Search redesign
Redesigning the Award Search experience, providing context to formulate a search and flexibility when interpreting results.
At a glance
Role: Design Lead
Timeline: Winter 2022 - Winter 2024
Activities: Scoping and roadmapping, user research synthesis, data elements mapping and APIs, sketching, wireframing, prototyping, usability testing, Figma components, design systems, developer handoff
Background
Award Search on USAspending.gov provides the public with the ability to search for federal awards, like contracts, grants, and loans, based on specific criteria, like the agency that gave the award, the entity that received the award, or other characteristics like the industry, time period, or location of where the award takes place.
This feature had not had a significant update since its launch over three years ago, so it was due for some updates.
What do we already know?
Even though there weren't any recent design updates, I realized that we had a ton of user research about Search. There had been nine usability studies exclusively focused on Search in the past three years.
To take stock of what we already knew about usability issues and user requests for updates, I led a re-synthesizing effort of the existing research reports about Search. We divided the nine reports among the designers on the team, and we each read our assigned reports. Then, using a Mural board, we pulled out the key findings from each report and tagged them as being an insight (yellow sticky note) or a pain point (pink sticky note).
After all of the insights and pain points were catalogued, we created groupings of similar stickies, to find the overall themes across reports.
We learned that many of the reports had similar findings about user behavior.
Exploratory research findings
We divided the themes up into three groups related to the user flow of completing a search: formulating a search, understanding search results, and downloading search results.
We also found several themes within each group:
Selecting filters
Users come to Search with an idea of what they’re looking for. People don’t come to Search to aimlessly explore, they have an idea of what information they’d like to find.
Users don’t always know the best way to search. Both new and returning users need guidance at different points in the process. For instance, new users might want context about what some of the filters mean, while returning users might be interested in data limitations and nuances for each filter and calculation.
Users need consistent design and interaction patterns in order to complete a search. Because filters were added at different times, a variety of interaction patterns were used when adding filters. Creating consistent interactions will be a main focus during the redesign.
Understanding Search results
Users want to understand the full picture of what’s being shown in the results. From aggregate totals across agencies and industries down through the details of how results are calculated, users want to understand what they’re seeing and why.
Users desire alternate views of search results, but are sometimes unable to find what they’re looking for. Some users are unable to find alternate views (like a map or over time chart) because they’re hidden behind tabs. The default view of a search results table is not always most helpful for new users who need more plain-language context. Additional views, like table views of each visualization, have been requested by multiple users across research efforts.
Downloading Search results
Users want more control over their download options. The process of downloading data from Search doesn't currently allow for much customization, and users have asked for the ability to choose which files are included in their download, as well as the ability to select which data columns are in their download.
Determining how we want Search to work
While we were completing the research synthesis, we also had recurring meetings with development leads and product owners to document the current state of how Search is working.
I created a collaborative Mural board where we documented the current state of how each Search filter is working and the changes we want to make to each filter during the redesign.
I facilitated sessions where we mapped out how each filter worked, which calculations they performed, and which data elements they used. Together, we documented a comprehensive picture of how Search worked, which is shared documentation that we didn't previously have as a team.
Time to sketch!
When we had all of the pieces on the board, it was time for the designers to start designing. I led sketching iterations and internal design reviews.
We came up with some pretty cool ideas to make searching more accessible for new folks, more clear for the data- and technically-minded folks, and also more customizable for recurring users who are just trying to get something done.
Testing concepts
We wanted to test the filter groupings and a few other concepts with both new and frequent users, so we prototyped a set of search tasks in a low-fidelity interactive prototype.
We decided to test a low-fidelity prototype (that is, no finalized typefaces, colors, components, etc.) because we wanted participants to know that this was a work-in-progress, so they wouldn’t feel bad about giving their honest opinion. We also wanted participants to provide feedback on the organization of information, and not get distracted by any flashy colors or visuals.
It was also faster for our small team to put a lo-fi prototype together 💪
We tested with five of participants, a mix of frequent and new users, and we found out that:
Additional views and capabilities within Search were more clear
It was clearer why certain filters led to their results
Certain updates to the results table views, like making award descriptions more prominent and keeping the left-most column visible when scrolling horizontally, provided helpful context
Adding details and views, like showing the count of award types within each result visualization, were welcome improvements
Time for high fidelity mockups
Armed with insights, we continued prototyping, this time moving into high fidelity.
We created desktop, tablet, and mobile mockups and documented interactions, layouts, and new components for the developers.
I worked with our Product Owners and the Backend and Frontend development leads to scope progressive releases of these features. The team is currently implementing these on the site.
This feature is currently in development, so stay tuned for more updates!
Lessons learned
Progressive updates to a feature are more sustainable than significant overhauls. Moving forward I’d advocate for progressive updates to Search as insights come up from user research. The team spent a lot of time just getting up to speed with the most-recent research, and I think that to stay up-to-date with user feedback it’s better to make smaller, more frequent updates than larger, less-frequent overhauls.
Designers benefit from understanding the ins and outs of a product’s subject matter. Even though it can be technical, understanding the underlying data schema and calculations within the Search feature really helped the design team to effectively update filters and result views. One of the main points of user feedback was the need for more context about why certain results appeared, and understanding the nuances of what contextual information is helpful when, could only come from diving into the data and backend filter functionality.