ClearGlass
ClearGlass Analytics is an independent data analytics company that enables asset owners, investment advisors, and asset managers to evaluate the Value for Money (VfM) of their investments through transparent cost and performance data.
As part of the product team, I contributed to the development of a SaaS platform designed to serve all sides of the asset management market, delivering structured cost transparency and advanced analytics across the UK and European investment ecosystem. The platform integrates industry frameworks such as the Cost Transparency Initiative (CTI) and the Cost Transparency Standard (CTS), enabling consistent data collection, benchmarking, and more informed investment decision-making.
Designing for this product presented unique challenges due to the highly cyclical nature of the industry. The platform operates around critical annual reporting cycles that determine when new features, improvements, and data submissions can be tested and adopted by users. These overlapping cycles created a distinctive product development rhythm and required careful prioritisation to ensure the platform evolved without missing key market opportunities. As one of the first platforms of its kind, the product played a pioneering role in bringing transparency and benchmarking capabilities to the asset management industry.
The organisation operated with a strong product-led mindset, with well-informed stakeholders actively collaborating across teams. Continuous experimentation, research, and rapid shifts in focus were embedded in the company culture, creating a fast-paced environment where multiple teams ran parallel initiatives to validate ideas and improve the product.
Conducting UX research in this domain presented additional complexity. Many users interacted with the platform only periodically during reporting cycles rather than daily, which made recruitment and engagement more difficult. To overcome this, we conducted regular user interviews (approximately every two weeks) and supplemented qualitative insights with internal data analysis and feedback from customer-facing teams. Asset owners were generally more open to participation than asset managers, which influenced our research strategy.
We triangulated insights from multiple sources to inform design decisions, combining user feedback with behavioural data from analytics tools such as Google Analytics, Hotjar, and Mixpanel. These insights were shared across the organisation on an ongoing basis, ensuring that product development remained aligned with real user needs and market dynamics.
Designing a communication hub for asset owners, asset managers, and internal teams
The Project
One of the most transformative initiatives within the platform was the development of the Queries / Comments Tab, designed as a structured communication hub connecting three key stakeholders in the ecosystem: asset owners, asset managers, and ClearGlass’ internal data team.
Through internal discovery we identified that communication around data submissions represented a major operational bottleneck. Questions, clarifications, and data discrepancies were being managed through fragmented channels, creating delays, reducing transparency, and significantly slowing the overall data collection and validation process.
The goal of the project was to introduce a centralised communication layer within the platform, enabling all parties involved in the data submission process to collaborate more efficiently while maintaining a clear audit trail of conversations and decisions.
Methodology
To better understand the problem space, the product team conducted a series of collaborative workshops and stakeholder interviews over several weeks. These sessions involved internal teams responsible for managing submissions and client communications, allowing us to capture operational challenges and identify opportunities for improvement.
We began with card sorting exercises to better understand how users conceptualised communication categories and message types. This helped us define the underlying information structure before moving into deeper discovery work.
From there, we developed detailed journey maps and interaction flows to visualise the communication process across the three user groups. These artefacts helped clarify the complexity of the interactions and informed the structure of the proposed solution.
Design & Prototyping
Once the core interaction model was defined, I moved into the prototyping phase, translating the conceptual workflows into interactive prototypes that demonstrated how communication could be embedded directly within the platform experience.
The prototypes allowed us to test the proposed workflow with users and validate whether the system supported their real communication needs during data submission and validation cycles.
Following successful validation, the designs were refined into high-fidelity interfaces, which were then prepared for development.
Cross-Functional Collaboration
Engineering collaboration was embedded throughout the entire process. At least one developer participated in each discovery and design stage, which proved invaluable when exploring technically complex interactions.
Having engineering expertise within arm’s reach allowed us to quickly evaluate feasibility, refine ideas in real time, and significantly accelerate the design process for this multi-stakeholder feature.