Data & Analytics Hub for Indeed Hire

PRODUCT OVERVIEW

Indeed Hire is a recruiting agency within Indeed where recruiters known as Delivery Specialists handle the sourcing, screening, and hiring process on behalf of enterprise-level companies looking to improve their talent acquisition flows.

ROLE

UX/UI Designer

TOOLS

Figma, Miro

DELIVERABLES

UX research findings, vision wireframes, workshop plan + facilitation, MVP designs

User problem

We had two main target users for this project: Delivery Specialists and Client Relationship Managers (CRMs). 

Delivery Specialists work to move thousands of candidates through the hiring funnel for many jobs each year, while Client Relationship Managers assess the hiring funnel performance of each client and job and provide recommendations directly to clients where they find opportunities to improve. 

We received feedback that both user groups were missing the data and reporting they needed in order to understand where hiring processes could be improved. From here, the Data, Analytics, and Reporting (DAR) team was formed, and we set out to learn about users’ pain points and design solutions to solve them.

How might we allow users to more easily access and understand the hiring data we collect in order to improve workflows and provide better client experiences?

Process overview

  1. Conduct Discovery research
  2. Develop UX vision
  3. Conduct concept testing
  4. Lead user story mapping workshops
  5. Create + test MVP designs
  6. Launch

1. Conduct Discovery Research

Due to limited UX Research resources, I had the opportunity to help lead some of the research efforts for this project.

Goal

The goal of this research was to uncover as many pain points and challenges as possible. We also wanted to gain a better understanding of what users were currently doing to find the data they needed.

Methodology

Qualitative 1:1 video interviews with Delivery Specialists and CRMs. We asked participants a series of open-ended questions such as:

  • How are you currently accessing data?

  • Describe 3 of your top challenges when trying to use data and reporting to do your job?

  • How would you ideally like to use data and reporting to complete tasks in your role?

Outcome

Participants were eager to share their thoughts. Many asked to share their screens and show me that they were manually recording candidate status data using multiple Google spreadsheets and reporting tools. Others showed us how they were using Google slides to draw their own data visualizations so they could share reporting with clients.

We came away from this study with 100+ sticky notes from these sessions, which we affinity mapped into 12 main categories by working with the DAR team’s Product Manager and Engineering Manager.

2. Develop UX Vision

With input from Product, UX, and Engineering, we mapped the user problems on a matrix (see image ) according to how much effort + value each one would bring to our users.

Now that we knew what challenges users were facing, it was time to do some Vision work. In creating a 0 to 1 solution, it was important to have an idea of what direction we should head in.

Inspiration

The first step was looking at examples of reporting experiences in the marketplace and gathering inspiration for our own solution.

(Note: screenshot of reporting inspiration blurred intentionally)

Data visualizations

Data visualizations were highly requested during the initial user interviews, so I looked at what was available in Indeed’s design system, as well as what other types of data visualizations might be best suited for the type of data users wanted.

Wireframing

Based on learnings from the initial discovery research, created low-fidelity wireframes of a Data & Analytics hub that could exist over the next 1-2 years. We took a first pass at creating categories, including various reports, and a variety of data visualizations that we could show in concept testing to our users to get their initial reaction and feedback.

3. Conduct concept testing

Working closely with UX Research, we stitched together a clickable Figma prototype and created a research guide to conduct concept testing of the wireframes. 

Goal

The goal of this research was to validate with users that we were on the right track, and to help generally understand what report + features they would need for an MVP. 

Methodology

1:1 concept testing with users from both of our target user groups

Outcome

We were able to identify where the Vision wireframes aligned with user expectations, and what areas needed changes as we would begin to develop the MVP and following iterations. We were also able to start prioritizing which features and functionality to focus on in the design. 

4. Lead user story mapping workshops

Workshop goal: Have our users help prioritize the long list of highly-requested features (figure A) uncovered during concept testing so we could start with an MVP.

I planned and led a 2-part user story mapping workshop with a group of our target users.

We put every task that users wanted to complete on a user story map, and I asked participants to vote on their top 3 features and tasks they’d like for MVP (Figure B) .

During this vote, participants were almost unanimously in favor of the same set of MVP features.

Post-workshop team alignment

After the user story mapping workshops, I synced with the Product Manager and Engineering Manager to ensure that the top-voted features were feasible from a technical standpoint. Now that we were aligned as a team on MVP features, I was able to move forward and begin UI design.

Figure A: Affinity map of highly-requested features from concept testing

Figure B: User story map workshop + synthesis completed in Miro

5. Design + test minimum viable product (MVP)

For MVP, we decided we would build the following:

  1. A new Analytics page with multiple filtering capabilities, where the team can continue to add more data and reporting for users to access.

  2. Candidate Journey report, which shows the candidate volume per hiring stage. 

  3. Unmatch report, which showed the volume of candidates that were unmatched, segmented by the reason why they were not a match for a job. 

There was no official data visualization library at Indeed, so I dove into creating several explorations for our MVP, taking into account additional capabilities and chart interactivity we hope to add in the future. 

Create

Various data visualization explorations I created from scratch

Test

MVP designs

Because of the user story mapping workshops and the previous user research, we rapidly completed usability testing with users to make sure nothing fell through the cracks when looking for minor usability issues.

6. Launch + next steps

Until the target launch date of 4/1/2023, I kept in constant communication with the developers and product manager to answer any questions and support the teams as development began. Although I was no longer working at Indeed by the time this launched, here were following next steps for this project:

  • Gather initial user feedback and work on any small feature iterations or tweaks as needed

  • Add in new data visualizations, such as unmatch reasons and job performance reports

  • Identify and design other places that data and reporting can be inserted into the product internally, such as on a candidate’s details page

  • Socialize the work we’ve done so far with other teams at Indeed that may be looking to gain their own homegrown reporting solutions