Banking has entered an uncomfortable phase of commoditization. A checking account is now a feature list. A mortgage is a rate on a screen. Investment advice is increasingly automated. As digital-first fintechs siphon off high-margin moments such as investing, lending, and payments, the strongest proprietary asset most banks still control is not a product. It is longitudinal customer data: transaction histories that reveal intent, life events, risk signals, and capacity to buy.

Yet many institutions remain data-rich and insight-poor, running broad campaigns that treat customers as clusters rather than individuals. The outcome is predictable. Conversion rates lag, cross-sell opportunities are missed, and churn becomes inevitable.

Furkat Kasimov, founder of GetSelene.ai, argues the fix is not better copy or more campaigns, but a change in operating model. He describes a move from broadcast marketing to what he calls the “Audience of One,” where every customer is treated as a segment of one, and engagement becomes a continuous, automated conversation that responds to a customer’s financial “heartbeat” in real time.

Why banks are uniquely positioned to personalize

Retailers and media companies often personalize based on clicks and browsing behavior. Banks can personalize based on reality. Transaction data shows when a customer has surplus cash that could be invested, when liquidity is tightening, and a credit line would reduce stress, or when a household is entering a major life phase, such as relocation, expansion, or retirement planning.

Banks can also detect early churn signals, including recurring overdrafts, fee fatigue, repeated declined transactions, or sudden changes in payroll deposits. These indicators are not abstract predictions; they are observable behaviors embedded in daily financial activity.

The business case for personalization is strong across industries, but it is particularly direct in financial services because the unit economics are large. McKinsey has reported that personalization commonly delivers revenue lifts in the 10 to 15 percent range, depending on execution. In banking, The Financial Brand has cited BCG research showing that personalization can reduce churn and increase sales, producing annual revenue uplifts of roughly 10 percent.

Proof that proactive personalization works at scale

Large institutions have already demonstrated that AI-driven engagement can operate at a meaningful scale. Commonwealth Bank of Australia described its Customer Engagement Engine as making more than 35 million decisions per day, delivering real-time “next best conversations” across channels. Its reporting also points to

machine learning models processing roughly 157 billion data points to support this capability.

Royal Bank of Canada’s NOMI platform shows how personalized financial guidance can strengthen relationships while driving measurable outcomes. RBC reports that NOMI Find & Save has helped clients set aside more than $7 billion since launch, while NOMI Forecast has been used by over one million customers to anticipate future cash flow. These interactions are not just engagement features. They reinforce trust and keep the primary bank central to saving, borrowing, and investing decisions.

Bank of America’s Erica provides another indicator of maturity and scale. As of August 2025, the bank reported more than three billion client interactions with Erica, averaging over 58 million interactions per month. The system has delivered more than 1.7 billion proactive personalized insights, including guidance that leads to product conversations.

Even outside conversational assistants, personalized decisioning has shown direct conversion impact. U.S. Bank reported a 127 percent increase in annual booked accounts in an Adobe-published case study on scaling personalized experiences.

Across these examples, the pattern is consistent. When banks replace generic outreach with contextual, individualized messaging, both conversion and retention improve.

Where most bank marketing still falls short

Most banks already operate lifecycle programs, onboarding sequences, product campaigns, and regulatory notifications. The issue is not a lack of messaging, but the underlying architecture.

Traditional customer engagement platforms are batch-based, segment-driven, and siloed by channel. They struggle to answer the central growth question in modern banking: what is the single most relevant action for this specific customer right now, and which channel is most likely to succeed?

This limitation shows up most clearly in three revenue-critical areas. Lending outreach is often mistimed, with offers arriving too early, too late, or not at all, even when transaction data indicates a clear need. Excess cash sitting in low-yield accounts remains vulnerable to fintech investment apps because banks lack automated, behavior-driven education and conversion paths. Application abandonment mirrors e-commerce cart abandonment, yet many banks fail to follow up quickly and personally, allowing conversion probability to decay.

How GetSelene.ai turns “Audience of One” into revenue

GetSelene.ai was built for performance-driven environments where speed, iteration, and conversion discipline are essential. Applied to banking, it acts as an orchestration layer that converts customer events into personalized messaging across digital channels.

A common problem in bank marketing is internal amnesia, where email, app, and SMS programs behave as if they belong to different organizations. GetSelene.ai is designed to coordinate journeys so messages reflect the current context and do not contradict each other.

Financial decisions are rarely driven by rate tables alone. GetSelene.ai incorporates behavioral psychology to frame messages in ways that motivate action, such as highlighting loss aversion around idle cash or using reciprocity when a fee is waived.

The GetSelene.ai platform orchestrates email, SMS, WhatsApp, and push notifications as a single system, selecting both timing and channel based on customer behavior. A “next best offer” only has value if it reaches the customer through a channel they actually attend to.

Where banks see the fastest returns

Executives often want a measurable impact quickly. Some of the highest-return use cases include recovering abandoned applications through immediate, personalized follow-up that addresses predictable objections and resumes progress. Transaction data can also surface pre-need lending opportunities, allowing banks to offer credit as assistance rather than advertising, tied to clear timing and capacity signals.

Persistent surplus balances can trigger personalized investment education and low-friction actions that keep assets within the bank’s ecosystem. Proactive fee-savings interventions, including alerts and selective waivers, may appear counterintuitive but can significantly reduce churn and protect long-term profitability.

What success looks like

The banks that succeed in the next phase will not be the ones that send more messages. They will be the ones that send more relevant messages, triggered by real customer context and delivered through the right digital channel.

Kasimov argues that GetSelene.ai brings this Audience of One operating model to banks that lack the appetite or timeline to build in-house AI platforms. Instead of treating personalization as a feature, the platform treats it as the system that drives revenue, retention, and relevance.