Problem Definition & Key Insights

To understand why the hiring process was breaking down, we combined qualitative and quantitative research. This included stakeholder workshops, interviews with recruiters, and analysis of behavioural data across the platform.

The findings revealed clear and measurable problems on both sides:

• For employers, 58% of job posts received applicants that didn’t match role requirements, leading to hours wasted reviewing irrelevant profiles and significantly slowing hiring cycles.

• For candidates, 73% dropped out before completing the application, largely because they felt their applications lacked visibility and feedback — often described as “disappearing into the void.”

These issues compounded into:

• Slow hiring and decision-making

• Low engagement across the marketplace

• Reduced liquidity, directly affecting revenue

The data made it clear that efficiency alone was not enough - the experience needed to feel more transparent, human, and collaborative.

Experience Goals & Hypothesis

Based on these insights, we defined two core experience goals:

• Make the hiring process more human and transparent

• Design for collaboration, not just automation

Rather than optimising for AI output alone, the product needed to support clearer communication and shared understanding between employers and candidates.

This led to the following hypothesis:

If we prioritise matching based on job requirements first and enable faster, clearer feedback loops, both employers and candidates will show higher engagement.

Success Metric

To validate this hypothesis, we aligned on a primary goal metric:

Job posting completion rate

This metric acted as a leading indicator of engagement on both sides of the marketplace and directly reflected improvements in hiring flow health and platform performance.