EffortlessFit is a web extension that helps determine your clothing sizes whilst shopping online.
Online clothing customers (shoppers) need a more accurate method of determining their sizes for clothing items online.
With the increase of digitalization with the Covid-19 pandemic, more people were forced to shop online and realized the convenience of online shopping. In Canada alone, there was an 83.3 % increase in spending online on clothes in Canada as opposed to the 84% decrease in in-store clothing shopping. One of the biggest problems whilst shopping online has been inconsistent sizing across clothing brands. Customers are returning 40 % of what they buy online, due to mostly sizing issues. In a Times article, comparing the variation of size 8’s across different brands, Zara had a waist of 27.6 inches whilst Guess had a 29 inch waist for the same size. This is leading to higher customer frustrations because these size references are not standardized across fashion brands; at a cost to both retailers and consumers. Therefore, we need to address this issue of inconsistent sizing to allow users more confidence when shopping whilst taking the pressure off the customer’s hands.
I created a broader survey to understand a user’s online shopping process in relation to navigating their sizes. I wanted to understand the frequency of ordering ill-fitting clothes, considerations when determining size on a new site or familiar brand, and the effects of ordering ill-fitting clothes.
After conducting a survey using Co.Lab’s chat and personal contacts, 24 respondents were surveyed.
Six (6) users were further interviewed to understand the exact pain points and problem areas. 100 % of interviewees responded that the time taken to determine their sizes when shopping at new fashion brands decreases the relaxation of a shopping experience. They have to consider different viewpoints including reviews, material, external research (youtube/social media) which then increases the time to determine their sizes up to 200%. 66.7 % of interviewees also commented that current size recommendations from websites are inaccurate because they don’t consider body shape compositions or enough data to give a recommendation. 100% of interviewees also noted that inconsistent sizing across different brands is a problem in size determination because they couldn’t rely on their usual sizes in a different brand.
From the survey and the in-depth user interviews, there were two biggest insights that were determined:
Based on the target user’s pain points, I wanted to work on features that encompass all inputs required to determine a size of a clothing item– reviews, materials, body measurements. This led me to a web extension connected to a user’s browser.
EffortelessFit works by initially creating a user profile that prompts the user’s for their body measurements including: height, weight, waist, hips, chest, inseam, full height, waist to floor measurements e.t.c. For user’s not familiar with these measurements, a tutorial will be available to serve as a guide.
Once these measurements have been saved and a user is on a shopping website, EffortlessFit extracts the details of fabric of a selected clothing item to use in its analysis of size. Input from size fit reviews will be accommodated if available on the website but primarily EffortlessFit will collect user based size fit reviews on a ranging scale for ease of interpretation and for use in future purchases.
Finally, EffortlessFit should use all information to match up with the size chart of the shopping website and provide a recommendation as a popup to the user.
The biggest additional problem was how to monetize this product. Currently, it is marketed towards online customers, but could it be pivoted towards clothing brands as their size calculators to prevent loss of revenue ?
Co.Lab was a very interesting experience for me because I started it with minimal exposure to the product management world. There was definitely a learning curve in the beginning, but as the program went on, the core takeaways were easier to understand.
The biggest takeaway was that a PM needs to fully understand a problem space as it could hold potential solutions. Understanding the problem space includes the surveys made, the user interviews conducted, research and the analysis that leads to the state of the solution space. I had never really done anything like this before, so it was definitely out of my comfort zone, but learning how to properly ask the right questions to get information from a user was a highlight.
Also when creating a product, I needed to really learn how to hone in on the MVP and realize what features were the most important. The videos on deciding on the MVP really broke down how to go through this process. I was able to learn that you need the most basic version of your product, but it also needs to be fully functional to be able to provide value.
The section I struggled with the most was in the creation of goals and success metrics in the spec document, but I know this is something I’ll need to further read and work on personally.