UX Design and Research for Institutional Trading: Enhancing the Liquidnet Experience

Project Type

Bank Portal Re-Design

Contribution

User Research

User Experience (UX)

Prototyping

My role

UX-UI Lead

Outcome

Rolled out 100% USA / UK 🎉

Target users

+ 160K Active Traders

Visit Website

OVERVIEW

In this phase, we deepened our understanding of both our users—professional traders—and the domain of dark-pool institutional trading, Liquidnet’s area of expertise. Our focus was on exploring how traders interact with Liquidnet 5, their core trading platform. Leveraging insights from earlier research, I transitioned from broad ideation to focused concept development, conducting iterative testing and refining designs based on user feedback. This phase blended exploratory design, case studies, and user testing to produce a comprehensive set of high-value alert system prototypes tailored to the needs of institutional traders.

PROBLEM

The challenge was to design an alert system with the potential to evolve into a personalized trading platform. This required rapidly immersing ourselves in the financial domain to understand institutional traders and Liquidnet’s product ecosystem. My goal was to synthesize research insights to identify meaningful design opportunities that would enhance the trading experience while aligning with Liquidnet’s strategic objectives.

RESEARCH METHODS

Given the specialized nature of dark-pool trading and the limited number of traders using Liquidnet, Me and my team conducted generative research to better understand this niche user group. I engaged with both actual dark-pool traders and proxy users who perform similar, trading-related tasks. These sessions provided valuable insights into the mindset, behaviors, and decision-making processes of traders, helping us build a clearer picture of their work environment and needs.

INTERVIEW

Observation : A Trader's Day and Peer Dynamics

- Initial research helped us develop a profile of a hypothetical trader, offering insights into daily routines and decision-making processes.

- Interviews revealed that trading teams vary greatly in size and roles, highlighting the lack of standardization within the industry.

- Understanding these diverse team structures and trader types is crucial for designing tailored solutions that address the unique needs of different traders.

STAKEHOLDER MAP

Insights

- Traders rely on in-house advisors to develop and refine their trading strategies.

- External services and brokers offer unique insights that complement internal expertise, enhancing traders’ decision-making capabilities.

- This valuable information helps traders optimize and adapt their strategies in a highly competitive market.

Overlapping Roles

- Liquidnet functions both as a broker and an external analytics platform, offering comprehensive solutions to traders.

- Relationship managers play a key role in fostering loyalty and maintaining trust with traders through personalized support and engagement.

- The platform’s proprietary data is a major factor in encouraging traders to continue using Liquidnet, reinforcing the value of the product.

CUSTOMER JOURNEY MAP

Repetitive Trading Actions

- Pre-trade decisions are typically guided by client objectives or portfolio strategies.

- Most trades remain unmonitored unless an anomaly arises.

- When issues occur, they are addressed collaboratively by the trading team, reinforcing a shared problem-solving approach.

Emotional Patterns Throughout the Trading Day

- The market open and close are the most dynamic parts of the day—both exciting and stressful.

- Mid-day tends to be quieter, focused on routine updates to earlier trades.

- This emotional rhythm shapes how traders engage with their tools and manage focus throughout the day.

SCREEN ALLOCATION

Optimized Screen Real Estate

- Traders typically operate with 2 to 6 monitors, each actively used throughout the trading day.

- Screens are often dedicated to specific applications, such as market data, trading platforms, or communication tools, to streamline workflows.

- The Liquidnet front-end sees limited engagement, as it’s not central to most traders’ core tasks during the day.

Front-End Viewed as a Disruption

- Liquidnet’s interface mainly appears through pop-up windows designed for alerts or urgent actions.

- These pop-ups, while functional, are often perceived as intrusive within the trader's multitasking environment.

- Consequently, traders may view the front-end experience as a disruption rather than a seamless part of their workflow.

AFFINITY DIAGRAM

Optimized Screen Real Estate

- Traders typically operate with 2 to 6 monitors, each actively used throughout the trading day.

- Screens are often dedicated to specific applications, such as market data, trading platforms, or communication tools, to streamline workflows.

- The Liquidnet front-end sees limited engagement, as it’s not central to most traders’ core tasks during the day.

Front-End Viewed as a Disruption

- Liquidnet’s interface mainly appears through pop-up windows designed for alerts or urgent actions.

- These pop-ups, while functional, are often perceived as intrusive within the trader's multitasking environment.

- Consequently, traders may view the front-end experience as a disruption rather than a seamless part of their workflow.

Competitive Analysis

Advantages

- Liquidnet benefits from a historical reputation as the first dark-pool trading platform, earning long-term trust from institutional traders.

- While competitors like BIDS and ITG offer more affordable options, they lack the depth of data provided by Liquidnet.

- Liquidnet continues to lead in liquidity access, a key differentiator that keeps high-volume traders engaged with the platform.

Disadvantages

- Competitors offer lower commissions, making their platforms more attractive for cost-sensitive trades.

- Liquidnet’s premium pricing positions it as a high-cost solution, which may deter newer or budget-conscious clients.

- Rivals have made strong advancements in algorithmic trading and fast-match execution, areas where Liquidnet is perceived to lag behind.

Sequence Modeling

Mapping Trader Workflows

- We conducted a detailed mapping of each workflow sequence, capturing the actions traders take throughout their day.

- These workflows are often interconnected, with processes overlapping or feeding into one another.

- For example, the passive match flow often contains elements of the active match flow, illustrating the complexity of decision-making in trading.

Recognizing Workflow Patterns

- Our analysis revealed that many workflows leverage the same platform features, though used in slightly different contexts.

- Mapping these patterns helped us identify which workflows are most frequently used and hold the greatest strategic importance.

- Active and passive trade workflows emerged as the most consistent and routine, forming the backbone of the trader experience.

Heuristic Evaluation

Industry Standards in Fintech

- Fintech platforms commonly emulate spreadsheet-like interfaces, favoring data density and grid-based layouts.

- The use of bright red, green, and yellow for indicators is widespread and serves as a quick reference for trade status or alerts.

- Notably, design conventions have remained largely unchanged over the past decade, signaling a stagnation in user experience innovation.

Design Standards and Liquidnet

- Liquidnet currently falls short of several visual heuristics, leading to a steeper learning curve for users.

- Improved color usage and iconography can enhance clarity and make the interface more intuitive.

- A strong visual hierarchy is essential to guide traders smoothly through complex workflows, reducing cognitive load and improving efficiency.

Service BluePrint

Overall User Trends

- Our research identified six distinct user types, each with unique behaviors and preferences.

- A key differentiator among them was the level of algorithmic trading (algo) usage, influencing how they interact with tools and data.

- Despite differences, all user types shared a common structure in their day, which could be divided into three main phases: open, mid-day, and close.

A Day in the Life of a Trader

- The phases of the trading day directly correlate with specific actions and decision-making needs.

- The market open and close emerged as the most intense and stressful periods, requiring high focus and speed.

- Mid-day activities are more variable and consist primarily of monitoring, though the nature of this monitoring differs by user type based on trading style and strategy.

Walking Wall

What we did

We gathered our product model, stakeholder map, affinity diagrams, and user interview notes together and posted them on a board to look through all at once. Then, we took some time to create new insights and re-emphasized the most important user needs.

What we learned

- Quality of Professional Network: I want my leads to come from an exclusive source.

- Data Analytics & Visualization: I need to be able to customize my work station.

- Workflow: To do research, I need to be on multiple platforms which is taxing.

20 Questions

What I did

I crafted a series of focused yet open-ended design questions based on insights gathered from our "walk the wall" research exercise. These questions aimed to directly address identified user needs while providing a foundation for scalable design solutions.

Sample Design Questions

- How might we help traders stay mentally alert to sustain peak productivity?

- How might we encourage routine flexibility without compromising trading efficiency?

- How can we create a visually intuitive interface that still feels familiar to seasoned traders?

- How can we support traders in managing work-life balance and reducing the risk of burnout?

Value Preposition

What I did

To address our key problem areas and user needs, we developed 15 "How Might We" (HMW) statements that captured the core challenges faced by traders. These were mapped onto a value-feasibility matrix to evaluate the impact of each idea versus Liquidnet’s capability to implement them. From this exercise, we prioritized five high-potential questions that guided our design direction.

Sample Design Questions

- How might we encourage traders to actively contribute to product improvement?

- How might we help traders stay mentally sharp and productive throughout the day?

- How might we intelligently automate analytics to improve data visualization?

- How might we help traders quickly re-engage after a break or hiatus?

- How might we prevent traders from spiraling when trades go against them?

Crazy Eights

What I did

We conducted a fast-paced ideation session where each team member had 8 minutes to sketch 8 ideas. This exercise encouraged both imaginative thinking and practical solutions.

Idea Categorization

- Closure and Reports – tools that help summarize and close out the trading day

- Group Feedback – mechanisms for team insight sharing and collaborative learning

- Motivation through Animations and Sound – playful enhancements to keep traders engaged and alert

Kano Analysis

What I did

We integrated Kano model questions into our testing to assess user satisfaction with and without our designs. These questions helped us distinguish between must-have features and nice-to-have enhancements. We then charted the desirability of features to understand their relative importance.

Idea Categorization

- Sound features emerged as a satisfier, enhancing the user experience when included.

- Grouped alerts were identified as a satisfier as well, making them more valuable to prioritize for further development.

- A virtual assistant was viewed as a delighter — users enjoyed the concept, but it wasn't deemed essential for the core product.

Feedback Grid

What I did

I created a four-quadrant grid for each research question, organizing the insights into the following categories:

Top quadrants: Features users liked and constructive criticism

Bottom quadrants: New ideas and open-ended questions for further exploration

What I Learned

- Pulling information emerged as a key interaction for traders, emphasizing the importance of easy data retrieval.

- Traders are generally averse to new features that come with a steep learning curve, preferring to stick with familiar tools.

DESIGN

Previously, sound classifications used red-green backgrounds to indicate 'good' or 'bad' status, which created confusion. To address this, we made the design more neutral, eliminating the color bias. Additionally, we expanded the options, incorporating the feedback from testing to provide more clarity and flexibility.

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