Loop Not Luck
Careers, diversity & inclusion
A diversity careers platform connecting underrepresented entry-level talent across the UK with employers, using a pioneering intersectional approach.
Why this brief existed.
Loop Not Luck is a diversity careers platform built on a pioneering intersectional approach across four dimensions of identity and background: gender, ethnicity, sexual orientation and social mobility. The name is the argument. Entry-level talent across the UK never hears about opportunities they would be right for, simply because they are not in the loop.
The employer side has a matching problem of its own. The brief cites Entelo's 2018 Recruiting Trends Report: talent acquisition professionals spend on average nearly a third of their working week - about 13 hours - sourcing candidates for a single role. Nearly one in three spend over 20 hours, and 70 per cent agreed that sourcing automation would increase productivity. Loop Not Luck's case is that giving recruiters a wide pool of diverse talent plus the right data points removes most of that manual sorting.
The appetite exists on both sides. The 2018 Deloitte Millennial Survey found 70 per cent of millennials want to work for a diverse organisation; High Fliers' Graduate Market in 2020 report found more than half of UK employers struggle to hit their diversity targets. The brief also sets out the backdrop the students were designing into: youth unemployment rising, job security declining, and talent shortages following Brexit and Covid.
“We connect diverse talent from across the United Kingdom to career opportunities they might have never been made aware of - if they were not in the loop.”
What Loop Not Luck asked for.
Refine the candidate UX and build out the early-stage company user flow - posting roles, buying credits and reviewing matched candidates - to improve engagement and retention.
What the students worked within.
Who the design had to serve
Two user groups. UK students completing higher education, and UK companies recruiting for graduate and entry-level roles - where the person using the platform is a talent acquisition professional making fast decisions across a large pool.
Constraints in the brief
- An MVP already existed, serving entry-level diverse talent on one side and companies looking for that talent on the other.
- The candidate journey was already defined and implemented on a modern tech stack. That side was a refinement job with real room to improve, not a rebuild.
- The company user flow, in the client's words, was still in its infancy. Company users buy credits to post roles and receive a feed of matched candidates; that flow needed building out.
- The web app runs on JavaScript and Google Cloud technologies; Figma is used for mockups and user flows.
- The deliverables list was explicitly a minimum, with teams free to push further.
What the team had to figure out.
No answers were supplied with the brief. These are the questions the students had to research, argue about and design their way through.
- Where does the existing candidate journey lose people, and what would make onboarding, profile completion and giving feedback on recommended roles feel worth finishing?
- What does a company user need to see in a feed of matched candidates before they will act on it?
- How should a credit-based model for posting roles be expressed in the interface so it reads as fair rather than as a toll?
- How do you surface the right data points for a quick, well-informed decision without flattening an intersectional profile into a checkbox?
- Engagement is measured as time on the platform and on specific pages, plus completion of key tasks. Retention for company users is measured by buying more credits and coming back for recommendations. Which design decisions actually move those?
The deliverables.
- Candidate journey refinement
- Company user flow
- Role-matching feed
- Prototype
And presented back to the client
The brief set out exactly what the team had to stand up and defend at the end:
- Insights from discovery research
- The problem being solved, and the hypotheses
- A walkthrough of the design process
- Evidence of iteration based on research insights
- Samples of the design artefacts
- A demo of the latest prototype
- Next steps
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