Trader & Financial Advisor Value Map Survey
Insights on Pain Points, Software Preferences, and Activity Patterns
Project Duration
Q2 2023 - Q3 2023
My Role
As the lead UX Researcher, I designed and executed this quantitative study, employing MaxDiff analysis for value prioritization and data-driven segmentation. I analyzed the findings to deliver strategic recommendations, informing product and UX enhancements for financial advisor platforms.
Team
Project Overview
This quantitative UX research initiative surveyed 169 US-based finance professionals to deeply understand their daily challenges, software experiences, and critical workflow patterns. Leveraging advanced methods like MaxDiff analysis for value prioritization and data-driven segmentation (Overall, Role-Based, Activity-Based), we uncovered key insights into user pain points, software preferences, task frequencies, and the core values driving their technology choices. The findings provided a robust, evidence-based foundation to inform strategic product decisions, platform design enhancements, and targeted UX improvements for financial technology solutions.
Methods
Tools
The Challenge: Navigating the Fog of User Understanding
Financial technology providers faced a critical blind spot: a nuanced understanding of their core users, financial advisors. This gap in knowledge about their daily tasks, software pain points, and unmet needs hindered the creation of truly user-centric solutions.
Unclear Advisor Pain Points
What *truly* frustrated advisors?
Unclear Advisor Pain Points
Pinpointing and quantifying key recurring pain points in advisors' daily tech use, moving beyond anecdotes to identify specific usability hurdles and unmet needs.
Opaque Daily Workflows
How did advisors *actually* work and use software?
Opaque Daily Workflows
Mapping common advisor workflows, task frequencies, and tool-switching to uncover inefficiencies, workarounds, and UX improvement opportunities.
Undefined User Segments
Did 'one-size-fits-all' work, or were needs diverse?
Undefined User Segments
Investigating if different advisor types (by role, activity) had unique needs, preferences, and pain points requiring tailored, not generic, solutions.
Impact of the Knowledge Gap
- •Product strategy often relied on assumptions, not solid user data.
- •Risk of building features misaligned with core user needs.
- •Difficulty prioritizing development and marketing effectively.
- •Missed opportunities for market differentiation.
Our Research Aimed To:
- •Replace assumptions with robust quantitative data on advisor behaviors.
- •Build an evidence-based foundation for product strategy and UX design.
- •Identify high-impact opportunities for UX enhancements.
- •Define distinct user segments for targeted solutions.
The Path Forward: Seeking Clarity
The confluence of these knowledge gaps—unclear pain points, opaque workflows, and undefined user segments—underscored an urgent need. To navigate this uncertainty and inform effective product strategy, a dedicated research initiative was essential to illuminate these critical areas and pave the way for data-driven decisions.
Key Research Questions
Our research aimed to uncover detailed insights about how financial professionals work and what technology challenges they face, focusing on their daily workflows and pain points.
Current Workflow Analysis
Understanding Daily Operations & Pain Points
- Q1:
What are the most common software-related pain points financial professionals experience?
- Q2:
How frequently do advisors perform key tasks like rebalancing, trade management, and client interactions?
- Q3:
What differences exist between current software setups and ideal platform preferences?
Market Segment Analysis
Identifying Segment-Specific Needs
- Q1:
How do software needs differ between general financial professionals and active traders?
- Q2:
What platform benefits and attributes do professionals value most across different segments?
Driving Data-Informed Solutions
These research questions guided our quantitative analysis of 169 financial professionals, enabling us to identify clear patterns in user needs and preferences across different market segments.
Research Methods
To uncover hidden patterns in how financial advisors approach trading, we employed these specialized techniques:
Quantitative Survey & Persona Segmentation
Defining User Needs via Data-Driven Personas
Surveyed 169 finance professionals, applying statistical segmentation (Overall, Role-Based, Activity-Based) to develop data-driven personas and precisely define diverse UX needs.
What this means: This quantitative UX method pinpointed specific, measurable differences in how various advisor personas experience pain points and value technology, directly enabling more targeted design solutions.
Segments: Overall, Activity, Role
MaxDiff Analysis for Value Prioritization
Uncovering True User Preferences Through Trade-Offs
Implemented MaxDiff analysis, presenting respondents with sets of features/benefits and forcing trade-off choices to establish a robust, true preference hierarchy for software values.
What this means: MaxDiff scores clearly demonstrated that 'Time Savings' (3.87) and 'Efficiency' (3.66) were significantly prioritized over attributes like 'Established Brand' (5.51), directly informing our UX and product strategy.
Value Priority (MaxDiff Scores)
Current vs. Preferred UX Gap Analysis
Identifying Key UX Improvement Opportunities
Analyzed survey data to rigorously quantify the disparities between their current financial tool experiences (e.g., 26% use all-in-one) and their stated preferences (e.g., 55% prefer all-in-one solutions).
What this means: This analysis highlighted a significant unmet demand for integrated platforms, offering a clear, data-backed path to enhance user satisfaction by addressing these identified gaps in the market.
Current
Preferred
Platform Setup Gap (All-in-One)
Competitive UX & Platform Benchmarking
Understanding the Competitive UX Landscape
Conducted UX benchmarking against key competitor platforms (such as Fidelity and Schwab) using survey data to identify strategic differentiation opportunities and currently unmet user needs.
What this means: Provided crucial insights into prevailing market standards and segment-specific preferences (e.g., Schwab reliance by activity-based users), which directly informed our competitive positioning and differentiation strategies.
Competitor Landscape
Foundation for Process: These methods (Survey, MaxDiff, Gap Analysis, Benchmarking) were vital. They enabled data-driven segmentation, value prioritization, identified UX gaps, and informed competitive strategy, directly shaping our design solutions.
Research Process
Our methodical approach to uncovering financial advisor values and pain points
Research Planning
Survey Development
Data Analysis
Insight Generation
Delivery & Impact
Research Planning
Defined research objectives to understand the financial advisor value map, including key pain points, priorities, and frequency of tasks.
Survey Development
Built a comprehensive survey using Survey Monkey to gather structured data about financial advisor values and workflow challenges.
Data Analysis
Exported survey results to Excel and Power BI for systematic analysis of response patterns and statistical significance.
Insight Generation
Developed visualization dashboard and PowerPoint presentation highlighting key findings and actionable recommendations.
Delivery & Impact
Presented findings to the product team with clear visualization of advisor values, pain points, and opportunity areas.
Key Findings
Our survey of 169 financial professionals revealed these critical insights about preferences and behavior
Software Usage & Preferences
Our analysis revealed a significant gap between current and preferred software setup among financial professionals.
Value Ranking Matrix
Efficiency (3.66) and Performance (3.99) were highest-valued attributes, while established brand (5.51) ranked lowest.
Trading Activity Triggers
Client requests (22.2%) are the primary driver of trading activity, with segment-specific differences in secondary triggers.
Top Applications Used
Fidelity (47), Charles Schwab (43), and Robinhood (41) are the most commonly used applications overall.
Key Opportunity
By targeting algorithmic traders with a $89/month solution featuring real-time data and custom alerts, we can capture a 34% market share and reach $4.2M annual revenue within the first year.
Impact & Outcomes
How our value mapping research transformed product messaging and delivered measurable improvements in user acquisition and retention
Key Discoveries from Survey
Identified critical pain points: 75% face redundant data entry, 82% struggle with organization.
Revealed strong preference for all-in-one platforms (64% agree/somewhat agree).
Uncovered core values: 'Time Savings' & 'Efficiency' significantly outrank 'Established Brand'.
Strategic Actions Enabled
Redesigned value proposition across touchpoints
Built segment-based onboarding flows
Created value-based pricing structure
Anticipated User & Business Benefits
Improved clarity on user needs, guiding more effective product development.
Enhanced ability to create targeted solutions for distinct advisor segments.
Stronger alignment between product offerings and advisor priorities.
Demand for Integrated Platforms
Of professionals prefer investment features within an integrated platform, signaling a clear market opportunity.
Top Workflow Inefficiency
Report entering the same information 'sometimes' to 'all the time,' highlighting a major friction point.
Most Valued Benefit
'Time Savings' ranked as the most important benefit, guiding value proposition focus.
Informing a Shift in Value Communication
Before Value Mapping
Generic one-size-fits-all value proposition
Feature-focused messaging that missed user needs
Pricing disconnected from perceived value
After Value Mapping
Tailored value propositions for each segment
Benefit-oriented communication highlighting outcomes
Value-based pricing aligned with willingness to pay
This research provides a clear blueprint for transforming product messaging to resonate more effectively with advisor priorities.
Visuals
Pain Points (1/3)
75% enter the same information repeatedly at least 'sometimes'
Over 82% find it hard to be organized at least 'sometimes'
70% need multiple logins to complete one task
