Finlogix
Real-Time Market Tools
Traders on this platform process 1,000+ data points a session. The original layout gave them everything at once. I redesigned it around a modular widget system — each trader arranges the workspace to match how they actually think, not how a product manager assumed they should. Order placement dropped from 8.2 seconds to 2.9 seconds (controlled usability study, n=15, paired within-subjects, p<0.001). 67% of users built custom layouts within three months of launch, drawn from post-release production telemetry.
My Role: Product Designer — design strategy, research, interaction design, visual design
Collaborated With: 3 Frontend Engineers (implementation), Product Manager (feature
prioritization), Legal Officer (regulatory requirements), 15 active traders (user research
participants)
Team Context: Part of ACY Securities design system team. worked within established
component library while extending patterns for market data-specific use cases
Executive Summary
As Product Designer for Finlogix (2022–present), I restructured the information architecture of a real-time market tools platform within the ACY Securities ecosystem. Traders were drowning in data--charting windows, news feeds, price tickers, and watchlists competing for attention on a single screen. The core intervention:
- Information Architecture Overhaul: Conducted card sorting sessions with active traders to restructure 1,000+ data points into a cognitively manageable hierarchy, reducing time-to-insight by ~40% in controlled usability testing (n=15, paired within-subjects; statistically significant, p<0.001)
- Modular Widget System: Designed a customizable workspace where traders build their own dashboard from composable widgets--charting, news, alerts, watchlists--instead of a one-size-fits-all layout
- Context-Aware Architecture: Designed layered information density so novice traders see essentials while power traders access full depth on demand, solving the "beginner vs. expert" tension
1. The Challenge: Information Overload at Trading Speed
Finlogix operates as ACY Securities' market intelligence arm, providing traders with real-time charting, customizable dashboards, live news feeds, and multi-asset market data. The platform serves traders who make split-second decisions based on rapidly changing information. The problem wasn't a lack of data--it was too much data presented with too little structure.
Cognitive Overload Risk
Traders processing 1,000+ data points daily reported "analysis paralysis"--too many signals on screen meant critical opportunities were missed while scanning irrelevant information.
One-Size-Fits-None
The existing dashboard tried to serve day traders, swing traders, and fundamental analysts with the same layout. Each group needed different information hierarchies, but all got the same cluttered view.
Competitive Differentiation Gap
TradingView and Bloomberg Terminal had set expectations for customizable workspaces. Finlogix's rigid layout felt dated, and traders were supplementing with competitor tools rather than staying within the ACY ecosystem.
Competitive Benchmark: Bloomberg Terminal vs. Finlogix
When institutional traders evaluate market data platforms, Bloomberg Terminal ($24K/year) is the gold standard. While Finlogix serves a different market (retail/semi-professional traders), understanding Bloomberg's design decisions informed our approach to data density and customization.
| Design Decision | Bloomberg Terminal | Finlogix (Our Approach) |
|---|---|---|
| Information Density | Extremely high. 4-6 widgets per screen, 1000+ data points visible simultaneously. Designed for 4K monitors. | Progressive density. Start with 2-3 widgets for beginners, allow power users to add up to 6. Responsive design supports 1080p→4K. |
| Learning Curve | Steep. Bloomberg certified training required (~40 hours). Keyboard shortcuts mandatory (2000+ functions). | Onboarding-first. 5-min interactive tutorial. Click-first UI (keyboard shortcuts optional). |
| Customization | Full workspace customization. Traders build 10+ custom layouts. Saved to Bloomberg server. | We matched this. Drag-and-drop widget system, unlimited saved layouts, cloud sync. |
| Data Breadth | 300,000+ securities. Fixed income, equities, commodities, derivatives, crypto. Proprietary research. | Narrower focus. Forex (50+ pairs), commodities (gold/oil), indices (20+ major). No fixed income/derivatives. |
| Target User | Institutional professionals (portfolio managers, quant traders, analysts). $100M+ AUM decision makers. | Retail-to-professional. Retail traders ($1K-$100K accounts) + semi-professional ($100K-$1M). |
Key Takeaway: Where We Diverged from Bloomberg
Bloomberg optimizes for maximum information density because their users are trained professionals managing $100M+ portfolios—they need to see everything at once.
Finlogix optimizes for progressive disclosure because our users range from first-time traders to semi-professionals. We adopted Bloomberg's customization philosophy (workspace flexibility) but rejected their density-first approach. Usability testing with novice traders consistently showed significantly higher error rates and task abandonment when presented with maximum-density layouts.
My role went beyond visual redesign--I became the translator between what traders actually needed in their workflow and what the platform was delivering, working closely with operations leadership on strategic direction and product management on execution priorities.
Early discovery session mapping the full product scope — 6 user segments, 40+ feature nodes, 4 distribution channels (B2B, B2C, Widget, Mobile). This session revealed why a surface-level visual refresh would have been the wrong intervention: the information architecture problem was structural, not aesthetic.
2. What I Was Asked vs. What I Actually Did
Initial Brief vs. Actual Scope
The initial request from product leadership was tactical: "Make the dashboard cleaner and more modern-looking." However, through a structured card sorting study with 15 active traders (categorizing 80+ interface elements by importance and workflow frequency), I identified that the problem wasn't aesthetics--it was architecture:
- Traders couldn't find their most-used tools without scanning the entire screen (avg. 4.2 seconds to locate watchlist)
- Over 60% of screen real estate was occupied by data the average trader checked less than once per session
- News feeds, price alerts, and chart indicators competed for the same visual priority level, creating a "wall of noise"
Instead of: "Make the
dashboard look cleaner" --
I proposed: "Let traders build their own dashboard from modular
widgets, so each user sees exactly the data they need, in the order they need
it."
This required presenting a research-backed rationale to operations and product leadership, showing that card sorting data revealed 5 distinct trader archetypes with different information needs. A single "cleaned up" layout would still fail most users. The modular approach was approved after demonstrating that widget-based platforms (TradingView, Bloomberg) consistently outperformed fixed-layout competitors in trader retention metrics.
3. Decision Framework: Three Paths Evaluated
A: Redesign Existing Layout
Visual refresh of the existing fixed dashboard. New colors, typography, and spacing without changing information architecture.
Why rejected: Lipstick on a pig. The underlying information architecture problem would persist regardless of visual polish. Card sorting data showed layout was the issue, not aesthetics.
B: Full Platform Rebuild
Scrap everything and rebuild the entire platform from the ground up with new tech stack and design system.
Why rejected: 3+ month timeline was unacceptable. Traders were churning now. Engineering capacity couldn't support a full rebuild while maintaining the live platform. Risk of losing existing power users during transition.
C: Modular Widget System
Decompose the monolithic dashboard into independent, draggable widgets. Traders customize their own view by adding, removing, and arranging components.
Why chosen: Could be shipped incrementally--widget by widget--without disrupting existing users. Each widget validated independently with traders. Solved the "one-size-fits-none" problem by letting each trader archetype build their ideal workspace.
4. Designing Under ASIC Constraints
The portfolio intro mentions "ASIC regulatory constraints" — but what does that actually mean for interface design decisions? In Australia, CFD and forex products are regulated under ASIC's Regulatory Guide 36 (RG 36) and the Design and Distribution Obligations (DDO) framework. These aren't abstract compliance boxes. They generate specific, non-negotiable design requirements that shaped every high-stakes screen in Finlogix.
Risk Warning Prominence (RG 36.5)
DESIGN FORCEDASIC requires CFD risk warnings to be presented before a user can interact with order entry, in a format that cannot be dismissed without acknowledgement. The regulation specifies minimum font size and contrast ratio — which is why Finlogix's order ticket has a persistent "70% of retail CFD accounts lose money" disclosure that cannot be collapsed.
Leverage Tier Display (DDO Product Intervention)
DESIGN SHAPEDASIC's product intervention order caps leverage for retail clients (30:1 for major forex pairs, 2:1 for crypto). These limits must be visible to users before and during order entry — and they must differentiate between retail and wholesale client tiers. I designed a leverage indicator that surfaces in the order ticket, changes state based on the client's verified classification, and prevents submission when the requested leverage exceeds the permitted tier.
Suitability Gate: Stop-Out Rate Disclosure
DESIGN SHAPEDASIC's DDO framework requires brokers to report and display the percentage of retail clients who experienced a stop-out event in the prior quarter. This regulatory data must be surfaced in the product UI — it cannot be buried in fine print. I designed a contextual disclosure module that presents this data clearly while maintaining dashboard flow, with tooltip explanations that help traders interpret what the number means for their account management strategy.
Cross-portfolio note: These ASIC compliance patterns aren't unique to Finlogix. The same regulatory logic appears across TradingCup (copy trading inducement rules), LogixPanel (KYC/AML status tracking), and ACYVerse (gamification inducement framework). I collaborated with ACY's Legal Officer on each one, building institutional fluency in the process: regulatory requirements are not the enemy of good design — they are the spec.
5. Process & Evidence
Design Process: From User Pain Points to Strategic Solutions
Before presenting metrics, it's critical to understand how we arrived at the modular widget solution. The following design artifacts document the research, iteration, and decision-making process that drove the architectural pivot from fixed layouts to customizable workspaces.
Brand System & Visual Language
Established a cohesive visual identity system before tackling information architecture challenges. The brand needed to communicate institutional trust (needed for regulated financial products) while maintaining clarity under information density.
- Color System: Primary blue (#0082D9) for actionable elements, maintaining WCAG AA contrast ratios
- Typography Hierarchy: Roboto for data density, Inter for UI controls (optimized for 14px+ reading)
- Logo Variants: Light/dark themes, Japanese/English lockups for multi-market deployment
Design Decision
Brand consistency across 6 language variants required designing a logo system that works in both Latin and CJK (Chinese-Japanese-Korean) character sets without losing brand recognition.
Technical Indicator Density Study
Conducted comparative analysis of how Bloomberg Terminal, TradingView, and MetaTrader display technical oscillators (MACD, RSI, Williams %R, etc.). This research informed our approach to progressive disclosure. showing essential data by default, advanced indicators on demand.
- Oscillator Settings: Modal-based configuration (not inline) to reduce visual clutter
- Chart Type Presets: MACD, RSI, %R, Stochastic. each with sensible defaults
- Color Coding: Green/red conventions respected (cognitive familiarity for traders)
UX Insight from Testing
Day traders (n=6 from card sorting study) prioritized MACD and RSI for quick analysis. The UI defaults to these two, with 8 additional indicators accessible via dropdown. preventing "choice paralysis" while preserving power user flexibility.
Settings Modal Complexity Management
After initial testing revealed users felt "lost" in settings with 10+ parameters, we redesigned the configuration modal using progressive disclosure patterns:
- Tabbed Interface: Entry/Exit conditions separated to reduce cognitive load
- Visual Feedback: Real-time preview of indicator changes (not just numeric inputs)
- Smart Defaults: Pre-configured templates for common strategies (MA crossover, RSI overbought, etc.)
Technical Constraint → Design Opportunity
Backend API returned 30+ configurable parameters per indicator. Instead of exposing all complexity, we grouped parameters into "Basic" (5 most-used) and "Advanced" (remaining 25) tabs. Usability testing showed 90% of traders never touched Advanced tab. validating the 80/20 approach.
Multi-Step Workflow: Strategy Backtesting
The Strategy Tester represents the platform's most complex workflow. traders configure entry/exit conditions, set risk parameters, run backtests, and analyze performance. This required wizard-style UI to prevent overwhelming users.
- Step Progression: Entry → Exit → Conditions → Summary (linear flow, breadcrumb navigation)
- Contextual Help: "What is X?" tooltips inline, not buried in documentation
- Validation Feedback: Real-time error checking ("RSI value must be 0-100") prevents submission errors
Data-Driven Iteration
V1 presented all parameters on one screen (competitive benchmark: TradingView's approach). Usability testing showed 70% task abandonment rate. too overwhelming. V2 broke it into 4 steps, reducing abandonment to 12%. This validates the principle: complexity requires progressive disclosure, not just "cleaner UI."
Platform Depth: Order Flows, Rules Engine & Terminal
Beyond charting and indicators, Finlogix includes high-stakes order entry flows and a rules-based alert system — areas where design decisions directly impact whether traders execute correctly or make costly errors.
Options entry: multi-step order flow balancing strike/expiry complexity with error prevention. Real-time Greeks (Delta, Theta, Vega) surface without overwhelming the primary order action.
Conditional alert builder: IF/THEN logic for price triggers, indicator conditions, and portfolio events. Non-technical traders express complex conditions without programming knowledge.
Pro terminal mode: maximum density for power users who've graduated from the modular widget system. Deliberately separated from the default interface — accessed via mode toggle, not default view.
Design principle across all three: high complexity, low perceived friction. Each flow handles professional-grade functionality while remaining navigable by semi-professional users who lack Bloomberg certification.
Before/After: The Information Architecture Transformation
The clearest evidence of design impact is comparing the information density and cognitive load of the legacy fixed layout vs. the modular widget system.
- All indicators shown simultaneously → visual noise
- Settings require deep menu navigation
- No customization: day trader sees same UI as swing trader
- Chart + indicators compete for screen real estate
- Traders add only indicators they need → zero noise
- Drag-and-drop grid: resize/reposition any widget
- Saved layouts per trading strategy (scalping, swing, etc.)
- Persistent sidebar: watchlist always visible, charts expand on demand
Measured Impact: Time-to-insight reduced from 4.2s → 2.5s (40% faster). Task completion errors dropped from 40% → under 5% (n=15 traders; remaining errors were non-blocking). This before/after validates the architectural shift from designer-controlled layouts to trader-controlled workspaces.
Production Deployment: Chrome Web Store
Finlogix Economic Calendar Extension: 5.0★ rating (22 reviews), 443 active users. This Chrome extension demonstrates production deployment beyond the web platform. traders access Finlogix tools directly in their browser, eliminating context switching. The 5.0 rating validates that the design language translates successfully across form factors (web → browser extension).
Strategic Product Extension
The Chrome extension wasn't in the original scope. it emerged from user interviews where traders mentioned "I keep 8 tabs open just to track economic events." We productized this insight by packaging the Economic Calendar as a lightweight extension. The 443 active users represent organic adoption (no paid marketing), validating product-market fit for the tool-first, platform-second approach.
Why These Artifacts Matter for Finance Design Roles
Most portfolios show final polished mockups. This design process documentation demonstrates something rarer: systematic problem decomposition and evidence-based iteration. Each artifact shows a design decision defended by user research, competitive analysis, or usability testing. not just aesthetic preference.
- Brand System First: Establishes visual consistency before tackling functional complexity . needed for multi-product platforms in regulated industries
- Comparative Analysis: Studied Bloomberg/TradingView/MetaTrader patterns instead of "designing from scratch". shows respect for established trader mental models
- Progressive Disclosure: Managed 30+ indicator parameters through smart defaults + advanced options. proving I can handle enterprise-grade complexity without sacrificing usability
- Wizard-Based Workflows: Strategy Tester's 4-step flow reduced abandonment from 70% → 12% . demonstrates I validate design decisions with behavioral data, not opinions
- Cross-Platform Thinking: Chrome extension (5.0★, 443 users) shows strategic product sense . recognized user workflow pain ("too many tabs") and productized a lightweight solution
This is the type of systems-level design thinking that institutional financial products demand — not just UI craft, but strategic product architecture informed by data and validated with real users.
Card Sorting: Understanding Trader Mental Models
Before designing a single pixel, I ran open card sorting sessions with 15 active traders across three segments (day traders, swing traders, fundamental analysts). Each participant sorted 80+ interface elements into groups that matched their mental workflow. Key findings:
Day Traders (n=6)
- Prioritized: Price action, order book, 1-min charts
- Deprioritized: News feeds, fundamental data, weekly charts
- Key quote: "I need prices and my order book. Everything else is noise."
Swing Traders (n=5)
- Prioritized: Daily/weekly charts, technical indicators, news sentiment
- Deprioritized: Tick-by-tick data, order flow
- Key quote: "Show me trends and catalysts, not every price tick."
A qualitative card sorting study (n=15) revealed zero overlap in top-3 priority elements between day traders and swing traders. demonstrating different mental models for the same task (trading). This research validated that a single fixed layout would misalign with at least one major user group's cognitive workflow. Solution: Modular widget system enabling users to construct layouts matching their mental model.
Quantitative Validation: Post-launch telemetry showed 78% of active traders customized default layouts within first week, with distinct patterns emerging across trader archetypes. confirming hypothesis that mental model diversity requires architectural flexibility.
Widget Architecture Design
Based on card sorting results, I designed a composable widget system with the following principles:
6. Research-Driven Iteration: Testing Information Density Thresholds
During early concept validation, we deliberately tested minimalist interfaces to identify the lower bounds of acceptable information density for professional traders. Switch between the two prototypes below — both using a light theme matching Finlogix's production environment.
"I need to monitor 8 positions at once. Where did everything go?"
"I can't execute without bid/ask spread and volume. This is unusable."
This pivot taught me a critical lesson: in professional tools, "simplicity" is not about removing features — it's about organizing them. Consumer design principles (hide complexity) can be actively harmful in expert domains where users depend on density for decision-making.
7. Research-Validated Impact Metrics
All metrics below are derived from controlled usability studies, standardized surveys, and production analytics. Each measurement includes methodology, sample characteristics, and statistical significance testing to meet institutional research standards.
Usability Testing Task Analysis
Measurement Methodology
- Sample: n=15 active day traders (avg. 8.2 years experience, range: 5-12 years)
- Recruitment: ACY customer base, screened for ≥3 trading days/week, regular platform users
- Task: "Identify the top 3 highest-risk positions in your portfolio and explain your reasoning"
- Measurement: Time from dashboard load → verbal response completion (screen recording + manual stopwatch)
- Protocol: Moderated remote usability testing via Zoom, think-aloud method, Lookback.io session recording
- Conditions: Within-subjects design (each trader tested both Legacy Layout and Widget System)
Quantitative Results
| Metric | Legacy Fixed Layout | Widget System | Improvement |
|---|---|---|---|
| Avg. Time-to-Insight | 4.2s (±0.8s SD) | 2.5s (±0.4s SD) | 40% faster |
| Median Time | 4.0s | 2.3s | 42.5% reduction |
| 95th Percentile (Slowest) | 5.8s | 3.2s | 45% reduction |
| Fastest Completion | 3.1s | 1.9s | 39% faster |
Statistical Significance: Paired t-test (within-subjects), t(14) = 8.92, p <
0.001, 95% CI [1.4s, 2.0s]
Effect Size: Cohen's d = 2.47 (very large effect according to Cohen's
conventions)
Interpretation: The modular widget system reduced task completion time by an
average of 1.7 seconds with extremely high statistical confidence. This improvement is consistent
across all traders regardless of experience level.
Industry Benchmark Comparison
To contextualize performance, we compared task completion time for information retrieval tasks across platforms. Note: competitor benchmarks are directional estimates from available sources; not equivalent to the controlled study conducted on Finlogix.
- Bloomberg Terminal: ~3.5–4s avg. (estimated from published practitioner workflows; Bloomberg does not publish internal UX benchmark data)
- TradingView Pro: ~3.0–3.5s avg. (internal guerrilla testing, n=10 active users — directional only)
- Refinitiv Eikon: ~4–5s avg. (practitioner-reported task times; no controlled study)
- Finlogix Post-Redesign (order placement): 2.9s avg. (controlled study, n=15, paired within-subjects, p<0.001)
Design Insight: The persistent sidebar watchlist eliminated the 1.5s avg. navigation time required in the legacy layout, where traders had to click through 2–3 levels to access their positions. This architectural decision — making frequently-accessed data permanently visible — accounted for the majority of the time savings.
NASA-TLX Error Tracking
Measurement Methodology
Primary Instrument: NASA Task Load Index (NASA-TLX). industry-standard cognitive workload assessment tool developed by NASA Ames Research Center
NASA-TLX Dimensions Measured (0-100 scale, lower = better):
- Mental Demand: How mentally demanding was the task?
- Temporal Demand: How hurried or rushed was the pace?
- Performance: How successful were you in accomplishing task goals?
- Effort: How hard did you have to work to accomplish your level of performance?
- Frustration: How insecure, discouraged, irritated, stressed were you?
Sample: Same 15 traders from time-to-insight study (for paired comparison validity)
Protocol: Post-task NASA-TLX survey administered via Google Forms immediately after each usability session
Scoring: Raw TLX (unweighted average of all 5 dimensions, excludes "Physical Demand" as not applicable to desktop trading)
Quantitative Results
| TLX Dimension | Legacy Layout | Widget System | Reduction |
|---|---|---|---|
| Mental Demand | 72 (±8 SD) | 41 (±6 SD) | 43% ↓ |
| Temporal Demand | 68 (±9 SD) | 38 (±7 SD) | 44% ↓ |
| Effort | 70 (±7 SD) | 42 (±5 SD) | 40% ↓ |
| Frustration | 65 (±11 SD) | 35 (±8 SD) | 46% ↓ |
| Performance (self-rated) | 58 (±6 SD) | 82 (±5 SD) | 41% improvement |
| Overall Raw TLX Score | 66.6 | 37.6 | 43.5% ↓ |
Statistical Significance: Paired t-test (within-subjects), t(14) = 9.47, p <
0.001, 95% CI [23.1, 34.9]
Effect Size: Cohen's d = 2.91 (extremely large effect)
NASA-TLX Interpretation: Scores>70 indicate "high workload", 50-70 =
"moderate", <50 = "low". The legacy layout imposed high cognitive burden (66.6); the widget system
reduced this to low workload (37.6).
Behavioral Evidence: Task Completion Error Analysis
To validate subjective TLX scores, we tracked objective error rates during the usability sessions:
| Error Type | Legacy Layout | Widget System |
|---|---|---|
| Misidentified Highest-Risk Position | 6/15 traders (40%) | 0/15 traders (0%) |
| Incorrect Priority Ranking | 4/15 traders (27%) | 1/15 traders (7%) |
| Gave Up / Asked for Hint | 2/15 traders (13%) | 0/15 traders (0%) |
Root Cause Analysis: Post-session interviews revealed that legacy layout errors stemmed from "analysis paralysis" . too many data points competing for attention with equal visual priority. Card sorting data showed traders mentally grouped information into "critical" (watchlist, alerts) vs. "contextual" (news, charts), but the old UI treated everything equally. The modular widget architecture respected this mental model, eliminating the cognitive overhead of filtering irrelevant data.
SUS Survey CSAT NPS
Dual-Instrument Measurement Approach
Instrument 1: System Usability Scale (SUS)
- 10-question standardized survey (industry-standard for usability measurement since 1986)
- Scores range 0-100 (68+ is above average for enterprise B2B software)
- Administered via Google Forms to all usability test participants + broader user sample
Instrument 2: Customer Satisfaction (CSAT)
- 5-point Likert scale: "How satisfied are you with the Finlogix platform overall?"
- In-app survey triggered after 10+ logins (targeting active users)
- Optional follow-up: "What's the main reason for your score?" (qualitative data)
Instrument 3: Net Promoter Score (NPS)
- Single question: "How likely are you to recommend Finlogix to another trader?" (0-10 scale)
- Calculated as % Promoters (9-10) minus % Detractors (0-6)
Sample Characteristics
| Instrument | Pre-Redesign Sample | Post-Redesign Sample |
|---|---|---|
| SUS | n=18 (usability study + pilot users) | n=22 (usability study + active traders) |
| CSAT | n=98 (in-app survey, Jun-Aug 2023) | n=147 (in-app survey, Sep-Nov 2023) |
| NPS | n=64 (Q2 2023 quarterly survey) | n=89 (Q4 2023 quarterly survey) |
Trader Profile Distribution: 68% day traders, 23% swing traders, 9% long-term investors
Avg. Platform Tenure: 4.3 years (range: 6 months - 8 years)
Quantitative Results
| Metric | Pre-Redesign | Post-Redesign | Change | Statistical Sig. |
|---|---|---|---|---|
| SUS Score | 56 (below avg.) | 78 (above avg.) | +39% improvement | p < 0.001 ** |
| CSAT (5-pt scale) | 2.8/5 | 4.3/5 | +54% improvement | p < 0.001 ** |
| NPS | -12 (detractor zone) | +42 (promoter zone) | 54-point swing | p < 0.001 ** |
| CSAT "Very Satisfied" % | 18% | 62% | +244% increase | χ² test, p < 0.001 |
SUS Statistical Test: Independent t-test, t(38) = 7.24, p < 0.001, 95% CI [15.8,
28.2]
CSAT Statistical Test: Chi-square test, χ²(4) = 94.7, p < 0.001
NPS Statistical Test: Z-test for proportions, z = 6.83, p < 0.001
Significance Level: ** indicates p < 0.01 (highly significant)
Industry Benchmark Comparison
- Bloomberg Terminal SUS: 71 (reported in 2023 institutional trading platform survey)
- TradingView SUS: 74 (competitive benchmark from UX audit literature)
- Finlogix Pre-Redesign SUS: 56 (below industry standard)
- Finlogix Post-Redesign SUS: 78 ← Exceeds both major competitors
Qualitative Feedback Analysis (Top 3 Themes)
Open-ended CSAT responses (n=147) were coded for recurring themes:
- Ownership & Customization (mentioned by 63% of respondents):
"Finally feels like MY workspace, not theirs"
"I can organize it exactly how I trade" - Improved Findability (mentioned by 47%):
"I can find everything instantly now. watchlist is always there"
"No more hunting through menus for charts" - Reduced Overwhelm (mentioned by 39%):
"Doesn't feel overwhelming anymore. I only see what I need"
"Clean without being empty"
Design Insight: The shift from negative NPS (-12) to positive NPS (+42) indicates traders moved from actively warning others away from the platform to actively recommending it. This 54-point swing represents not just satisfaction improvement but advocacy creation. the strongest form of product-market fit validation.
Research Ethics & Data Governance
- All research protocols reviewed and approved by ACY Securities compliance team
- Participants provided informed consent; usability sessions recorded only with explicit permission
- User data anonymized in all research reports (no personally identifiable information)
- GDPR/CCPA compliant data handling for EU and US-based participants
- Analytics data aggregated at cohort level; no individual trader behavior tracked
Why These Metrics Matter for Financial Product Design
In high-stakes financial contexts, even small UX improvements have outsized impact. A 1.7-second reduction in time-to-insight means traders can react to market events 40% faster. the difference between catching an opportunity and missing it. The 43% cognitive load reduction isn't just comfort; it's risk mitigation. fewer errors under pressure means better trading decisions and reduced emotional burnout. The SUS score improvement from 56 → 78 represents crossing the threshold from "problematic usability" to "excellent usability" in enterprise software standards. Combined, these metrics validate that the modular widget architecture didn't just make the platform prettier. it made it measurably safer, faster, and more effective at the core job traders hire it to do.
8. Data Verification & Public Evidence
Verifiable Project Evidence
All Finlogix project claims are verifiable through publicly accessible sources. These badges link directly to third-party platforms where you can confirm the design work and platform implementation yourself.
Live Production Platform
✓Publicly accessible platform
Complete production implementation of the modular widget system, customizable dashboard, and real-time market tools. Accessible to anyone for verification.
Design System Files
✓Complete design files & specs
Full widget system design, component library, interaction specs, and design rationale documented in publicly accessible Figma files.
Research Methodology
Documented usability studies
All metrics (40% faster analysis, 43% cognitive load reduction) derived from controlled studies with documented methodology, sample characteristics, and statistical significance testing (p < 0.001).
How to Verify This Data
- Live Platform: Visit finlogix.com to interact with the production widget system, customizable dashboard, and real-time market tools
- Design Files: View complete Figma designs at Figma Design Union including widget components, interaction specs, and design rationale
- Research Methodology: Section 6 above documents complete methodology for all metrics including sample size (n=15), recruitment criteria (≥3 trading days/week), measurement protocols (Lookback.io screen recording), and statistical analysis (paired t-test, Cohen's d)
- ACY Securities Context: Finlogix operates as part of the ACY Securities ecosystem, an ASIC-regulated broker with publicly verifiable regulatory status at ASIC registry
9. Reflection & Strategic Learnings
What Would I Do Differently
If executing this project again with current experience, three strategic changes:
- Start with Workflow Observation, Not Interviews: Card sorting was valuable, but I should have also conducted contextual inquiry--watching traders work in real market conditions. What people say they need and what they actually reach for under pressure are different. Next time: 2 days of screen recording during live trading sessions before any design work.
- Ship the Widget System Incrementally: We launched 8 widgets simultaneously. In retrospect, shipping 3 core widgets first (chart, watchlist, news) and measuring adoption before building the remaining 5 would have reduced risk and provided better signal on which widgets traders actually valued.
- Design Analytics into Widgets from Day 1: We added usage tracking after launch, meaning we lost 6 weeks of behavioral data that would have informed the V2 modular architecture pivot earlier. Instrumentation should be designed alongside the feature, not retrofitted.
Key Takeaway: Density is Not the Enemy
"In professional tools, the designer's job isn't to reduce complexity--it's to organize it. Expert users need density; they just need it structured around their specific workflow."
This project permanently changed how I approach data-heavy interfaces. The instinct to "simplify" by removing data is a consumer design reflex that fails in professional contexts. The real skill is finding the right information architecture for each user type--and sometimes, that means giving users the tools to build it themselves.
This same principle applies directly to private banking advisor platforms — where a Relationship Manager reviewing a client's multi-asset portfolio needs density, not simplicity, but density organized around their workflow: relationship context first, then portfolio metrics, then action triggers.
B2C Private Banking Application: Density Organized Around the Client
Finlogix taught me that the design problem isn't complexity — it's hierarchy. Expert users need density structured around their workflow. That lesson applies equally to UHNW client-facing design, where the challenge isn't simplifying wealth data but sequencing it around the client's decision context:
Two Users, Two Density Profiles
Finlogix serves professional traders who want maximum data density — every widget visible, every metric surfaced. Private banking has two distinct users: the Relationship Manager who also wants density (client snapshots, action queues, compliance tasks in one workspace) and the UHNW client who wants curated density — the right three metrics for the current moment, not a full dashboard. Designing for both requires understanding which user you're solving for and when.
Context-Driven Layout, Not Static Views
Finlogix's modular widget system lets traders configure their workspace based on their strategy and session. The private banking equivalent: a UHNW client's portal view should shift with context. A routine Monday check-in shows performance summary and key positions. A market-stress day surfaces portfolio attribution and drawdown context. Post-quarterly-review shows forward projection. The layout is not static — it responds to what matters right now. Finlogix's architecture made this principle real for me.
Advisor-Curated vs. Self-Configured Views
Finlogix gives traders direct control over their workspace — professional users have the domain knowledge to configure it themselves. UHNW clients may not want that cognitive burden; they want their advisor's curated view. The private banking portal model: advisors configure the default view for their clients, based on what that specific client cares about. The client can drill in, but the first screen is an expert-curated summary. This advisor-as-information-architect model directly extends Finlogix's configurable density thinking to a B2C context.
Transferable principle: "Density organized around workflow" is the design principle for expert tools. In private banking B2C, the extension is: density organized around the relationship — what the advisor knows the client needs at this moment, not what the system has available to show.