Review the Runway


In just 1 month, we launched a new shopping solution to help all women find flattering clothing for their unique needs.


In spite of many emerging online shopping technologies, 50% of women still prefer to buy their clothing in-store. Women feel anxious and self-conscious about purchasing online because they lack the comfort of knowing an item will fit and flatter their unique bodies. Instead, they shop in-store, sacrificing access to an endless online selection and the convenience of browsing in their own home. Unfortunately, many online purchases end up returned.


Over the course of a rapid 1 month sprint, I worked alongside stakeholders to develop a product strategy and MVP that reimagines the shopping process. Using product reviews enhanced by ML, women shop a curated selection of clothing optimized by how they feel about their bodies. Our platform is a more personalized approach to shopping, asking users to shop based on their unique needs and concerns.This product was launched in November 2019.

My Role


Product Design, UI/UX, User Research


Nov 2019 - Dec 2019

Team Members

3 Dev
1 Business
1 Data
1 Design (that's me!)


Sketch, Principle, Framer X, Zeplin

Personal and body positive shopping
Find clothing across hundreds of brands that make you feel beautiful, no matter what size or shape.

A community of helpful, real women.
Share reviews to help other likeminded women of all ages and backgrounds. Get advice from real women that share your needs.

Clothing that shows off what you love.
Shop to accentuate your body, not fit a number. We know that feeling confident in clothing is beyond the ‘right’ measurements.

My Contribution

As the only designer, I led the design process using a human-centered methodology within business strategy, budget, and engineering constraints. review the runway process



I initially performed explorative research to justify introducing a new product to the space. My human-centered approach comprised of a thorough review of broad consumer trends, journey mapping, and individual research interviews with our target audience.

review the runway tools

Before diving in, I built alist of assumptions with stakeholders to validate through research. Below, I’ll present 3 key insights and what steps led to their discovery.

review the runway tools

review the runway tools

What are the pain points in the online shopping process?


I conducted semi-structured interviews with US women aged 18-50 about their online shopping behavior. We decided to scope the problem to this demographic since shopping needs change significantly between cultures and gender. These interviews were explorative, not prescriptive.Our goal was to uncover pain points in the shopping journey and identify product opportunities.

Observational Audit

I began with an observational audit of each woman's shopping flow. I took careful notes of their browsing behavior, and asked them to verbalize their thought process. When appropriate, I asked questions to clarify their attitudes and motivations. This exercise was a fantastic way to break the ice and lead into our explorative interviews.

Interview Guidelines & Thematic Coding

At the end of each interview, I summarized learnings into codes. After all interviews were completed, I reviewed all codes and revised those that didn’t capture the underlying consumer need.

Sentiment Mapping

Our team had assumed that the blocker for online shopping was decision paralysis in the discovery phase. To test this assumption, I asked shoppers to draw a sentiment graph representing their journey to a purchase, marking milestones in the process, and explaining their choices. To our surprise, users were most frustrated after they received their purchase, because they rarely fit like imagined.

Digging Deeper...

All interviewees did external research before deciding to make a purchase. All of these sources were social; this doesn’t come to much surprise given the global socialization of e-commerce. 87% of consumers begin their shopping journey with digital. (Salesforce)
Through conversations with extreme users, I discovered private groups where women shared candid photo reviews about online purchases. To join, they needed to be invited by an existing member with a unique code.
Through interacting with these groups, I observed a contribution economy of women ‘paying forward’ the guidance they received. Our interviews also revealed a growing distrust of sponsored endorsements on platforms like Instagram and Youtube, but these general users felt they had no other options. This insight ultimately informed our product and community strategy.

Visual Research Aids

After understanding that many women feel insecure about their online purchases, I conducted a follow up interview to understand more about this pain point. One exercise asked them to identify the body shape that they felt resembled them the most.
I found that women very rarely picked the person who shared the same numeric measurements as them (height, weight, bust size). Instead, women identified with body shapes that they perceived as having the same insecurities or highlights. Moreover, each woman’s last purchase was driven by this unique way of viewing their bodies.
For example, women that identified as ‘busty’ did not necessarily have large cup sizes. Instead, they described feeling insecure about their chest or wanting to show off their curves. Another woman, insecure about her shoulders, shared that she buys the same type of top because she didn’t know what other styles would “work” on her body.


Unique Women, Real Curves

We learned that online shopping is frustrating for women because it exasperates feelings of imperfection. Clothing often looks vastly different on their unique bodies than shown on traditional, airbrushed product photos.

Authentic Social Discovery through Contribution

We hypothesized that our demographic trusted influencer recommendations, but we learned that an inclusive contribution model led to more authentic and valuable social discovery. The most savvy shoppers turned to niche forums or invite- only groups for genuine recommendations.

The Right Fit is More than Accurate Measurements

Women don’t describe their bodies using standard numeric measurements. Instead, woman percieve their bodies in terms of what parts they love and don’t love. Ultimately, this influences what clothing they feel confident in and eventually purchase.

Storyboarding & Journey Mapping

I used these interview isnights to construct customer journey maps and storyboards. These allowed me to visualize pain points in the current process, and proved to be a valuable tool to communicate areas for opportunity and motivate stakeholders.

sentiment graph

Competitive Analysis

Our team we agreed that our main competitors were social platforms, not retailers. I then performed a competitive analysis of other social platforms for sharing the fit of clothing, and found that there was a gap in the market.

competitive analysis brands

I found that:

  • Tools like Youtube and Forumslacked the e-commerce tools necessary to lead to the end goal of a fulfilled purchase.
  • All tools suffered from out-of-date information that cluttered their platforms. Products that were no longer available for purchase were featured, which made all platforms difficult as shopping tools.
  • Tools like Instagram and Pinterestlacked important searchable metadata to find specific products.

User Types

To better guide our design and enable everyone on the team to empathize with our users, I further synthesized the interview results with the following personas. We used these to share insights with team members outside of product, and to guide our ideation.




I brought these findings back to stakeholders and we soon began ideating solutions. During early brainstorming, we competed for the best ideas. This allowed us tocollaboratively refine our ideas and consider its value within the holistic vision for the product.

After presenting research findings, both founders and myself brainstormed solutions together. I encouraged them to come up with as many ideas as possible, without considering feasibility.

We then went through the ideas and discussed engineering feasibility, eliminating features that were too complex. We eliminated ideas such as using computer vision to predict a person's body measurements based on a camera, which is extremely challenging even without time constraints.

We then assigned an ICE (Impact, Confidence, and Ease) score to each idea which allowed us to quickly compare cost and benefit. It was particularly effective in our brainstorming phase, since both business and engineering leads were active contributors to ideation and had full awareness of impact and ease. For example, we eliminated personalized recommendations because we deemed the impact and ease to be low, since users would be in a state of high-intent search (not discovery) and the extra time implementing a sophisticated ML recommender would take up too much engineering time.

After much discussion, we decided to first pursue UGC (user generated content) and then enhance the data using an ML algorithm trained on the UGC to fill in labeling gaps.

Trust the process...

After setting up a roadmap and aligning our objectives,I put together a full design brief that outlined the project goals, stakeholder comments, user insights, and key deliverable dates for the project. We reviewed it with the engineering team and made sure that everything we outlined was in scope and possible for the MVP. After synthesizing feedback from our prototypes, I went back and refined interactions, navigation patterns, and visual design for the final product. Many iterations later, we launched our MVP.



We launched the MVP in November and were excited about its adoption. At the end of the project, there were 350,000+ posts from users.

In the coming year, the team will be refining the MVP to make it even easier to help consumers shop based on what matters most to them. All so they can feel more confident about their purchases. We are continuing to Average session duration, iterate on the product and signficantly larger than officially launch an app in late most retail sites. 2020.



Advocating for User Experience

Within the narrow budget and timeline, I had to be proactive and advocate for the user experience. This required careful strategizing and prioritization to ensure a proper human-centered methodology was applied to improve our end product.

Timeline and budget

Our short timeline made it imperative to appropriately scope the problem and weigh the ROI of various product opportunities. We had to strip away secondary features and focus solely on refining our core use case.


I believe there are two types of design problems: 1) Optimizations and 2) Imaginations. Imaginations are open-ended, they force you to think outside the box and be creative and resourceful. There are no simple KPIs for success, no best practices. You have to be imaginative, trust your team, and believe in your research process to derive key product opportunities.

Project Learnings

Recognize your assumptions

Humans naturally make connections and fill in blanks. This skill can hurt product development by treating assumptions as objective truths. We must be proactive at identifying our assumptions, testing them, and proving their validity. Otherwise, we begin a project by building on a faulty foundation.

Seek Feedback Early and Often

Keeping the stakeholders and users consistently involved in testing solutions in whatever form (paper, low- fi or hi-fi) as early as possible will always lead to new learnings that can course-correct a misaligned vision.

Understand that Design is Iterative

Case studies often speak to the success of a project, but researcher can only strive to fully understand the problems a user faces, but in reality, we must wrestle with uncertainty, limited resources, and complex problems. However, the iterative design process allows us to invest little, learn significantly, and always move closer to a solution.