Bridge Learn is a LMS catered to fortune 500 clients. It however wasn’t always this way.

Like most start-ups, our first clients were SMB. As Bridge grew, so did their customer size. Pretty soon we had bigger companies, such as Tesla, using tools designed for much smaller organizations.

We were also losing deals. One of the top reasons potential customers did not ink a contract was due to a lack of reporting features.

I lead the discovery and design process. Since we were implementing a new backend, I collaborated very closely with the lead engineer. Tech constraints informed what was possible for me, design informed what the tech should strive for.
Job seekers were consistently complaining about the relevancy of jobs on Indeed. There was alot of wasted effort of clicking on a job card to read more only to find it didn’t meet their needs.

We needed to help job seekers find the right jobs by continually learning what they wanted and delivering personalized results based on their inputs.

I had two weeks to deliver a design that SWE’s could start working on. From that point I had another couple of weeks to refine the details.

It took some time to build momentum, but over the course of a quarter we saw less and less customer acquisitions being lost due to reporting. We did however see immediate wins for our customers.

By giving admins more competent tooling, we were able to help them confidently use their data to answer important questions.

The net-new product gave admins the power to search and manipulate their data. No more having to export and join multiple .csv files. The table design was broken down to its atomic levels and added to the design system for other products to utilize.
By giving admins more competent tooling, we were able to help them confidently use their data to answer important questions.

Sometimes admins weren’t 100% sure what they were looking for. Clicking on a row to drill down into the details gave them another way to explore their data.

A prominent context switcher deemed the “mega-filter” allowed admins to quickly search the most asked for data sets as revealed by research.

Based on research, we identified the intervals we felt would cover most of the use cases. A custom date picker was also added. This component was added to the design system.

Filtering is table stakes for any table design. I wanted the filters to be exposed to assist in discovery. Filter search was added for longer lists to assist with recall.