Optimizing for the Future
Making Project Managers even more efficient.
Research Now has been the leader of digital data collection for over a decade, and as the leader, Research Now has consistently defined and shaped the future of the research industry. Just in the last year, Research Now has launched several products aimed at helping businesses better understand their audiences and to help them make smarter decisions.
All of this growth, however, has put strain on the existing teams responsible for creating and managing projects. To alleviate some of the stress, the UX team was tasked with increasing the efficiency of internal tools so that we could improve key metrics, like the time it takes to field a project and the effort required to train new employees.
The following describes the process we took to diagnose existing usability issues and how we ultimately addressed them. This page has been left intentionally vague to comply with confidentiality agreements.
No Sacred Cows
To avoid recreating flaws in the existing system, the UX team questioned all assumptions. This included reevaluating existing workflows and clarifying the definitions of common terms used throughout the company and its software.
Additionally, we were never shied away from proposing radically new ideas. While many of them ended up in the garbage bin, several led to solutions present in the final designs.
Our design process…
Finding redundancies and strategically cutting them out.
To better understand the system, we used both qualitative and quantitative research methods. By combining methods we could create compelling narratives that helped explain existing pain points.
We consolidated our findings with the help of affinity diagramming and experience maps. With the quantitative data, we also calculated how much time would be saved by implementing key features.
After a steady back and forth between the designers and key stakeholders, we built a prototype that we tested on real Project Managers to measure efficiency gains.
Leveraging the benefits of both qualitative and quantitative data.
At the beginning of the redesign, none of the members of the UX team were knowledgable of how the existing system functioned. To better understand how the system currently worked we performed several contextual inquiries.
Additionally, we performed several remote interviews to learn how different teams across the globe used the software. We found many variations on how Project Managers navigated and used the site depending on the types of projects they used and where they were located.
GA + Tableau
We used Google Analytics to track page usage, site speed, and behavioral information. To make the data even richer, we sent Google Analytics key information about projects so that we could have access to the information during the analysis phase.
We also had reports generated within Tableau to get an even broader picture of the existing system. With Tableau, we were able to collect information regarding team composition and how well projects were performing.
By combining both data sources we were able to ask much deeper questions regarding the usage of the internal tools within Research Now.
Analysis of both the qualitative and quantitative data provided rich insights.
We organized over 400 observations and breakdowns into overarching themes by categorizing each note by intent and task. Once we were done, we had 7 different distinct areas ranging from errors to communication.
We referenced the affinity diagram throughout our design process in order to ensure each of our design decisions were grounded in data.
Data Mining with R
With R I was able to find interesting trends within the data collected by Google Analytics and Tableau. On the left, for example, is a graph of the most common sequences of pages accessed for all projects. This helped us reconsider how we wanted to update the navigation for the new version of the software.
Before I was able to learn anything, however, I had to do significant cleaning and manipulation of the data. For example, I had to restructure all of the Google Analytics data so that it was structured by Project instead of by page.
After consolidating we came up with the following insights.
Opportunity Area 1
System requires duplicate entry of project data.
Opportunity Area 2
Teams perform similar tasks differently.
Opportunity Area 3
Documentation is not adequately maintained.
Opportunity Area 4
Important details are buried several pages deep.
Opportunity Area 5
Navigation does not match mental models.
Repeatedly testing until it was just right.
AngularJS + Firebase
To ensure that the application was indeed more efficient, usable, and enjoyable, I built a prototype with AngularJS that demonstrated our major design changes. This included everything from navigation to component interactions.
This was a humbling experience because it revealed areas that we oversimplified or where we improperly used existing terms, but after repeated iterations we had a prototype that was significantly better than the original design.
Once we were done, we were able to compare the time it took to complete projects within the old and new systems. With this information, we were able to calculate the ROI of the entire project if it was implemented by the development team.
The people who made this project a reality.
Sr. User Experience Designer
Kris Lynn Giamello
While designing the solution, each of us took an equal role in determining the direction of the design and where it ultimately headed. My main contributions can be seen on the right. Shoutouts to Kris for scheduling everything and keeping us on track and to Nicki for making our designs pop.
Led contextual inquiries and affinity diagramming session
Provided quantitative analysis
Developed final prototype
Led design discussions with upper management
Produced documentation for dev team