The American Bankers Association (ABA) recently surveyed its members about marketing’s use of data and analytics. While most marketers were comfortable using data and had access to a wide variety of data types, there was an interesting disconnect. When asked about their ability to effectively use the data for things like targeting customers, supporting their brand and products, and developing new products, there was an obvious lean toward being “ineffective” versus “effective.”
This points to a bigger issue. As noted in my recent Forbes article, banking organizations—not just marketers—must be able to convert their customer data into actionable insights, or they risk lagging at a time when customers are demanding more than ever from their banks. Banks need insights that help them meet customers where they are in life. Insights that help them put the financial best interest of individual customers first, at the center of operation-wide decisioning.
Clearly this is a more dynamic use of data than the ABA survey alludes to, yet it’s the next step banks should be striving toward, and it’s all about hyper-personalization. Let’s take a closer look.
Be the Amazon of banking, sort of. Search for anything on Amazon and you get a long list of product options. But you also get cost-saving product bundles. Lower-priced ‘used’ options. Recommended companion products. Instant buy-now pay-later financing. A link to apply for an Amazon Visa card. All are tailored recommendations to enhance your buying experience.
This is, essentially, hyper-personalization. It involves using data and analytics to better understand the wants, needs and preferences of individual customers, and then using those insights to deliver unique experiences and moments throughout their journey. It’s light years ahead of standard segmentation, or even micro-targeting.
And, it’s exactly what banks should be striving to achieve.
Consider this. Nearly 60 percent of retail bank customers expect their bank to help them improve their financial health, according to a recent JD Power survey. However, JD Power warns that “a cookie-cutter approach will not suffice. Advice and guidance must be personalized to the specific customer, delivered to the right person at the right time.”
By employing hyper-personalization, banks can deliver on this expectation and others—at scale— across their business, throughout the lifetime of the customer relationship. In fact, some banks and financial providers are already doing it. They’re reviewing their portfolios to proactively identify and assist at-risk customers with individualized financial support. They’re creating tailored payment alternatives to keep customers in their vehicles, instead of letting defaults fester and lead to repossession. They’re protecting vulnerable customers by reaching out in the pre-delinquency stage with creative financial options.
Yet, hyper-personalization can also be used to enhance the lives of customers. For instance, banks can pre-emptively fetch and send a list of insurance quotes to a customer who applied for an auto loan. Or they can offer tailored perks and rewards like a free latte from a nearby coffee shop, based on a customer’s spending patterns, preferences and lifestyle. Check out our intriguing Digital Jane series to see how this works.
Here's the thing. A customer is more than a moment – a customer is for life. Likewise, hyper-personalization is more than a one-off project. It shouldn’t be treated as an add-on; it must be embedded in a bank’s enterprise architecture. Here’s a glimpse of what that looks like.
Converting “bits and pieces” into 360-degree views. To achieve hyper-personalization, banks must understand their customers’ activity, behaviors and preferences across the entire banking ecosystem. This can include everything from standard account payment data to more contextual data feeds that reveal how much customers are spending per purchase, where they’re spending their money or even how often they check their account balances.
Individually, these disparate bits and pieces of data can be difficult to interpret and use. But, when you integrate them into a cohesive, 360-degree view of the customer, they power a far more detailed understanding of where individual customers are in life right now, and their best path forward. Banks can then use these insights to craft unique moments reflective of their commitment to improving their customers’ lives and financial health.
In general, the process of deriving and applying hyper-personalized insights involves a lot of moving parts. Things like, gathering your customer data. Building profiles. Applying advanced analytics and machine learning techniques. Adding in business constraints. And finally, operationalizing the insights at scale to create bespoke, “in moment” customer experiences.
But there’s a secret sauce in this process. It’s called “applied intelligence” and it comprises a dynamic mix of AI, advanced analytics and human expertise that can be accessed and used by all teams within the banking organization. It connects, strengthens and binds a bank’s existing data and systems, enabling the consolidation of all streams of customer data into an agile, data-driven framework that produces more personalized and actionable insights. See an illustrated explanation in our Applied Intelligence ebook.
Think of it this way. Instead of internal teams using different platforms and disparate data to develop, launch and manage separate analytic projects, an applied intelligence platform (sometimes called an AI decisioning platform) provides a collaborative space. A secure, cloud-based environment where decisioning assets—things like data features, predictive models, algorithms and more—can be created and shared across the business.
Everybody can work from the same treasure trove of constantly updated and automated customer data. Functional teams can save time and resources by piggybacking on each other’s analytic projects. Business users can work more efficiently by learning in real-time from analytic outcomes and making smart adjustments. Business analysts, data scientists, and technology partners can innovate to create new financial products and more customer-centric approaches by experimenting with new data sets, analytic strategies, AI and machine learning techniques and more.
The result is a smarter, leaner banking infrastructure capable of powering real-time, hyper-personalized moments and “next best experiences” that tangibly improve the lives of customers.
Building a go-forward strategy, without “ripping and replacing.” No matter where your bank is in terms of data and analytic sophistication, you can use what you have within an applied intelligence platform to generate deeper customer insights. Thankfully, there’s no need to rip and replace. Here are a few high-level capabilities that can be integrated with your existing data and infrastructure, based on the Insights capabilities within FICO® Platform.
- Analytics and machine learning enable you to use powerful AI techniques to develop and train your machine learning models so you can deeply understand and predict customer behavior, enhance your decision making and power more customer-centric strategies. You can use FICO’s analytic development capabilities or automatically import models built elsewhere.
- Feature generation and profiling facilitates an advanced data infrastructure for AI-enabled, digital business operations. You can calculate, maintain, and expose thousands of data features tied to business entities across your internal and customer-facing decisions, ranging from static calculations and compliance metrics to complex event-series aggregations derived from high-volume raw data sources.
- Link analysis helps you find networks of association across federated data stores. It gives you a single view of the customer, which enables your functional teams to treat each customer holistically (no matter how fuzzy they may be represented in your disparate records) and understand shared traits that can help you detect important behavior.
- Optimization can help operationalize analytics and AI across your banking organization. It allows your stakeholders across data science, operations research, business staff, and IT to collaborate and rapidly create, validate, and launch models that find the best choice amongst alternatives. Even better, this capability extends to ‘Simulation’ in the OUTCOMES column. Simulation allows anyone to experiment with hypothetical scenarios and accelerate both learning and innovation.
These platform capabilities lay the foundation for generating intelligent, customer-focused insights that support improved decisioning and next best customer experiences, at scale, across your entire banking operation. In addition, the following overarching themes should guide your analytic evolution and become top-of-mind priorities.
Dissolve your data silos. By connecting disparate data across your bank systems, you enable your internal teams to share and reuse valuable assets from feature sets and transaction profiles to analytic models and standard calculations. These and other decisioning assets will become discoverable, easy to clone and reuse, as well as fully traceable. This helps improve consistency and productivity with a full view to dependency management, so the impact of every change is fully understood, 100 percent of the time. Remember, the better your connections, the better your insights.
Innovation and efficiency can go together. Applied intelligence doesn’t just deliver hyper-personalization and competitive distinction. Your ‘Digital Teams’ have become the productivity backbone of your digital strategy. By focusing on enabling and empowering these multi-disciplinary units of innovation, you should be pleasantly surprised by the efficiency ratios you see. It’s a consequence of everything I’ve shared so far: collaborating, sharing, and building a unified foundation of digital intelligence.
Achieve and maintain trustworthy AI. At a time when demand for AI and machine learning technology is soaring, 71 percent of financial executives admit they have not implemented responsible AI in their organizations. Knowing this, be sure your AI and machine-learning strategies include explainable models that detect bias, ensure fairness, and are transparently auditable. Otherwise, you risk hurting your customers instead of helping them.
No doubt, this is a lot to think about. But the point is this: a good place to begin your journey toward more hyper-personalized banking is with an enterprise approach to applied intelligence. In fact, IDC predicts that by 2025, 75 percent of business leaders will leverage applied intelligence platforms and ecosystem capabilities to become more agile and adaptive. Everything you need—data, insights, actions and outcomes—is connected within a single, inclusive environment to automatically generate fresh, real-time insights that reveal what’s really happening with your customers and how best to move forward. Only then, can you craft the specialized strategies and 1:1 experience that move them closer to their financial goals and catapult their loyalty to you.