OPINION | Purposeful Personalisation: AI-driven transformation in financial engagement

To win trust, financial institutions must adopt Purposeful Personalisation with AI.

India’s financial services sector has rapidly transitioned into the digital age, unlocking immense potential; yet the industry now stands at a crossroads. Many banks and fintechs talk about “customer-centricity,” but far too often their outreach amounts to digital static: generic messages dressed up with a name. The result is eroded attention, falling trust, and lower lifetime value for customers.

A recent Customer Engagement Benchmarks report provides a data-driven view of where the industry is underperforming. The findings make one thing clear: generic communications underdeliver. They trigger higher unsubscribe rates, lower conversion, and squander marketing spend, especially when run on legacy, fragmented technology stacks that can be many times more expensive to operate than modern, integrated platforms.

If financial institutions want to win in a trust-driven market, they must move beyond volume-focused tactics and adopt what I call Purposeful Personalisation, powered by the next evolution of AI-driven, goal-oriented systems.

Three Core Engagement Failures

The benchmark data highlights three key problems that repeatedly undermine engagement strategies:

1) The Low-Impact Default

Mass broadcasts, whether for new cards, savings plans, or offers, fail because they ignore individual context. Benchmarks show that behavior-based email campaigns deliver 2.18× higher CTOR than generic emails in the banking and finance sector. Relevance matters: when outreach reflects a customer’s recent actions or intent, engagement multiplies. Treating personalisation as a superficial label squanders the chance to build genuine Customer Lifetime Value (CLV).

2) The Fragmentation Barrier

Seamless personalisation depends on a single, trusted customer profile. Yet many organisations are shackled by fragmented data, siloed martech stacks, and manual integrations. The result is what industry practitioners call a “fragmentation tax”: legacy architecture that can be 13×–45× costlier to run due to complexity and inefficiency. Without unified data, real-time personalisation, compliance checks, and reliable campaign orchestration become slow and error prone.

3) Missing High-Intent Moments

Customers are most receptive during key lifecycle moments—such as applications, approvals, renewals, and product interactions. Generic outreach at these points is a lost revenue opportunity. Benchmarks reveal a striking uplift: iOS journey-based push notifications delivered 9.3× higher CVR than generic push. Timely, context-aware messaging during critical moments produces dramatically better conversion and customer satisfaction.

From Predictive to Agentic AI

The path from noise to meaningful engagement requires advancing beyond manual rules and simple predictive models. While Predictive AI identifies customer intent, such as propensity to adopt wealth products, the next layer, Agentic AI, executes on that insight autonomously and at scale.

Consider what an agentic approach delivers:

* Precision Timing & Channel Orchestration: Benchmarks indicate behavior-based SMS yields 5× higher CVR than generic SMS. An autonomous system can choose the right channel and message for a specific customer at the precise moment, optimising for compliance and deliverability.

* Guidance Through Complex Journeys: Journey-based on-site messaging achieves 68× higher CVR than generic prompts. Agentic systems can nudge customers through multi-step financial processes, loan applications, insurance comparisons, onboarding flows with personalised assistance that reduces drop-offs.

* Elevating the Marketer’s Role: Agentic AI removes operational friction automatically generating tailored copy, building dynamic segments, and optimising flows. Marketers then operate as strategists: defining goals (e.g., “increase new account activations by 5%”) while the system handles execution and continuous improvement.

Modern, Unified MarTech

Purposeful Personalisation is not a tactical trick, it’s an architectural mandate. Institutions need a modern Customer Data & Engagement Platform (CDEP) that is native to cloud warehouses, unifies data from all sources, and serves as the single source of truth. Only then can agentic systems reliably orchestrate personalised experiences at scale.

When a CDEP is paired with autonomous AI, brands reduce campaign go-live time, improve marketing ROI, and lower the operational drag of fragmented systems. The result is faster experimentation, better compliance controls, and more meaningful customer relationships.

The Bottom Line

Benchmarks show a performance gap: brands using behavior- and journey-based tactics see uplifts ranging from 1.68× to 9.3×, while those relying on broadcasts fall further behind. For financial services, this gap isn’t theoretical; it translates directly into lost revenue, poorer customer experience, and inefficient spending.

The future of engagement will be defined by purpose and intelligence. Brands that build unified platforms and adopt agentic AI will transform marketing from a volume-driven cost centre into a high-value advisory channel. The data demands a rethink, and the time to act is now.

(Raviteja Dodda, CEO & Co founder, MoEngage.)

Views are personal, and do not represent the stand of this publication.