Filtering Paradigms

Filtering Paradigms

Preface:

Wayfair, for many years, has had a Left Hand Navigation (LHN) or a Vertical filtering experience. When I started, the company was working on testing into a new filtering experience, which was called Horizontal Filters. When tested, Horizontal Filters increased our core KPI's, including increasing Add to Cart.

Part of this project is still on-going and more information will be added after launching. 

 

The Task: 

I was given the task to look at Wayfair's filtering experience and make recommendations to help accommodate for an upcoming projects.  

After speaking with my Product Owner, and her Director, we decided to not only look at recommendations for the specific project, but to look at our filtering experience holistically and find room for improvement. We had just rolled out our new filter layout, but had not done much improvement since then. 

 

Starting: 

When starting this project I knew I would need concrete evidence to make any changes to the Browse experience on site. 

I started simply enough. I submitted a test to our user testing site.  We tested users who were in market for Décor, and who were in our core demographic. We gave them a simple task: find a piece of wall art of wall décor that they would consider buying for their home.  

 

User A accidentally closes filters by moving mouse. Then clicks Architect Filter.

User A is unsure if they have applied the Architect Filter due to page load time. Repeatedly tries to reopen filter. 

From that I was able to make video clips of every place a user had trouble filtering or finding what they were looking for. This gave me physical evidence of user problems on site.  This I was able to share with my Product Owner, but also our Frontend Engineers.  

Next, for my larger long term recommendations, I did a completive analysis. I looked at 40 E-commerce sites, who were either direct competitors, or who were large scale distributors similar to Wayfair.  This gave my product owners a good feel for the consistency of Filtering Paradigms and where Wayfair had room to improve. 

Competitive Analysis

 

Just Enough: 

At this point I had just enough research to start my Wireframe explorations for the long term recommendations, and plenty of bugs to flag to engineering for simple improvements. 

We were able to implement quick fixes that have helped mis-clicking on filters, and were able to improve user frustration around not knowing which filters were clicked.  

I was also able to look at layouts which we could use for our upcoming filter projects, and which ones long term we should consider using. (Examples will be posted after launch of projects)

These gave my Product owner enough information to guide her roadmap in the direction she see's success in. This way we can incrementally test into our future ideas.  

Examples:

 

I was able to suggest a fix that had a low engineering lift that would prevent users from mis-clicking. 

 A much smaller goal from this project was to condense the information so that product could be as high as possible on the page.  I suggested a few small shifts in content to acheive this.

A much smaller goal from this project was to condense the information so that product could be as high as possible on the page.  I suggested a few small shifts in content to acheive this.

 Focusing on Search was a early recommendation from this study. Which in theory sounded good, but with site data on the existing filter we were able to de-prioritize it based on low user engagement, and the amount of engineering work needed to get it up and working efficently. 

Focusing on Search was a early recommendation from this study. Which in theory sounded good, but with site data on the existing filter we were able to de-prioritize it based on low user engagement, and the amount of engineering work needed to get it up and working efficently.