By: Craig McLaughlin- CEO, Finalytics.ai
Community banks and credit unions have long prided themselves in deep local roots, personalized service, and enduring customer relationships.While these differentiators, typically delivered through in-person branch interactions, successfully created a competitive advantage over national banks with far larger marketing and IT budgets, this time-tested model faces significant headwinds.
Half of all credit union business originated through digital channels in 2021, up from just 15%only five years earlier. Although COVID functioned as an accelerant, this digital shift was already well underway and shows no sign of abating. As fewer customers visit branches the primary vehicle upon which community institutions have pinned their value proposition is being neutralized –amid an environment that plays to the strengths of fintech disruptors and large tech-forward banks.
A Finalytics.ai survey indicates that 56% of consumers would prefer to engage with community financial institutions, but believe their digital offerings fall short of their needs. As a result, 53% chooseto bank at regional or national institutions. This implies not only an opportunity for smaller players to pick up market share but also a risk of customer attrition if their digital shortcomings – real or perceived – remain unaddressed as the migration from branches continues.
The good news for community institutions is that given nominal retooling, they remain well positioned to deliver on their longstanding high-touch model. Service providers stand ready to assist them in leveraging the wealth of data they possess about their customers and members, deploying much of the same technology FinTechs have used against them. By applying this knowledge to their solid existing relationships and established product portfolios, these financial institutions can redefine personalization for a digital era – preserving their key advantage while also delivering the convenience that today’s market demands.
Conducting Effective Customer Journey Orchestration
Businesses across all sectors strive to segment their customer bases in order to communicate with andserve each demographic in the most effective manner. This process is not without risk; however, Millennials and members of Generation Z will be quick to tell you they don’t all speak with a single voice or follow every trend in lockstep.
When interacting with banks and credit unions, consumers take unique, individualized paths based on their needs at a given moment – understanding this context can be as valuable as demographics. For this reason, it’s important for institutions to focus on the customer journey, influencing the path beginning when a customer or prospect initially signals an interest or need and continuing through product purchase and service after the sale.
The ultimate goal of customer journey orchestration is to deliver a segment-of-one journey, updatable in real time, to each customer or prospect. Today, all customers likely receive the same information and product offers. As financial institutions apply artificial intelligence (AI) and machine learning to increasing volumes of data these messages will diverge, creating more relevant propositions.
Successful execution requires a combination of internal and external data. In the simplest of examples, offers are based onconsumers’ past actions. A member who has been visiting Zillow or scanning online mortgage calculators is clearly ripe for mortgage offers. A household browsing college websites is a candidate for student loan offers.
Community institutions are accustomed to relying on external data – such as third-party credit bureau reports – to mitigate lending risk. For existing customers, they certainly leverage internal data to inform credit decisions as well. However, credit unions and banks are historically less adeptat leveraging data for marketing– even though it can often be derived from the same sources.
Internal transaction data serves as the bedrock for segment-of-one marketing. For instance, a customer with significant spending on airfare and hotels is likely ripe for travel offers. Lots of purchases at Home Depot? They probably enjoy DIY and home improvement. Transactions at REI may indicate a customer who will respond to messages and images conveying nature and adventure. Thorough personalization also requires a complement of external data such as web searches, geolocation data and purchase activity from the share of wallet outside any institution’s line of sight.
Ongoing fine-tuning and “learning by doing” is a necessity- AI engines improve over time based on actual experience and the opportunity to adjust to real world outcomes. Not every attempt at personalization will hit the mark, but it will be closer than blindly posting banner ads that everyone sees. And the more data the machine learning engine consumes the better its efficacy. Early returns have shown 3x the uptake on offers using this model.
The Road Ahead
Finalytics.ai’s analysis of the 50 largest credit unions reveals that just 17 have implemented full digital consumer journey tracking, and only seven had extended the process to deliver some level of personalization. The vast majority of credit unions continue to deliver the same content without regard to geolocation data or prior search history.
Some brave marketers have attempted personalization initiatives without the support of AI/machine learning or related technologies. These teams quickly discovered that personalization without automation simply isn’t practical.Marketers quickly encountered time and resource roadblocks as they attempted to craft unique messages for various segments. Given the broad product portfolios maintained by many institutionsthis quickly added up to potentially hundreds of messages and hundreds of ways to make an offer to someone that only demonstrates how little their institution knows about them.
Trained data scientists remain in short supply and high demand, posing another hurdle. Even community institutions fortunate enough to secure budget funding for such a position will be hard pressed to recruit a viable candidate.Even then, the new hire will likely have to be “trained” in the ways of community banks and credit unions. That training could take about as long as it required for the individual to increase his or her salary by 50% at another organization.
Fortunately, AI has moved out of the realm of futurists, with affordable and scalable tools now available enabling non-data scientists to deploy predictive models using intuitive drag-and-drop interfaces.
Impactful personalization doesn’t demand the most sophisticated marketing stack or the biggest budget; it does require a deep understanding of how AI and machine learning powered personalization can offer moments of impact to buildingcustomer trust and cementing relationships.Institutions should create a technology platform capable of integrating new channels without discarding past development or disrupting the existing IT landscape.
Of course, a digital banking solution must also be in place to fulfill the promise of these personalized offers. Many community institutions have already implemented effective solutions. Market perception is often the greater challenge; a modernized approach to personalization can fuel a fresh look from customers and prospects. It’s important to get the foundation right to avoid the all-too-common issue ofpropagating siloed systems.
FinTechsfirst generated attention with their marketing savvy, digital-forward approach and focus on optimizing specific use cases. Disruptors like SoFi and Rocket Mortgage are alreadyextendingproduct portfolios and marketing scope beyond their initial narrow focus. Community institutions can aim for a similar endgame from the opposite starting point – they already have full product suites and established customer relationships. With new tools and techniques at their disposal, customer journey orchestrationcan drive the personalization necessary to meet the evolving needs and expectations of existing customers—and to acquire new ones.