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Data Analytics Web App

Overview

The Data Analytics Web App is a data tool that helps recruiters and hiring managers refine job details associated to open positions. Supply, demand, compensation, and location play extremely important roles when sourcing tough to fill positions.

Hiring and Recruiting Workflow Scenarios:

  1. Hiring managers will use the data analytics tool to better understand the job market prior to opening a new position

  2. Recruiters, working with a hiring manager, will use the data analytics tool report during kickoff meetings or after the job is opened

  3. Recruiters will user the data analytics tool to help them refine their search criteria while at the same time sourcing candidates


 

Overview Cont.

Product History

The existing legacy tool had been built by engineers and the user experience was never a priority. Leadership wanted to revisit this powerful data tool with the goals of addressing the low usage and create experience consistency that matched the product ecosystem.

The problem

Our target users (recruiters) with subscription access weren’t regularly using the tool. Only 8 to 10% of all users were actively using the product. We wanted to grow our active user-base and address some of the main pain-points that users had with the experience.

Original dashboard.

 
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Main User Pain Points

"I want to export certain data points, not a full 10 page report."

"It's just a wall of data. I'm unsure if this data is accurate or what it even means."

"I hate filtering in this app. I have to continually scroll and scroll to make a simple update."

 

 
 

Exploratory Wireflow

During our exploratory collaboration sessions with research and engineering we wanted to tackle big wins – high impact with minimal effort.

1. The user has to perform a job title keyword search prior to use access the tool and on returning visits we’d surface the recent keywords, past search history, and relevant data. This would speed up the user workflow by removing the need to continually perform a new search.

Users would then land on an intermediate screen that would allow users to see high level results with the opportunity to refine results through the filter.

2. This filter overlay would address user’s pain-points associated with a long filter panel in the left rail. Users would be able to filter and see the supply, demand, and compensation data update in real time without having to apply the filter to the previous search results.

After interviewing users and better understanding what data they found important on their dashboard. We came to the conclusion that users want to navigate through the content at their own pace.

3. Users would seamlessly shift between their different supply, demand, and compensation views.

4. Secondary data would still be accessible in the data gallery below the data hero area.

5. User’s would be able to dive deeper into the data with these expanded pages. Actions, include exporting the data and tweaking the data if functionality permitted. Our long term goal was to bring in relevant candidate profiles from the candidate sourcing tool.

6. A customize report with an overview page. Users only wanted to export relevant data associated to their requisition.

 

 
 

Data Visuals

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Through research, and trial and error, we realized that visually stunning data visuals can sometimes over shadow the insights. If users are unable to understand insights the data trying to convey then that data is illegible and wasted.

 
In the example above the, the option on the left is displaying varying colors with similar values . The option on the right is using the same color but with varying values.

In the example above the, the option on the left is displaying varying colors with similar values . The option on the right is using the same color but with varying values.

In the example above, the gray options represent what a person with monochromacy color blindness might see. The left option has similar color values leading to a potentially illegible data visual.

In the example above, the gray options represent what a person with monochromacy color blindness might see. The left option has similar color values leading to a potentially illegible data visual.

During our data visual exploration we wanted to follow visual accessibility best practices. Using variations in color value (or tones) helps color blind users differentiate between segments of the data.

 

 
 

Containers and Layouts

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Following modern design trends we broke the analytics into cards which allowed the content to react responsively for mobile, better customize the exportable data points which they would then present to their clients in reports, and segment the data allowing the user to dive deeper into relevant content.

We experimented with accordion cards that grouped related content that would be selectively exposed by the user. Usability testing proved that this functionality wasn’t ideal and users and prefer that all relevant information to not be hidden from view.

 

Accordion treatment.

Dynamic Filtering Panel.

Dynamic Filtering Panel.

Expanded Data View.

Expanded Data View.

Exported Report Overview Page.

 

 
 

Retrospective

Addressing the pain points, plus big wins, did positively affect the user experience, and EOI did increase in the following product releases. Overall, there were key issues that weren’t going to be solved solely by mvp solutions.

Here are my suggestions that could would have better setup the product for success:

  • A long-term product roadmap that includes user validated feature releases that align with business goals

  • Better product alignment with engineering and design

  • Understand why data points or sources are leading users to distrust the results

  • A more robust reporting experience that is mobile friendly

  • Integrate this data into the other products within the ecosystem

  • Deliver candidates after an analytics search is performed

Screenshot of the live product today.

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On the Bright Side

Working on this product refresh was a great opportunity and learning experience. I was able to worked with smart product, engineering, and research teams. On a daily basis I collaborated with software architects, full-stack developers, and data scientists. This exposure helped me to better understand the challenges that face software teams.

As the sole design resource I led meetings, whiteboarding sessions, and presented my progress to r&d leadership. This experience has helped guide me through other software projects.