E-Commerce Search Redesign

Overview

Role | UX Researcher/Designer

Research Methods | Qualitative Survey, Competitive Analysis, Google Analytics

Tools | Wordpress, Figma, Mailchimp

Client | Oldpatterns.com

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Background

Oldpatterns.com is an e-commerce business selling thousands of one-of-a-kind vintage items. The challenge came in finding the best way for users to sort and refine items with such variety.

My Role

As the sole UX Researcher and Designer, it was my task to guide the website redesign strategy and champion user feedback to drive design decisions.

Research Goals

To understand the project scope, I mapped the current site structure and conducted a stakeholder interview with the company CEO, and developed the following research goals:

Evaluate the different ways users search for vintage patterns online
Discover current user expectations navigating Oldpatterns.com to uncover pain points and expectations when searching for patterns

Research Methods

Given the time and budget constraints, I chose the following research methods:

Qualitative survey with open-ended questions
Competitive analysis to understand the landscape and estabilished UX best practices

Recruitment

Analysis

After I received 70+ substantive responses, I analyzed the data to synthesize key insights. Visualizing a problem helps me best gain insights, so I used Figma and FigJam to sort key themes. I organized the insights found in the open-ended questions by motivation, pain point, and behavior. I then sorted these into key themes and prioritized themes with the largest representation.

Key Pain Points

Finding Sizes

“The difference in vintage and modern sizing is the worst offender to easy searches.”
Finding their pattern size is the highest priority when searching patterns.
Unclear Images

Clear product images build confidence in the quality of one-of-a-kind items. Without images of the back of the pattern, users lack sizing context.
Unclear Filters

Users are looking for patterns from a specific time period and expect eras to sort by era.

Competitive Analysis

Next, I conducted a competitive landscape to understand the competitive landscape outlined by user feedback. This gave me a chance to weigh the strengths and weaknesses of different design solutions and understand what users expect from an e-commerce experience.

Solution Prioritization

Finally, I outlined different design solutions that incorporated key research insights:

Redesign Site Architecture- Largest time & money commitment and would require additional research
Filter by Product Era and Size- Improve UX experience without complete rehaul

I sat down with the website owner to prioritize the best solution for the budget and timeline, and we decided to move forward with a filtering improvement with the given constraints.

Design Output

Before: Users had to navigate hundreds of subcategories to find products.

After: Filtering allows users to quickly find products that match their needs.

Business Impact

Doubled sales year over year - Site is easier for users to navigate
Increased order value by 37% - Users are able to find and add additional patterns

Reflections

What Went Well

I was able to prioritize the highest impact research with few resources
User centered design improvements were well received and led to increased sales

Challenges:

As the researcher, designer, and developer I wasn’t able to spend as much time understanding the problem
Participation was limited so I had to focus on only one demographic

With More Time I Would:

Conduct one-on-one usability testing on the new designs to understand pain points and iterate on designs.
Tackle navigation challenges through card sorting and tree testing
Expand recruitment criteria to include less experienced users