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Meaning,
Not Data
Not Data
Market updates arrive constantly, but they don’t arrive in a form that’s instantly usable in a client conversation. RMs often need to reframe fragmented information, decide what matters for each client, and translate it into a message that feels timely and trustworthy. In this gap, speed fights clarity: moving fast risks being generic; being precise costs time. We set out to design a lightweight sensemaking layer that turns information delivery into meaning-making without adding extra admin load.
Problem Statement
Information flows, but meaning gets lost.
Hypothesis
If updates are structured into context–change–meaning–action, prep time drops and messaging becomes more consistent and confident.
Research Goal
Identify bottlenecks in how RMs translate updates into client communication, then define information structure and interface principles to reduce them.
Project Overview
Research Categories
Exploratory, Evaluative
Project Type
Information Architecture, Workflow Design, Sensemaking Tool
Timeline
10 Weeks
Contribution
Research planning, Interviews, Synthesis, IA, Prototyping, UX Writing
Method
Stakeholder Interviews, Journey/Workflow Mapping, Affinity mapping, Concept framing, Usability testing
Deliverables
Insight Report, Problem Framing, Information Model, Design Principles, Prototype, Testing Notes
Constraint
Anonymized Public Case, Limited User Access, Timeboxed Schedule
Key Insight
Signals that shaped our direction
A quick snapshot of the strongest patterns I validated across research and synthesis.
65%
Of HNWIs say wealth advice feels insufficiently personalised.
43%
RMs spend only 43% of their time on direct client interaction.
15%
A key constraint is the limited time RMs have for client-facing work.
30 seconds
The concept aims to help RMs grasp the full client flow within 30 seconds before a meeting
Personalisation Gap
Clients want advice that feels tailored, yet updates arrive as raw signals. RMs end up doing the translation work under pressure, and the message quality depends on who has time that day.
Sensemaking Bottleneck
The issue isn’t access—it’s turning information into a clear “so-what” with proof. When takeaways and rationale are split, confidence drops and prep time grows.
Integrated Narrative Wins
Strong RMs link hard facts with behavioural context and client cues. When those pieces stay connected, the story is clearer and action becomes easier.
Research Impact
User Impact · Strategic Impact · Business Impact
We translated research insights into decisions that improve user clarity, align with strategy, and enable business outcomes.
User Impact
- RMs spend less time turning fragmented updates into a usable narrative.
- Faster read of a client’s current state (context + behaviour + sentiment).
- Market updates become easier to explain as what it means for this client, not generic info.
Clarity ↑
Relevance ↑
Prep time ↓
Strategic Impact
- Shifts communication from information delivery to meaning-making.
- Makes the interpretive layer explicit, linking signals to client context and action.
- Creates a shared structure teams can align on when preparing messaging.
Meaning > Info
Interpretive layer
Alignment ↑
Business Impact
- Supports more consistent advisory quality, less dependent on individual style.
- Improves timing and relevance of outreach by making context faster to recall.
- Builds a more differentiated, context-led client experience over time.
Consistency ↑
Trust signals ↑
Differentiation ↑
Methodologies
What I did — and why I chose it
Key methods used in the project, why they were chosen, and what they produced.
01
Stakeholder Consultations
Output
Workflow Notes · Pain Points · Decision Moments
02
Secondary Research
Output
Workflow Realities · Pain points · Job-to-be-done Themes
03
Scenario Breakdown
Output
Scenario Map · Breakdown of Moments · Prioritised Requirements
04
Signal + Data Input Mapping
Output
Signal Taxonomy · Input Model · Timeline Rules
05
Concept Prototyping + Iteration
Output
UI Prototype Set · Iteration Notes · Refined Concept Scope
Evidence
Secondary Research
Signal + Data Input Mapping
Concept Prototyping (Figma)
Pitch Deck
Reflection
What I learned and how we’ll apply it next
A short wrap-up on decisions, trade-offs, and what we’d improve in the next iteration.
Key reflections
What worked well
We aligned fast on the core job: turn a generic market update into a client-ready read of “what matters now.” Keeping one timeline as the backbone helped us make hard data, soft signals, and sentiment sit together without losing the thread.
Alignment
Narrative
Clarity
Trade-offs we accepted
We chose a strong default over endless customisation. Instead of trying to model every client nuance, we focused on repeatable structure: signal → meaning → next action, with optional depth when needed.
Default
Scope
Structure
What we’d do differently
We’d test earlier with two very different users: experienced advisors and newer advisors. Short, timed tasks would help us catch where wording and ordering break, especially around “so what do I say next?”
Validation
Wording
Sequencing
Open questions
Some parts depend on governance: who owns each signal, what can be stored, and what must stay ephemeral. We still need clearer boundaries for data sensitivity, auditability, and handover.
Metadata
Ownership
Governance
How we’d validate success
In a next round, we’d validate three things: can someone summarise the client’s current state quickly, can they explain “why this matters” without re-reading the whole update, and can they draft a next-touchpoint message with fewer back-and-forth steps.
Signals
Findability
Handover
Next steps
Tighten the timeline logic, lock the minimum set of signals, and prototype two core flows: market update intake and client-ready recap. Then run quick walkthroughs and iterate wording and hierarchy.
IA
Prototype
Iteration