Global Filtering
Our filters help users find products faster and make more intentional purchases and decisions. Brooklinen did not have a filtering experience, but our research showed that this is a key feature users wanted.
Background
Brooklinen is a DTC brand “disrupting the bedding industry with premium, hotel-quality sheets and towels at accessible prices”.
Impact
2.6% ↑ Product Page Click Through Rate
2.7% ↑ Add To Cart
2.9% ↑ Conversion
Role
Product Designer
Researcher
View On Brooklinen
Problem Statement
Brooklinen’s website lacked filtering capabilities which limited our users' ability to navigate and refine product based on their preferences or needs.
Hypothesis &
How Might We?
Per user interviews with prospects and customers, we believed filters would help users find products and categories on Brooklinen more easily, leading to an increase in conversion.
”How might we help users quickly narrow down product options so they can find what they’re looking for more efficiently?”
Research
I partnered with my product manager and completed a series of design sprints where we aligned on recommendations for general filter rules and logic and also created a filter matrix which was referenced to make sure our build was returning accurate results.
Design Sprint Goals
Optimal Order of Filter Facets
Filter Facet and Value Taxonomy
User Interaction with Filters in Bedding Bundles
Most Important Filter Facets for Users
Color Filter Interaction and Functionality
Prioritization
Filters were deprioritized for a time due to technical limitations. Filters were reprioritized several months later as our user research proved that this was a huge pain point for users and came up time and time again in our moderated user interviews.
Solution
With all of the preliminary work already completed, our team was able to use the insights from our design sprints to quickly align and dive straight into final designs & development.
We landed on a solution where filter are exposed and expanded on default desktop and hidden behind a drawer on mobile. Per user research we launched filters with the filter facets: Size, Color & Fabric.
A/B Testing
We performed an A/B test on our All Sheets product listing page, one of our most performant pages, where our control experience did not include a filtering experience. From this test we saw an increase in our primary and secondary KPIs.
Initial Impact
Filter engagement was promising and we saw increase in the following KPIs.
0.03% ↑
Increase In Product Page CTR
6% ↑
Increase In Add-To-Cart
4% ↑
Increase In Conversion
Continued Impact
Continued optimizations, such as integrating storytelling, product education and expanding our filter offering, led to further improvements.
Post launch we added the filter facets Product Type & Featured. We also added filter descriptions and content tiles to drive education and story telling on these pages.
15% ↑
Increase In Product Page CTR
23% ↑
Increase In Add-To-Cart
26% ↑
Increase In Conversion