Libby – Social Discovery Prototype
Reframing Libby from a borrowing utility into a trusted discovery ecosystem.
Product Strategy • UX/UI • Rapid Prototyping • Usability Testing
Libby allows users to borrow ebooks and audiobooks from their local libraries. Through early research and journey mapping, our team identified a critical behavioral gap: users rarely open Libby to discover what to read. Discovery happens on TikTok, Goodreads, Amazon, or through friends. Libby functions as the checkout window, not the place where interest is cultivated.
Our sprint focused on a central question: How might we internalize discovery and build network effects so Libby becomes both the starting and ending point of the reading journey?
Defining the Opportunity
Collaborative Sprint • Journey Mapping • HMW Clustering
We began by mapping the end-to-end Libby experience, including inventory constraints, hold mechanics, and external recommendation behaviors.
Mapping the current journey revealed (1) Users often arrive with intent formed elsewhere, (2) Wait times and availability shape decision-making, (3) Users leave the app mid-session to validate recommendations, and (4) “Popular” lacked clarity compared to “Trending” or “Highest Rated.”
We translated these friction points into clustered How Might We opportunities across four themes: Onboarding and Filters, Supply and Wait Times, UI and Reading Experience, and Network and Discoverability.
While supply constraints were visible pain points, we prioritized Network and Discoverability because it offered high user value, clear differentiation, and feasible intervention without changing inventory.
Strategic Direction
Instead of treating personalization, reviews, and social features as isolated ideas, we reframed the solution as a cohesive discovery bundle:
- A personalized “Just For You” shelf
- A Follow feature for friends and authors
- Libby Book Clubs for lightweight discussion
- Availability-aware signals, such as "Trending" and "Available Now"
Our hypothesis was that credible social signals reduce decision friction more effectively than ratings alone.
This reframes Libby from a transactional tool into a community-informed ecosystem.
Prototype
Figma Make • AI Assisted Build • GitHub Deployment
To rapidly test this direction, I built a high-fidelity interactive prototype. Using Figma Make, I recreated Libby’s interface and layered in new discovery forward flows. The prototype was refined collaboratively and deployed via GitHub for live usability testing.
The experience tested a unified flow:
User opens Libby → lands on a personalized discovery feed → sees what trusted accounts are reading → evaluates lightweight social proof → chooses to borrow or place a hold.
Rather than validating feature preference, we tested behavioral shift. (Would users rely on social forward discovery instead of direct search?)
Usability Testing and Behavioral Insights
We conducted three moderated usability sessions focused on open-ended discovery.
Key findings:
- "Trending" and "Highest Rated" felt more dynamic than "Popular"
- Social features generated the strongest excitement
- Following authors and friends reduced the need to leave the app
- Book Clubs increased perceived engagement beyond borrowing
- Privacy controls were essential, and users preferred opt-in sharing
While personalization was expected, social discovery created the most confidence and engagement. The strongest signal was that users wanted trusted context more than more metadata.
System Thinking: Discovery Loop
The proposed system integrates: (1) Personalization inputs such as reading history and engagement signals, (2) Social graph signals including friends, authors, and book clubs, and (3) Availability constraints such as copies left, wait time, and trending demand.
These elements form a discovery loop:
Better social signals → More confident borrowing → Increased engagement → Stronger recommendation signals.
Rather than solving supply constraints directly, the system reframes wait time as one variable within a richer decision process.
Outcome
The sprint demonstrated that discovery can be meaningfully improved without altering inventory supply. By reframing Libby as a socially informed ecosystem rather than a checkout utility, we identified a path to reduce off-platform leakage, increase engagement through credible context, and build defensible network effects over time. The prototype validated strong user interest in social forward discovery, with privacy-first design as a critical requirement for future iteration.