In this phase, we collected information about our users and "dark-pool institutional trading", which Liquidnet specializes in. Our work focused on understanding the trader (our user) and their technology (the Liquidnet 5 product).

We needed to quickly onboard ourselves in the financial world to study traders and Liquidnet's product.


Our team had to understand traders well enough to design for them according to our project brief.

Research Journey
The team began learning about the average trader's lifestyle and technology usage. Once we had a better understanding of traders, we studied Liquidnet's software, Liquidnet 5. Finally, we examined the trader's current usage of Liquidnet 5 features.
Users + Product
Many trader types emerged as we studied trader's priorities and metrics for success, with varying Liquidnet usage.
We examined Liquidnet 5 to understand its current use. Different styles of usage emerged across user types.
User + Product
The user types found earlier matched different interactions with Liquidnet's front-end and back-end software.
Traders are core to Liquidnet's success - they execute trades, they analyze research, and they develop algorithms, all benefitting Liquidnet. Understanding trader’s goals and needs tells us where Liquidnet should target traders. We'll walk through the research we did to understand the different stakeholders in dark pool trading.
Jeevan the Equity Trader
As an equity trader Jeevan's goals are to improve his performance in his trades, and to create innovative trading strategies.
Jeevan needs to be confident in his trades even if they fail, and needs to grasp high-quality data and algorithms fit his trading strategy.
Jed the Research Analyst
Jed wants to provide useful insights on 'good' trading decisions, thus becoming a trusted resource for traders.
Jed needs to be aware of market direction, and must establish a rapport with traders so that his research is taken seriously.
Jane the Portfolio Manager
Jane wants to achieve 'good' portfolio performance with low risk overall, as well as avoid information leakage at all costs.
Jane needs to be confident in her opinions, even if her team disagrees. She must also understand clients’ need to build portfolios.
How did we study the user?
Only a few niche traders use Liquidnet or take part in dark-pool trading. As such, our team used generative research to understand the elusive dark-pool traders and proxy users that experience trading-esque work. Through these activities, we learned how traders think and act on the job.
After gathering a considerable amount of generative data on traders, our team synthesized findings by using the following techniques. These maps and graphs helped us collect insights and communicate our research findings with ease to our clients.
With a better understanding of traders' workflow, our team studied the Liquidnet software. We learned about the target customers and the features marketed towards them. Then, we compared the target to the actual users of Liquidnet. Below are some of the generalized findings about the product.
An entire company or a single trader may purchase Liquidnet for their trading teams. When interacting wtih the customer, Liquidnet must keep the customer’s portfolio in mind.
Liquidnet has acquired analytics technology to build upon its order management and execution software. They want their customers to do more than execute trades on Liquidnet.
Traders often avoid Liquidnet's front-end product and features. They want Liquidnet's features to be easier to find and use, and will avoid features with ambiguous value to them.
How did we study the product?
Parallel to our generative research on users, our team gathered preliminary research about Liquidnet and their competitor's offerings. This involved a competitive analysis, as well as studying Liquidnet's sequence of operations and adherence to design heuristics.
Through the methods just shown and from our onsite interviews, our team found the following insights. Overall, many traders have cognitive and emotional overload from their job and their software. Traders avoid new features and experience difficult UI interactions on a daily basis.
Users + Product
After user and product research, we understood where Liquidnet stood with its users. The trader's needs that were not met and the usability issues in Liquidnet 5 showed our client where they could improve.
Different workflows
Liquidnet's users have vastly different workflows and trading styles. The product should be tailored to these user types instead of forcing all users to trade the same way.
Data and information is at the heart of trading. Traders want proprietary information from Liquidnet to decide if they or their team should trade on Liquidnet or at all.
Liquidnet 5 has many hidden features that do not match current design heuristics or usability standards. This makes their product and new features less friendly to traders.
How do users currently interact with the product?
To collect our user and product related findings in one location, we developed a service blueprint. Each swimlane designated a different user-type, all of which use Liquidnet through unique workflows. Our clients reviewed and confirmed our understanding of the design space.
After meeting with our clients, our team narrowed down on our problem scope to focus on developing alert design guidelines and prototypes. We began to exploring how to turn our insights into the backbone of our design to ensure the user was central to our work at all times.
Overall, this phase of research was able to turn our team from financial novices to traders-in-training. We deeply understood both the trader's needs and our client's goals in designing for the next generation of institutional traders. Our charge was now to utilize our research insights in our design phase to develop front-end style prototypes.

Our team narrowed our project focus based on our client's feedback and the research gathered. Future phases will focus on designing for Liquidnet's front-end and alerts system.


Based on the data provided by our clients and interviews onsite, we discovered that front-end interactions and Liquidnet's new 'alert stream' may be the best avenue to design for.

Next step in our journey:
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