Data Visualization Dashboard
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Enabling Market Researchers to quickly identify key insights with effective data visualization and customization.
Role
Lead UI/UX Designer
UX Researcher
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Duration
Jan – Apr 2022
(4 months)
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Tools
Figma, Invision, Abstract, Mural, Jira
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Teams
Lead Designer
Engineering
Product Manager
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OVERVIEW
At SurveyMonkey, I was the lead designer of the Usage & Attitudes product, which was in its infancy at the time. It existed within the Market Research pillar, which was one of three product pillars. It was targeted at researchers wanting to explore how consumers use a product or service and their attitudes (opinions, feelings, and perceptions) toward it.
Our goal was to create an analysis experience tailored to market researchers; distinct from SurveyMonkey’s existing core analyze products, but aligned with the broader Market Research suite.
Because the Market Research suite had unique visual requirements, I collaborated closely with the Design Systems team to ensure my work was scalable across SurveyMonkey while still fitting the Market Research identity.
The aim of this feature was to launch an MVP that enabled Market Researchers to effectively analyze and customize the presentation of data collected from their survey.
CHALLENGES
GOALS
PROCESS
RESEARCH | Competitive Analysis
I analyzed competitors Qualtrics and Typeform to identify strengths and gaps, as well as view industry standard data visualization methods.
RESEARCH | Internal Interviews Uncover Data Design Best Practices
I partnered with the UX Research team to understand what differentiates a “good” graph from a “bad” one, uncovering that researchers place high value on surfaced insights and the ability to quickly highlight interesting findings. I also chatted with researchers throughout the design process to gain feedback on the customization settings, entry points, and interactions.
I also consulted with the Product Designer on SurveyMonkey’s core analyze product, where I learned best practices for ensuring data legibility and minimizing visual clutter.
Researchers value surfaced insights and the ability to quickly highlight interesting findings.
RESEARCH | Secondary Research
I conducted secondary research to better understand data visualization best practices. This included when to use certain chart types, how to best portray complex data in tables, and data table best practices.
RESEARCH | Identifying Constraints
Constraints were identified through conversations with stakeholders in the Market Research team, Design Systems team, Product, and Engineering.
MEASURE | Tracking Success Metrics
Before conceptualizing, I set markers for success. Comparing data from before and after implementation enables data-driven iteration.
DESIGN | Exploring AI Insights and Auto Generated Graphs
I explored flyout toggles that allowed researchers to quickly switch between graph types, as well as new graph visualizations, and a side panel that included customization along with other settings. I constantly went back and forth with the Design Systems and Market Research design team for feedback. Furthermore, I also introduced WCAG-compliant color recommendations to improve accessibility.
Some chart types already existed within Usage and Attitudes when I tagged into the project. I iterated on them as well as made new chart types. There was a focus on determining which data to display, and how researchers could adjust the formatting and visualization. Designs prioritized clarity, and scannability for complex datasets.
I proposed AI insights based on my research findings, but we didn’t have the resources to pursue this.
Based on my user research findings, I proposed using AI to surface key insights directly to researchers. Conversation with Product and Engineering revealed that we didn’t have the resources to inject AI into the analysis page, and that this was on the road map. The business decision was made to surface this with a disabled button. This conversation revealed that we were creating an MVP.
Fine-tuning components
A lot of care was put into the UI/UX patterns seen in the side panel, as these would be reflected in the broader SurveyMonkey Design System and Market Research team.
HAND OFF
When handing off designs to the engineers, I generated style guides for all new components the feature used. I annotated wireframes with pertinent information, such as data behavior.
Product
SurveyMonkey’s Market Research Data Analysis Dashboard takes complex data and translates it into simple but powerful graphs that tell a story.
NEXT STEPS
Although I left SurveyMonkey before this project was completed, I had outlined clear next steps to continue refining the experience. My focus would have been on conducting usability testing to evaluate the customization panel, data organization, and overall legibility. These insights would have informed further iterations to improve researcher satisfaction and ensure the analyze features were both intuitive and powerful.
KEY TAKEAWAYS
Collaboration is essential
Working with Design Systems and cross-functional partners helped align on scalable, sustainable solutions.
Constraints drive focus
While ML insights were exciting, focusing on an MVP ensured the project remained feasible.
Scalability is key
Designing for a large product ecosystem requires balancing innovation with established patterns.