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What is Hatch?

Hatch is an application built to bring content creators together. Creators are able to search for people with similar interests, particular levels of engagement, and certain follower counts to find others to collaborate with. Utilizing ML techniques, Hatch is able to learn what the user is targeting and can make recommendations for creators and collaborations based on what the user has matched with or disliked previously.

What is the problem?

There are so many content creators these days that it can be hard to meet others without getting lost in someone’s comments or DMs. Because of this, it can be difficult to solidify brand partnerships or set up collaborations with creators that have a similar following to your’s.

Design Process


A variety of research was conducted to inform prioritization of key features for Hatch. We carried out qualitative research on use cases for creator apps and did a competitive audit of adjacent collaboration apps (Intellifluence, SocialBook, IndaHash, and Collabor8) which provided us with insight into expected features, market standards, design patterns, and common use cases. Our research was then heavily focused on what creators prefer in terms of platform and monetization opportunities, as well as what it takes to achieve high engagement so we knew how to structure our filters. 


Based on the Preferred Platform graph, we know there is less of a demand for content monetization on TikTok and Youtube, but there are still creators that prefer to monetize that way. When looking at Instagram alone and seeing what it takes to achieve the highest engagement rates in the other graph, it's pretty widespread based on follower count. Despite having a higher follower count, it can take equal to, if not more, posts to achieve higher engagement rates if the creator is not above 250k followers.

Further research revealed that while TikTok isn't the top choice for monetization right now, it is very quickly growing. For example, Jennifer Lopez recently posted the same video on Twitter and TikTok. She has 45 million followers on Twitter and 5 million followers on TikTok. The video on Twitter got 2 million views (off 45 million followers). The video on TikTok got 71 million views (off 5 million followers). TikTok presents a much greater opportunity for higher engagement rates.

"TikTok influencers with 2.5 million followers or more charge around $600-1000 per post compared to $100-$200 for every 10,000-20,000 followers on Instagram."


Motivation & Drivers: 

  • Looking to grow follower count and engagement statistics.

  • They want to network with other creators and partner with those in a similar niche.


Pain Points: 

  • Not enough use cases to resonate the struggles they face in trying to grow their brand.

  • Lack of incentives to push the creator out of their comfort zone when trying to work with someone new.


  • Some creators may not take them seriously because they have a smaller portfolio and/or follower count.

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Motivation & Drivers: 

  • They want to expand their network and meet new creators.

  • They want to find new content avenues.


Pain Points: 

  • Lack of resources to effectively search for creators.


  • Opportunities getting lost in DMs.

  • Unable to effectively communicate with other creators without giving out personal information (phone number, email, etc) due to lack of organization in their inbox.

Motivation & Drivers: 

  • Discover new creators based on location, follower count, and engagement they’re targeting, as well as the type of partnership a creator is looking for.


Pain Points: 

  • Lack of resources for brands to discover new creators other than scrolling existing social media.


  • There are so many people that want to work with a particular brand; oftentimes their niche, engagement stats, etc don’t correlate with what the brand is targeting in their partnerships.

  • No good way to organize creators based on their work and what they bring to the table.


The Design

When I began designing, I knew I wanted to support a few key interactions, particularly around search filters since preferences and engagement ranged significantly based on platform. I also wanted to showcase how the user can see creators recommended to them based on previous search, engagement, and matches. Our matching system allows for the user to discover new creators, review their statistics and past projects, and decide whether they want to work with them. ML algorithms are learning the data points these users are both liking and disliking to subsequently make better recommendations. Once the core design was finalized, we looped in the content design and dev teams to see what was feasible and what needed to be adjusted to avoid challenges down the road when we were further along in the design process.


The engineering team recommended I change the landing page based on existing components to make production faster. In the case of Hatch, there was a constant back-and-forth dialogue between Design and Engineering as the product was being built due to time constraints.

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If I had additional time, I would...

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Create user tests with validating metrics.

Even though this project was a new product experimentation, it’s very important to make sure that I am able to showcase that I have thought about how I am able to measure my impact.

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Explore more in-depth UI trade-offs.

Under the time constraints, I was not able to fully investigate all the possible trade-offs for my users. I want to showcase that I make purposeful decisions that impact my solution.

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