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Data visualization
that tells a story.
Wunderkind is a marketing platform which provides personalized, data-driven marketing solutions to businesses across e-commerce and publishing.
My role
UX Designer
Team
Product Manager
UX Researcher
Director of UX
Engineers
Duration
V1: 6 months
Our data visualization system was limited.
Access to performance data impacts marketing strategy, client relationships, and user efficiency. Our existing system needed a facelift to reach the calibre of data management of a powerful marketing SaaS software.
Time Consuming
Multiple teams have to communicate, create, and manually send visualizations to clients across many different platforms.
Out of context
We relied solely on basic line graphs to communicate data which could not account for the range of data groupings we hope to show.
Client mistrust
Lack of visibility into how our data is retrieved and the oversimplification of data views has been the cause of some client churn.
Limited resources
We could not spare a lot of engineering or business resources to bring our data tooling to the ideal level.
I set out to better understand how people interact with data...
...and built a how-to guide for data viz best practices to get us started.
The Key Insights:
Be weary of circles.
People tend to prefer curved to sharp edges, but we're not very good at interpreting value from them.
People are typically good at discerning LENGTHS & ANGLES.
People are generally good at interpreting value from length primarily, and then from angles.
Remove (or strategically present) cluttered data sets.
Less is more. Remove fluff and do what you can to make data as easy to interpret quickly as possible.
Common multi-dimensional graph styles can be challenging for users to understand.
Users can struggle to quickly gauge proportions.
Look to your viewers expectations.
As with anything, the way your viewers want to see data will be the way it should be shown. From structure to complexity, users should define your data viz strategy.
But theory only goes so far, and we needed to figure out how to apply learnings in practice.
We have a good understanding of the theory and best practices,
but how do our users actually want to see their data?
Reporting data
Data that doesn't get along ☹️
By breaking out graphs and providing tooltips to compare the different views, we can maintain each trending visualization without sacrificing being able to compare them against each other.
A consistent issue we ran into with multiline graphs was that metrics would be in vastly different ranges, and often in different units of measurement.
Designing for global utility
Building out our design library 📚
Users need the highest level snapshots all the way down to the nitty gritty details. We have to build components that can withstand different levels of data.
Experimenting with styles
Inserting personality into data views 🕺
Our design system is fun, but to avoid clutter we looked to ways to incorporate branded moments into data viz without sacrificing usability.
We set the standards for how we will use our visualizations.
When to use specific data visualizations.
We defined the use cases for each data viz type.
🕓 Time based
🏆 'Top' preview
🏷️ Categorical
⚖️ Distribution
Using natural language to provide insights.
I added conversational insights and commentary into dashboard data views to give meaning to metrics.
Findings
Sexy data viz is not always the best data viz.
In experimenting with different data visualization styles, I learned the value of keeping things simple and avoiding ornate styles for showing data.
Guardrails need to be built into data views.
Data can be finicky and so defining a flexible and dynamic system for data is essential in development and maintenance of a data system.
Users want to see comparison data wherever possible.
We found that comparing current data to past performance and/or other data sources was a key need for our user base.
Next steps
Build in more customization functionality.
In experimenting with different data visualization styles, I learned the value of keeping things simple and avoiding ornate styles for showing data.
Incorporate AI feedback tools to prompt strategy.
Users liked having insights and commentary included in data visualizations.
AI tools can offer us the freedom to provide more targeted and specific strategy insights and suggestions.
Thank you for reading!
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