About this Project
Data Explorer enables Suzy users to visualize and analyze their research data directly on the platform. Market researchers can load any of their Suzy actions into Data Explorer and create custom banners and sophisticated data cuts using in-platform demographics, targeting criteria, and previously asked questions.
Other features include stat testing, chart visualizations, create custom NET, merged data, and advanced data filtering.
Since its release in late spring 2023, Data Explorer has been a successful upsell adding at least $18k to a customer’s annual contract.
Details
Dates
Start: Q4 2022, Beta Release: Q2 2023
Role & Responsibilities
As Lead UX Designer, my primary responsibility was to balance the robust set of essential functionalities expected of Data Explorer with the existing architecture, all while advocating for simplicity and usability for users of varying experience levels. Once I had a clearer understanding of how to approach the feature design, I created detailed medium-fidelity prototypes in Figma. I then gathered feedback through stakeholder interviews, design critique sessions, and team reviews to refine our ideas and guide us toward a final direction.
Key collaborators
Product Manager, UI Designer, Front End Developer, Principal Architect, Chief Technology Officer
Additional stakeholders
Select members of Customer Success and COE teams to better understand the feature need from a client perspective and provide feedback to key user flows
Product/Design strategies
Stakeholder interviews, competitive/comparative analysis, agile design sprint
Opportunity
Challenges
Data analysis was a major pain point at Suzy.
Market researchers on Suzy traditionally relied on external software like SPSS or Q to analyze survey data due to the limitations of our platform’s crosstabs feature. This feature allowed users to cross-reference only a limited number of data cuts at a time, hindering researchers from efficiently interpreting their Suzy data and drawing meaningful connections across multiple projects.
Crosstabs allowed user to cut by up to one demographic and one other question at a time.
For months, we debated whether to build a new solution or buy an existing one. Initially, the team leaned towards the "buy" approach, considering a premade open-source application. However, after encountering several technical setbacks and undergoing team reorganization, we pivoted. Our principal architect and CTO developed a preliminary framework that was minimal and viable, but not yet a fully usable product.
This is SPSS, which our COE uses.
…and this is Q. These are the industry standard.
As the lead designer for this project, I faced the challenge of understanding our researchers' data analysis tools. With no prior experience using Q or SPSS and only a basic proficiency in Sheets and Excel, I had limited knowledge of concepts like "banner cuts," "derived questions," and "subpopulations." Surprisingly, this turned out to be an advantage. It allowed us to draw inspiration not only from our rigidly designed competitor tools but also from comparator tools that we, as designers, find useful and appreciate.
Development Process
The overall process of building this feature set presented its own unique challenges. Unlike the ideal “double diamond” process, where rounds of design ideation and testing often precede development, we had to work somewhat backwards with Data Explorer. The basic framework had already been established, and our task was to retrofit the envisioned level of functionality and usability into the existing architecture. Additionally, we did not have solid documentation or technical or business requirements to serve as reference for this feature. The process was to get up and go. This required me not only to quickly grasp the feature concepts we were designing but also to ensure they aligned seamlessly with the current backend infrastructure.
During this process, we identified banner creation as the focal point of the user journey within Data Explorer. We recognized the importance of simplifying the 'banner builder,' which later evolved into the Table View creation. Our goal was to offer users enhanced usability that surpassed competitor tools. Reconciling the robust MVP feature set with a simple banner creation interface became one of the key challenges we had to overcome while working on this feature.
An early wireframe for table view creation. We made significant changes to this design by the time the feature was launched.
Users
Our goal was to create an analysis tool that met the robust needs of market researchers who would otherwise create their banner cuts on another application such as Q or SPSS, while still offering a simple and usable interface that less experienced researchers can easily learn.
Solution
MVP Feature Set
We launched a fairly robust Beta product for Data Explorer’s initial launch, allowing the user to:
Load any survey into Data Explorer
Build “table views” (i.e. banner cuts) across any demographic attribute, quota group, segment, panel, or previously launched survey
Create cross-tabs across different survey types
View response data in table or chart format
Export data cuts into a spreadsheet or slide presentation
Key Screen Wireframes
Key Flow Recordings
Loading a Survey into Data Explorer
Creating a Table View
Conduct In-Platform Stat Testing
Applying a table view to multiple questions
Outcomes
Impact
The launch of Data Explorer in Spring 2023 created an additional upsell opportunity for our commercial team, leading to over $1.5 million in additional revenue by the end of the year.
As of Q2 2024, Data Explorer—only available as an add-on to a Suzy Insights license—has made over $3 million in additional revenue.
Agile Improvements
Since launching, our agile Data Explorer team has implemented numerous improvements to the MVP, such as:
Stat Testing for multiple question types
Creating subpopulations and derived questions
Adding subpopulations and derived questions to data tables
Merging identical data across multiple surveys
Public link survey support
Brand Tracker support