Hatch
"Connecting influencers, fueling brand growth"
Los Angeles, California
United States
Last login:
9/29/2025
Hatch is in your extended network
The Research
Early Exploration:
Because Hatch aimed to solve a real discovery problem for creators, I invested significant time in research before moving into design. My process included:
1. Competitive Analysis
I reviewed existing platforms used by creators — from social networks like Instagram and TikTok to influencer marketplaces.
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Found that search and discovery tools were shallow (limited to hashtags or follower counts).
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Observed that collaborations often happened organically in comments or DMs, making them difficult to formalize or scale.
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Noted a lack of personalization — users were often presented with broad lists, not curated suggestions.
2. Creator Interviews & Informal Surveys
I spoke with creators at different audience sizes (micro to mid-tier) to uncover pain points.
Key themes included:
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Relevance over reach: Many preferred smaller but engaged collaborations over “big names” with low ROI.
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Transparency matters: Creators wanted clear signals of engagement rates, follower overlap, and content style.
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Time is scarce: Many found searching for collaborators too time-consuming given inconsistent results.
3. Personas & Use Cases
From research, I developed lightweight personas to anchor the design process:
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The Growth Seeker: Focused on finding peers with similar audiences to cross-promote.
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The Brand Collaborator: Looking for partnerships that align with their niche and values.
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The Community Builder: Prioritizing long-term relationships over one-off deal
4. Key Insights
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Creators value quality of connections more than sheer volume of options.
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Platforms must make discovery fast, transparent, and trustworthy.
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Machine learning personalization could uniquely differentiate Hatch by cutting through noise and surfacing relevant matches.
Quest Analytics Introduction
Overview:
Hatch is a platform designed to help content creators find meaningful collaborations with other influencers and brands. By leveraging machine learning, Hatch personalizes recommendations based on shared interests, engagement, and past matches — helping users cut through the noise of endless feeds and overlooked DMs.
Problem Statement:
With millions of creators online, building authentic partnerships is harder than ever. Discovery often relies on chance encounters in comment sections or direct messages that are ignored. This fragmentation makes it difficult for creators to:
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Connect with peers who share similar audiences.
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Secure brand deals aligned with their niche.
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Build lasting, mutually beneficial partnerships.
Hatch was designed to close this gap by making collaboration discovery simple, relevant, and scalable.
The Process
Design Iteration:
1. Ideation & Wireframes
I sketched initial flows to explore how creators might:
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Search and filter by interests, follower size, and engagement.
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Receive intelligent match suggestions.
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Evaluate profiles quickly for partnership fit.
2. High-Fidelity UI Design
I translated sketches into high-fidelity wireframes and mockups, emphasizing:
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Clean profile layouts highlighting metrics that matter (engagement rate, niche).
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Recommendation carousels powered by machine learning.
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Consistent UI components from our design system for efficiency and scalability.
3. Collaboration with Engineering
To stay aligned with project timelines, I partnered closely with engineers. They recommended reusing components from the evolving design system — ensuring both consistency and faster development.
After ideation, I participated in some post-design testing to validate:
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Search and filter usability.
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Clarity of recommendation results.
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Ease of profile comparison for collaborations.
Feedback confirmed the value of personalized suggestions but highlighted future opportunities to better visualize performance metrics.
Usability Testing:
Hatch successfully delivered:
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A platform where creators can discover collaborators and brands more efficiently.
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Personalized, machine-learning powered matches that improved relevance over time.
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A scalable design system foundation to support future features.
Outcome:
While I’m proud of delivering research and UI design that directly influenced Hatch’s launch, with more time I would have explored:
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Dashboards for engagement insights: Allowing creators to compare performance across multiple profiles.
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Deeper usability testing cycles: To refine features before launch.
This project reinforced the importance of balancing innovation with feasibility — leveraging design systems allowed us to ship faster without sacrificing quality.
Reflection:
