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Case study

INGRID data product

Leading a team of stakeholders through the User-Centered Design process to define a data product for graduate employers.

📖 Background

The work outlined in this case study was part of a project to explore how student/graduate data could be used by employers seeking to discover graduates when hiring.

Third-party data had shown that a large number of UK job vacancies were going unfilled each year despite the high number of students graduating, so the organisation wanted to explore how we could help to get more graduates into roles.

I’d carried out user research interviews with those who handle recruitment in Small to Medium-Sized Enterprises (SMEs) and larger-sized enterprises as well as a handful of recent graduates. We’d gained an understanding of their recruitment processes and had uncovered problems/needs that were common across most interviewees.

⚠️ Problem statements

These two problems stood out:

“It can be difficult to find candidates from diverse backgrounds when trying to strengthen our teams.”

“We struggle to identify which University courses to target our marketing at when trying to attract high quality candidates. Some courses are more obvious for certain roles, but not knowing the courses on the fringe mean we’re missing opportunities to hire great talent.”

✨ Ideation

At this point in the project the team had a clear understanding of the challenges users faced and it felt appropriate to start solutionising, but the team were struggling to come up with ideas.

So I setup and facilitated a brainstorming workshop. In Miro, we used a virtual version of the group passing technique - you write the start of an idea on a post-it and then move onto the next person’s post-it and add something to the idea. You keep going until you get back round to your original idea and see how it evolved.

The workshop went smoothly, was enjoyed by the team and we produced 18 different ideas! The team were buzzing afterwards, as they’d gone from feeling all was lost to having some exciting new ideas to try.

✏️ Prototyping

We narrowed the ideas down to 2 that we felt had the greatest potential, considering technical feasibility, value to the user, effort to implement and commercial value. Then we worked together to produce a prototype for each idea using Figma.

Prototype 1 - Gradspire

Graduates interviewed during the earlier discovery phase talked about their career aspirations.

Gradspire was a series of data dashboards where employers could choose a role they were hiring for and see insights about student aspirations regarding that role. For example, seeing a list of the courses graduates studied who aspired to become mechanical engineers. Knowing this would enable employers to target their marketing at students taking those courses.

Prototype 2 - INGRID

A traditional BI platform in which you can create custom dashboards using student data from large datasets.

🤔 Assumptions gathering

Prior to concept testing the prototypes, I did a quick assumptions gathering exercise with the team using bingo cards, to find out what the team expected to hear in the feedback. I found this to be an effective way to get the team invested in the testing and thinking objectively going into it.

🔬 Concept testing

I carried out 1-2-1 interviews remotely using MS Teams with our pool of participants, with members of the team observing and taking notes.

Each participant was provided with a link to the prototypes and asked a series of open- ended questions. The interviews followed a semi-structured approach; providing room between questions to follow lines of enquiry that emerged through our conversations.

For one interview we allowed the participant to explore the prototypes on their own, as well as ask them questions about specific pages. This isn’t something I’ve been encouraged to do before as you’re trying to fill specific gaps in your knowledge, but for these prototypes it worked quite well, as there was quite a lot of data in each one and it allowed the users to find things that were relevant to them.

📊 Research analysis

We pulled our notes out into an affinity map to help us gain a holistic view of the feedback about the concepts.

🗣️ User needs/requirements

We also learnt something about the users through our conversations with them, so we captured user needs as well.

📋 Product requirements

Seeing both the concept feedback and user needs helped us to define product requirements. This enabled us to frame what the product needed to do for users, directly linking each requirement back to a user need.

📜 Concept statement

To wrap-up the project, we turned what we’d learnt and validated into a concept statement, a sort of elevator pitch that explained what the product would do, who it was for and what problems it solved.

Bonus

Product Death Flow

Just to double check we were confident with the outcome, we ran the product concept through the Product Death Flow, a checklist I put together to make sure we focussed on discovering a product with high user value and commercial value.

There was a shared feeling of joy and accomplishment across the team when we successfully made it to the end!

Product funeral

Once we’d transplanted the valuable bits from Gradspire into INGRID, we held a virtual funeral for Gradspire, to help the team celebrate the fact we’d tried something and it didn’t work. I hoped that this would also prevent people in the future trying to pursue ideas from it that had already been proven to be of little to no value.

This was a moment of delight for the team and lead to requests to hold funerals for other failed product ideas in the future.