FACTUAL: PLACES INSIGHTS PRODUCT
Filtering Systems for Big Data
ROLE Product Designer
WORK Information Architecture, Interaction & Visual Design
TEAM UX Researcher, 2 Front End Engineers, PM
Do you ever wonder how physical businesses keep track of their branches’ popularity?
Factual’s Places Insights product provides a way for businesses to measure the footfall trends of their stores while keeping customers’ data private.
With the success of the product, I was tasked with redesigning the “Top Filter Bar,” which allows users to filter or compare data against each other. The purpose of the redesign was to make the filter bar more scalable and dynamic so that it can hold different kinds of info.
DESIGN PROCESS
How might we design a filtering system that is both dynamic and easily understandable?
The current Places Insights MVP can only hold a small amount of filters and doesn’t allow users to compare their company’s different customer segments against each other.
It also had outdated UI that needed to be updated to match Factual’s other products.
PART 1 DESIGN PROCESS TIMELINE
The redesign process took 4 weeks, with tasks spanning from understanding the scope and user needs all the way to user testing.
Preliminary Research
Sat in user interviews with 3 real clients
Scoping
Met with PM to align on scope and identify user needs and design principles
Designing
Figured out the filtering logic, how to make filters scalable, and overall design
User Testing
Tested the UX and Interaction Design
PART 2 IDENTIFYING DESIGN PRINCIPLES AND REDESIGN REQUIREMENTS
After speaking with my PM and sitting in 3 user interviews, we came up with these requirements for this project:
Adding visual clarity
The design should be clear enough to enable users to derive useful conclusions from the data.
Balancing Features
The design must make it easy for companies to both filter their own data and compare their data to their competitors’ data
Ensuring Scalability
The challenge is to design a scalable filter bar so that new filters can be added with ease without sacrificing UI quality.
Updated UI
The current UI uses Factual's old design system so the new UI will need to be updated to be harmonious with Factual’s other products.
PART 3 MAJOR DESIGN DECISIONS
This section will cover some of the major decisions that we made during the exploration phase before arriving at the final solution.
❌ Comparison-First
Incorrect hypothesis: Users will naturally choose to see competitors before filtering everything down by other factors like geography and age
✅ Filter-First
Correct hypothesis: Users prefer to filter through the data of their own business before comparing their data to their competitors’ data
❌ Neatness
Incorrect hypothesis: Our users are already used to using data analytics tools, so we can hide obvious definitions and focus on visual clarity
✅ Context
Correct hypothesis: Users need a visible explanation or tool tip for every term or graph that is introduced so that they are empowered to use the data to make important decisions
❌ Scalability
Incorrect hypothesis: It is more important for filters to be displayed on the side so that no matter how many filters are added, they can be accessed without breaking the UI
✅ Clarity
Correct hypothesis: Because the filters are near the top of the screen, they are immediately visible and don't take space away from the data
Final Solution
After weeks of exploration and evaluating design tradeoffs, below is the finalized design.
Disclaimer: my contribution to the project lies mainly in the comparison and filtering structure, which affects the whole web application across multiple pages. All data visualizations were designed by Lawrence, who was one of my co-collaborators and mentor.
Please play video using the max resolution.
Reflection
Before this summer, I was intimidated by the thought of designing a tools that involve big data because it is a huge responsibility to design properly. At the same time, though, I was – and still am – very inspired by the effect that good quality and actionable data can have on an organization and on the world.
If there is one thing that designing Places Insights taught me, it's that good, high-quality data is useless unless it is understandable and actionable.
Thank you!
My mentor Lawrence, who was a huge part of this project. Together, in the initial exploration phase, we explored more than a hundred different screens to arrive at the right solution. He trusted me and brought me in as an equal collaborator despite being an intern he did not have experience working with.
My co-intern Aneri, who was successfully able to drive the team in the right direction when we were going off-path. Her ability to pitch design decisions to the rest of the team is top-notch, and she was dedicated to helping us make it work till the end.
The rest of the Factual design team, who pushed through the stress and ambiguity to get this beefy design project done before the deadline.
Our PM Zohaib, who was really supportive and set clear expectations for which features and updates needed to be brought to life.