a fuller picture of the customer’s financial life. In turn, it allows you to better assess their risk and borrowing capacity. It also enables you to offer them comparable products with competitive rates and features.
This phase is about making customer relationships as “sticky” as possible by offering value across more dimensions. Establishing a trusted relationship with your customer increases the chances that they will rely on you for more of their financial needs. This aids your upselling and renewal efforts and empowers relationship managers to serve as trusted advisors to your customers.
Data types that drive the Grow phase:
Life-stage data. Like in the Lead phase, life-stage data signals what’s financially important to a person at this time in their life. Only now, this data isn’t cloaked or coming from a third-party aggregator—it’s coming directly from a customer you know. They may open a joint account with their new spouse, or ask about starting a 529 college savings plan; knowing this can help you stay attuned to their needs on a personal level and tailor your support as their life events unfold.
Servicing metrics. How many service calls did a customer make in a given time? How often do they conduct balance inquiries? How frequently do they search for certain terms on the mobile app? All of this can tell you where you have an opportunity to improve their experience and make it easier for them to use more of your offerings.
Renewal data. By knowing the lifecycle of the financial products your customer is using, you’re better equipped to help them renew their products before they expire. Not only does this improve retention, but it also can be an opportunity to upgrade them to a longer-term product and increase their lifetime value.
Transactional data. Your ability to monitor and prompt certain actions before a customer does can be key to building trust. Transaction data from the banking core can offer insight into a customer’s IRA RMD amounts and deadlines, how much equity they have in their home, how long they’ve owned their home, and other details that allow you to issue reminders and recommend expanded offerings—like a HELOC or a retirement account roll-over.
A data-first bank knows how to use different types of data strategically at different phases of the customer journey. By being able to aggregate and isolate the right data points at the right time, your bank can begin to unlock new insights to exponentially improve the customer experience.
Of course, knowing how to store, process, and interpret the data can be overwhelming for banks that have lean IT and data teams. But to truly become Data-First, a bank must understand best practices and tools for keeping data in the data lake for AI/ML analysis, housing it in the data warehouse for analytics and operational reporting, storing it in data marts and customer data platforms for marketing, and keeping it in CRM systems for customer interactions.
Fortimize can help banks apply the right architecture, tools, and data for the right job— but it starts with your people and processes. We’ll work with you to build a strategy from the ground up and support you at every stage of your data-first journey — from planning to design to implementation and beyond. Our experts can help you leverage your investments to get the most out of your data and equip your users and decision makers with actionable insights that transform your business.
Contact us to connect with one of our experts.