- As banking has evolved, much more of the acquisition, retention and deepening responsibilities have landed at Marketing’s door.
- No longer can marketers hide behind vanity metrics and campaign count to show output. Today, they’re asked to maximize the institution’s return on investment in marketing while contributing to overall corporate goals, and they’re doing so by personalizing communications through the use of predictive modelling and decision intelligence. Empowered by technology infused with artificial intelligence, banks are building customer value for the long term.
- Here we look at four examples of how banks have recently added significant relationship value at different stages of the customer lifecycle.
Use Case: Keeping Relationships Alive after the First 90 Days
In the first 90 days of new relationships, most banks suffer significant attrition. Leading providers, however, are using decision intelligence to deliver personalized marketing communications during this critical window to drive longer-term engagement and value.
The first points of contact—during which the new customer receives account-opening confirmation followed up with funding instructions—are well-established and expected. But after that, there are dozens, if not hundreds, of possible ways to engage a customer in ways that strengthen the relationship. A one-size-fits-all messaging approach can leave many customers feeling like their bank just doesn’t get them.
Using Amplero, Curinos’ AI-fueled decision engine, to optimize communications after the standard initial treatments, a U.S.-based bank boosted click-through rates for early relationship-building marketing experiences by more than 160%.
During the initial “learning phase,” Amplero’s platform automatically tested a wide library of possible messages and offers against diverse customer contexts (within eligibility guardrails)—no pre-scripted test designs were required. By tracking every delivery and outcome at the individual level, Amplero gathered real-time performance insights. Once enough data was collected, the system moved into an optimization phase, using these learnings to deliver tailored communications for each customer, ultimately helping the bank personalize at scale.
By solidifying each relationship early on, the increased engagement targeted attrition, with key takeaways that will inform balance building and product-adoption marketing going forward using Amplero’s decision intelligence.
Use Case: What if Marketing Generated $1bn in New Deposits… and it Was Only the Beginning?
In today’s retail banking market, rates fluctuate quickly and competition for deposits is fiercer than ever. That means marketers need to react instantly to changing customer preferences while maintaining an unwavering commitment to their institution’s business goals. Using Amplero, one bank has added more than $1bn in incremental account balances, about half of which is truly new to bank, and has only just started the full optimization process.
The early growth was achieved during an initial learning and design period that focused on cross-selling CD and savings products involving various terms and rates. The Amplero team and platform applied industry best practices and advanced customer scoring insights, serving up marketing treatments to eligibility-driven segments.
During the initial learning and design phase, the focus was on cross-selling CD and savings products with various terms and rates. Drawing on industry best practices and advanced scoring insights, Amplero served targeted marketing treatments to eligible customers—without having to rely on deterministic, rules-based sequencing.
Instead, the platform leveraged a broad library of marketing collateral and applied probabilistic experimentation to test multiple approaches in real time. This flexible data-driven process drove early deposit growth while it supplied the rich performance data used to train and refine Amplero’s optimization model for subsequent phases.
Optimizing Cross-Sell
As soon as the optimization model is deployed, Amplero switches from naive learning to optimized decisioning, using all previously gathered insights to tailor messaging at a micro-segment level. At the same time, the platform continuously “listens” and adjusts, capturing new performance signals and refining its recommendations in near-real time. This dynamic approach quickly identifies which messages to deploy and in what sequence, leading to significant improvements in engagement and deposit growth. With each iteration, the marketing team sees stronger results across key KPIs, including increased account openings and total deposits.
Scientific and Certain
Even with unpredictable market conditions and other macroeconomic factors rapidly reshaping consumer mindsets, many banks still depend on “batch and blast” campaigns—generic messages tied to rigid schedules set far in advance. This leaves them unable to adapt swiftly, causing engagement strategies to become outdated almost as soon as they launch.
At the same time, banks have significant cross-sell opportunities waiting to be tapped. To capture that potential, marketers need to leverage fresh customer data, continuously test and refine their messaging and recognize that consumer needs can shift at a moment’s notice. By embracing continuous optimization and agile personalization, banks can stay ahead of the curve and drive meaningful growth despite the volatility.
Use Case: Personalization’s Possibility:
95% Increase to MMA Openings
As banks strive to acquire new accounts, one institution nearly doubled its money market account openings after training an optimization model specifically for that purpose. Since starting to optimize for conversion in March of last year, the marketing team’s use of Amplero has driven a 95% increase in monthly account openings.
The optimization model was designed to cross-sell money market accounts, aligning with a strategic goal set just before its launch. After a brief learning phase—during which the system captured and validated data—the model went live to focus on generating new money market accounts.
The next phase of optimization will focus on driving overall customer value, balancing both the quality and marginal cost of deposits from cross-sold accounts. Rather than just recommending a single “next best” offer, Amplero’s AI can continuously weigh multiple objectives—like deposit growth, cost of funds, and long-term relationship potential—so the bank can invest in strategies that sustain profitable growth and deepen customer engagement.
Use Case: What if Two-Thirds of Your
Private Wealth Emails Were Being Opened?
Wealth clients present a conundrum for banks: They’re good for high deposits and deepening potential, but they can also be swayed by digital wealth management platforms and fintechs that offer user-friendly experiences, lower fees and innovative products. With competition rife, banks need to be able to communicate to them clearly and to consistently react to their changing needs and circumstances.
Using decision intelligence capabilities, one bank has been successfully driving deposit growth, engagement and retention across estate management, private wealth checking accounts and security-backed lines of credit. While the average email open rate across financial services is about 27%, this bank recorded that more than 65% of emails sent in 2024 were opened. And while the industry average clickthrough rate is 2.4%, 4.01% of the bank’s email calls to action were being clicked.
The bank used Amplero to test and learn which creatives, tones and value propositions resonated most effectively with each customer, using the platform’s AI capabilities to absorb, learn and action data at scale. After metrics were established around the strategies and customer segments, a model was deployed to optimize for click-through rates. The always-on model continues to mature, and as it achieves optimization, the team is layering in additional strategies to drive even better results.
