This project for Open Assessment Technologies focused on uncovering deep user insights to guide the development of TAO Studio, a SaaS assessment platform. Through strategic UX planning, workshops, and mixed-method research, the work identified key user needs, clarified personas, and provided actionable insights to enhance engagement, shape product strategy, and explore opportunities for leveraging AI in education.
Open Assessment Technologies (OAT) sought to transition from a customisable education assessment platform to a SaaS provider. This shift revealed gaps in understanding core Users, prioritising features, and delivering value. The goal was to align OAT’s business growth ambitions with meaningful User engagement by clarifying key personas and addressing their needs.
The project began with UX discovery workshops to identify gaps in knowledge and align on priorities. Conflicting information about 15 User profiles was streamlined into three key archetypes, validated through evidence-based personas. Mapping the customer journey uncovered missed opportunities to engage Users earlier in their assessment cycle, forming the foundation for targeted research.
Using qualitative User interviews and quantitative surveys, the research revealed critical insights:
By combining data with sales strategies and industry reports, the research identified six key unmet needs and strategic opportunities.
These insights aligned with OAT’s SaaS ambitions and highlighted areas for innovation and differentiation.
Lack of focus on key personas: Do you struggle to identify who your primary Users are for your product or service?
Developing evidence-based personas could help you better understand and address your Users’ specific needs.
Poor User engagement: Does your product fail to engage Users early in their journey, leading to lost opportunities?
Mapping the customer journey might reveal key moments where improved engagement can make a difference
Don’t know where to focus to offer customers: Do conflicting internal assumptions make it hard to prioritise product features?
Mixed-method research could provide clarity by validating assumptions and uncovering key insights.
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