.png){kind=logo}
Top banks only have a 32% market share of the total wealth management market globally, losing ground to traditional asset management firms and independent advisors. Most of the wealthy customers still use retail banking for their day to day uses and banks could expand their wealthy client base by identifying those high-potential customers within their existing retail portfolios, yet most financial institutions fail to do so, mainly by using unsophisticated segmentation models or not targeting their own portfolio at all. The result? Banks routinely overlook their most valuable clients, missing revenue opportunities worth tens of millions.
Daryn Kalym tackled this challenge while analyzing customer profiles from a big data perspective in highly dynamic retail banking markets. Where traditional segmentation saw average customers, his behavioral clustering algorithms revealed substantial untapped revenue potential driven by increased customer loyalty, extended customer lifetime value, and cross-selling opportunities for financial and non-financial products, value that can translate to millions in revenue over 3-10 years, even for smaller institutions. The discovery wasn't a fluke – applying similar methods across markets, Daryn has consistently uncovered hidden premium segments numbering in the hundreds of thousands.
Now Daryn has refined these discoveries into a systematic framework that combines supervised and unsupervised machine learning. His approach doesn't just find missed opportunities; it explains why traditional methods fail and provides actionable strategies for capturing value. Here are five key techniques transforming how financial institutions identify, understand, and monetize their true premium customers
Traditional banking methods segment customers based on demographics, assets under management, and sometimes loan portfolios. Banks typically classify customers as premium if they hold significant account balances – often treating clients with $1 million or more in assets as high-value. Modern analytics goes beyond these static metrics, analyzing behavioral patterns to predict lifetime value (LTV) and discover hidden premium segments. Daryn combines clustering algorithms with LTV models to discover insights that allow us to take action on customers.
"Banks are losing high-value customers every day because they are only looking at surface indicators," Daryn explains. "By using behavioral segmentation instead, we can illuminate customers as groups that traditional methods overlook completely."
Daryn's approach has delivered results across several international markets. In one implementation, behavioral analytics identified previously unknown communities of premium clients based on their spending patterns and engagement behaviors rather than account balances – opening up entirely new targeting opportunities. "Traditional segmentation would have classified these customers as average retail clients," Daryn explains. "But their transaction behaviors revealed premium characteristics hidden beneath the surface."
Customer lifetime value (LTV) modeling is among the most powerful applications in banking analytics. LTV models use a range of data to look beyond a customer's current balances or transaction frequency, quantifying how spending, product usage, and engagement behaviors may vary across time.
Daryn's one LTV model uses customer transaction histories at touchpoints, along with the economic data. "Even when traditional banks use LTV models, they typically focus on historical revenue – how much money the client has already brought to the bank," he notes. "Full-scale predictive LTV models go further, forecasting how much revenue a customer will generate in the future based on behavioral trajectories”.
In one notable case, Daryn's team built an LTV model analyzing a bank's existing premium customer segment. The results were unexpected: the model revealed that the bank's current premium offerings were generating negative returns on investment. These insights prompted a comprehensive re-assessment of the value proposition and service packages the bank provided to premium clients, ultimately leading to a restructured premium program with improved economics.
Identifying premium customers early in their lifecycle constitutes a challenge for financial institutions. Wealth levels based on traditional income indicators, like account balances, are missing customers with great potential earnings but current moderate balances on saving and current accounts.
Daryn has developed advanced lookalike models to identify customers who behave similarly to known affluent clients. Advanced algorithms look at merchants and their categories, timing of transactions, and digital engagement, and then develop an ‘affluency’ score based on those characteristics.
Implementation has occurred in a few international markets, and the results have been consistent and repeatable, each identifying a very similar high-value, untapped segment.
Even with analytical prowess, advanced modelling will be successful only if the financial institution is able to surmount its serious “infrastructure” and “data quality” issues that invariably differ from bank to bank.
The synergy of banking and analytics in all markets has a set of constraints. Institutions are often working with legacy systems and inconsistent data quality, making analytical processes more difficult; this requires agility in terms of expertise and approaches to the analytics.
"Many banks do not have structured data, or are working with legacy systems that were not designed for advanced analytics," Daryn said. "Successful implementation frequently requires developing interim data pipelines, feature engineering, or auditing data and analytics processes within the constraints of what is available."
Projects typically begin with a data quality assessment and developing an analytical infrastructure. For banks seeking to develop their analytical capabilities, significant investments in infrastructure are required, which can also be time-consuming. However, many analytical projects can be conducted using external computing resources, allowing banks to test methodologies before committing to major system overhauls.
When the technical hurdles are addressed, the final crucial hurdle is embedding analytical functionality within operational business processes to create a sustainable impact. Analytical insights only generate value when made part of a process. Most effective banking analytics projects have processes embedded within a pre-existing organisational structure, thus ensuring capabilities are maintained beyond the initial deployment.
Daryn places a priority on knowledge transfer throughout an engagement. "Analytical frameworks need to become a part of the bank's internal capabilities and not just external deliverables," he says. "The key is helping teams understand the methodology, as well as have the ability to work with the new tools so they can adapt and apply these approaches to their specific contexts."
As banking continues to evolve to digitise its operations, banks with a solid foundational analytical framework can take advantage of new technological opportunities as they arise.
By using Daryn's methods, financial institutions can pursue these tactical methods:
1. Comprehensive Data Analysis for Hidden Premium Detection
Analyze transactional patterns to find customers with high affinity towards luxury lifestyle. Focus on non-transactional metrics like loan volumes and account balances, and examine bill payment patterns – consistently high monthly utility or mortgage payments may indicate customers who own valuable assets but keep their primary banking relationships elsewhere. Additional insights can come from device data (iPhone vs Android usage) and other behavioral indicators that banks can track.
2. Transaction analytics LTV Models Over Current Balance Focus
Use predictive models that weight levels of customer engagement (consistency) and changing trajectories of growth, over the immediate balance; monitor product adoption and frequency of digital engagement, and remember – a customer with increasing levels of spending consistently over time most often emerges as a premium customer. Incorporate third-party analytics such as credit bureau scores and external data sources to build more comprehensive customer profiles..
3. Optimal Timing for Premium Service Offers
Identify key moments in customer journeys when they become most receptive to premium services. Target customers who are increasing their transaction frequency or expanding into new spending categories. These behavioral changes indicate readiness for premium offers and generate significantly higher conversion rates than demographic-based targeting.
Global banking is evolving and opening new avenues for the use of advanced analytical solutions. Digital banking penetration continues to increase, resulting in comprehensive behavioral datasets to develop analytical models.
Daryn is uniquely positioned because his original contribution to behavioral client segmentation was through the use of advanced machine learning models, grounded in banking transaction data and customer behavior patterns with banks and financial institutions. His exclusive arrangements have driven tremendous revenue for financial organizations, all the while behavioral segmentation models continue to proliferate through global banking networks.
The combination of behavioral segmentation and predictive modeling transforms traditional banking into a data-driven customer value optimization engine. Banks that successfully identify their hidden premium customers can unlock additional revenue potential that traditional demographic segmentation leaves on the table.
