As the Product Director at a Wealthtech firm, I spearheaded the development of an Advisory Product Strategy to transform how relationship managers deliver personalized investment advice.

My vision was clear:

Create scalable, technology-driven solutions that empower relationship managers to efficiently manage up to 300 clients—six times their typical capacity of 50—without compromising the quality of service.

Below, I outline the strategic process I followed, the actions taken at each stage, and the output artifacts that emerged, showcasing my approach to solving complex problems in the wealth management space.

Step 1: Understanding the Problem Space

To build a transformative advisory strategy, I began by immersing myself in the problem space. I engaged with relationship managers, the Investment Committee, and clients to understand their needs and challenges. I asked critical questions:

Without quantified data, based on interviews, I started with a baseline: relationship managers relied on manual processes—spreadsheets, emails, and ad-hoc analyses—making it time-intensive to serve even 50 clients effectively. Scaling to 300 clients demanded a radical rethink.

Outcome:

I identified four core challenges:

Step 2: Defining the Product Landscape and Motivation

Next, I analyzed the broader Wealthtech landscape to position our advisory strategy. I studied consumer behavior (clients expecting personalized, digital-first experiences), technology trends (AI and machine learning disrupting financial services), and competition (traditional firms and fintech startups racing to innovate). This context reinforced the need for a tech-enabled advisory solution.

Outcome:

I crafted a placeholder mission statement to guide the roadmap:

"To empower relationship managers with intelligent tools that enable scalable, personalized advisory services, enhancing client satisfaction and operational efficiency."

Step 3: Identifying the Target Audience

I mapped the ecosystem of players:

I selected relationship managers as the primary users of the tools we’d build, given their role as the bridge between clients and advisory services.

Outcome:

Their primary motivation? To deliver high-quality, personalized advice to more clients efficiently. I considered segmenting managers by experience level or portfolio size but opted to target all, prioritizing those managing larger portfolios or open to tech adoption.

Step 4: Pinpointing Problems Along the User Journey

I broke down the relationship manager’s user journey into five stages:

Financial Advisor Journey Map

I prioritized high-level problems based on frequency and severity:

I zeroed in on “Difficulty in generating personalized recommendations” as the top priority, given its direct impact on scalability and personalization. Digging deeper, I identified sub-problems:

Step 5: Designing Solutions

With the problem defined, I brainstormed solutions to address these sub-problems, leveraging technology to drive efficiency and personalization. I evaluated each based on impact and effort:

I prioritized solutions with high impact: the Insights and Recommendation Engine and Model Portfolios, as they directly tackled personalization and efficiency at scale.

Step 6: Scoping the Minimum Viable Product (MVP)

For the Insights and Recommendation Engine, I defined an MVP to test the concept:

Road ahead

This structured approach led to the development of five key products:

These tools collectively will reduce the time relationship managers spent on analysis and recommendation generation, allowing them to scale from 50 to 300 clients.