UXR Tooling Case Study
This project focused on defining and implementing a fit-for-purpose UXR tooling ecosystem in a space where there was no clear strategy and limited internal expertise.
The challenge was to deeply understand researcher workflows, evaluate a broad and evolving supplier landscape, and identify the right combination of tools to support current and future needs while navigating budget, legal, and compliance constraints.
Context
The UXR function was operating without a clearly defined tooling strategy. Multiple tools were being used or trialled in isolation, and there was no aligned view on what the right platform setup should look like.
At the same time, the tooling landscape itself was complex and rapidly evolving, with overlapping capabilities across vendors and increasing emphasis on AI-assisted research and analysis. Internally, there was also no single source of truth for requirements, and decisions were being made through fragmented conversations.
This was also outside my core domain, requiring me to build a working understanding of UXR methodologies, workflows, and tooling needs from the ground up.
Objective
The goal was to define and implement a coherent UXR tooling strategy that identified the right combination of tools to support key research workflows, balanced capability with cost and compliance, reduced fragmentation, and positioned the team to take advantage of emerging capabilities such as AI-supported analysis.
My Role
As the client-side lead, I owned the end-to-end process of understanding the space, structuring requirements, and driving the supplier evaluation and decision-making process.
I ran structured stakeholder discovery to centralise input across researchers and build a clear picture of needs across moderated and unmoderated testing, diary studies, IA research, panel quality, analysis workflows, and repository requirements. This replaced fragmented conversations with a shared and structured view of requirements.
I then led a thorough supplier investigation across platforms including dscout, Optimal Workshop, Askable, GreatQuestion, Genway, Strella, Discuss.io, and repository solutions such as Stravito. This involved organising staggered demos, follow-up deep dives, and targeted trials to evaluate tools based on real workflow performance rather than surface-level features.
In parallel, I worked closely with procurement, legal, and security teams to ensure all proposed solutions were viable from a compliance and contracting perspective, and aligned decisions to budget cycles and renewal timelines. I also drove stakeholder alignment, moving the team from fragmented tool decisions to a coherent platform strategy.
Approach
The work began by establishing a clear set of requirements grounded in real research workflows. I mapped out the full range of research activities and identified where current tools were insufficient.
I then conducted a broad supplier scan followed by a structured narrowing process. Tools were evaluated through multiple stages including initial capability screening, structured demos, deeper technical sessions, and targeted trials where required.
Particular focus was placed on evaluating unmoderated research quality and emerging AI-assisted analysis capabilities, where vendor claims needed to be validated in practice.
Rather than forcing a single-tool solution, the outcome was a composed tooling stack, with dscout selected as the primary platform for diary and unmoderated research, Optimal Workshop as a specialist IA tool, and Stravito explored as a cross-functional repository. Decisions were deliberately framed as revisitable after real-world usage.
Output
The project delivered a clearly defined UXR tooling strategy, a structured supplier evaluation framework, procurement-ready onboarding plans, and a defined model for how tools would be used together.
Impact
This work transformed a fragmented tooling landscape into a coherent, strategically aligned platform. It enabled more confident decision-making, improved research capability, particularly in analysis, and established a more disciplined approach to future tooling decisions.