Comparative Research and Participatory Roadmaps

Find our 2×2 competitor matrix at: https://www.figma.com/file/j81l7vmXWVu47TgFoauqfy/2×2-Matrix?node-id=0%3A1

EDIT: Screenshots for participatory roadmaps are not inserting into the post correctly, so please view them in this pdf: Comparative Research and Participatory Roadmaps

The two axes we’ve chosen are:

• Noisy vs. clean: How noisy is the amount of data that is shown to the user? How easy is it to sift through all the information? We believe that many sources of inspiration/fashion out there have a lot of noisy data–we hope to provide a source of clean data through Quinn.
• General vs. personalized: How personalized is the data/content that is presented to the user? We believe that people have their individual sense of style, so Quinn aims to present personalized inspiration to our users.

Our competitors:

1. StitchFix
   1. Personal styling service that uses your preferences and social media outlets to select and ship five items (you choose shipping frequency)
   2. Recommendations are made with the combination of a professional stylist and data science/machine learning 
2. Stylebook
   1.  #1 paid Lifestyle app on the App Store
   2. Wardrobe organization and closet management tool that offers 90+ features spanning outfit collage creator, owned clothes importing, outfits using shared item features, and outfit repeating features
   3. Primarily a content management system, rather than a recommendation tool
3. Amazon
   1. #1 retailer of e-commerce, which includes clothing bought online
   2. Large variety of vendors hosting their products on Amazon platform
   3. Amazon offers the most competitive pricing for goods and best deals for delivery/returns (which is very important for buying clothes online)
4. ThredUp
   1. #1 thrifting app on the market
   2. High quality clothes at low prices
   3. Allows for any user to sell their clothing 
5. Pinterest
   1. Image sharing and social media service, users can create boards full of images
   2. Many of the interviewees we talked to mentioned Pinterest as a place where they went to in order to get outfit / style inspiration
   3. Feed is curated by users
   4. Search bar and save feature similar to what we hope to create with Quinn
   5. Two-step moderation process: AI-based moderation and human moderators
6. Instagram
   1. Image/photo sharing service, feed is generated by users
   2. Can save / like / comment on photos
   3. Searching keywords can allow you to pull up similar posts, hashtags are a main way to search
   4. Heavily moderated by AI
7. Google
   1. Commonly used to search for fashion inspiration or outfits to build around certain items
   2. Uses web scraping to produce results
   3. Results can often be noisy or low quality
8. TikTok
   1. Many fashion trends/microtrends are birthed from TikTok
   2. Influencers and everyday creators post outfit inspiration
   3. Uses algorithm to learn user preferences and cater to tastes

Our participatory roadmap interviews can be found here: https://www.figma.com/file/bxHQku7LLYOz8ypTO16kLL/Participatory-Roadmaps?node-id=0%3A1

Here is the Quinn team’s roadmap:

For our participant interviews, we targeted interviewees in two categories: interviewees who showed a strong interest in fashion, and interviewees who are working on developing their fashion sense. Below are the potential users we interviewed and their corresponding maps.

Cathy

1. Cassie: 21 year old Asian female college student who is working on developing a sense of style but wants to find outfits that match her height, body type, skin tone, etc.
   1. Key insight: Cassie feels that the recommendations are unclear or may be irrelevant, and rather, finds the value in the search and feed. She imagines searching for something like “minimalistic work outfits for apple body shape,” and that would essentially serve as recommendations that she could manually filter through. The “For You” style feed would also essentially just be presenting recommendations.
2. Kasturi: 21 year old female college student who is generally interested in fashion
   1. Has a lot of clothes, but it’s hard to match, tend to repeat combos, tend to be newer clothes or more basic clothes, leaving older or patterned clothes unworn; if she had better information using pictures that look similar to those pieces of clothing, I would use those more
   2. Had an idea to upload your own outfits
   3. Prefers viewing multiple views at once rather than one-by-one scrolling through
   4. Key insight: Kasturi believes the machine learning and recommendations part is the key distinguishing factor of the app, and therefore should be prioritized.

Joe

1. Tracy: 20yo female college student
   1. Tracy put the “purchasing links” in the soon categorie. Her reasoning was “Well, if I can’t buy it off the app, then I would just use Google to search and then buy off a website”. She seemed to be very satisfied with using Google as a search for clothing style / outfits.

1. Phuc: 20yo female college student
   1. Phuc put “FOY feed of content” in later rather than soon. She said that, what she wanted was an app that allowed you to search across stores. She said that TikTok and Instagram already had great FOY pages, which included fashion

Michelle

1. Jay: 22yo male new graduate / young adult who cares a lot about the way he looks / his style. Jay invests a lot in designer pieces and high-end brands, but wants to make sure he’s spending his money on the right pieces. 
   1. Key insight: Jay wanted almost all of the features either soon or later, rather than much later. He agreed mostly with the Quinn team on which features to implement soon rather than later. He mentioned that having a “save” feature is less important because he cares more about getting a general “idea” of what the feed is recommending him than following specific posts.
2. Derrick: 23yo male software engineer based in New York / struggling to find what to buy next for his closet, unsure of what he needs
   1. Key insight: Derrick prioritized features that would help recommend styles and items to buy over having the app learn his style. We hypothesize that this is due to him still trying to figure out his own style. He especially cares about having a feature that would allow him to compare the same item across different stores–he mentions that he cares about getting the most value for an item, so he wants to find the best deal.

Annie

1. Carina: 20yo college student who is into fashion and loves trying out new styles
   1. Key insight: Carina cares about features that can show her new styles over features that help her catalog her existing clothes. She has a good sense of her wardrobe and how she can style it already, and she hopes to learn more about new styles she can incorporate into it.
2. Kevin: 20yo college student who is decently satisfied with his style but is open to putting in more effort
   1. Key insight: Kevin really liked the feature of the app learning your style based on user activity to suggest more tailored outfits, because it would take out a lot of guesswork involved with finding new clothing and styles and figuring out if it would fit in his wardrobe. Other than that, he also agreed with Carina’s reasoning preferring features suggesting new styles over styling existing clothes.