Deep Dive
Research
Research
Understanding the Opportunity Space
To explore how referencing could better support reading, I led a discovery phase spanning competitive analysis, behavioral research, and iterative user testing.
Rather than optimizing existing tools, the goal was to understand how referencing should work within a digital reading experience.
What we explored
I analyzed patterns across adjacent products—including Apple Books, Notion, Google Docs, and media platforms like Netflix and Apple TV—to understand how primary content and supporting tools coexist.
This revealed two key tensions:
PiP patterns worked well for passive consumption, where content is viewed at a glance
Interactive tools required stability and depth, especially when users needed to read, scroll, or zoom
Additionally, platforms like Notion and Apple Books demonstrated that users are comfortable with on-page tooling—so long as it enhances the primary task rather than distracting from it.
This challenged an existing assumption within Kindle: that preserving a “reading sanctuary” required minimizing all on-page UI.
What we learned from users
Through multiple rounds of user testing, a different pattern emerged.
Users didn’t just want access to saved content—they wanted to interact with it meaningfully.
They prioritized not losing their place in the book
They expected to scroll, zoom, and explore pinned content
They quickly created new workflows (e.g., pinning answers, building flashcards)
They welcomed on-page tools when they added clear utility
Critical insight
Referencing is not a passive behavior—it’s an interactive one.
This invalidated our initial assumption.
We had optimized for visibility (seeing content alongside reading), but users optimized for interaction (engaging with the content itself).
Design implication
This led to a key reframing:
The problem was not how to surface content
It was how to support interaction without disrupting reading
Which required moving beyond existing tool-based models entirely.
Ideation
Iterations to final design
Improved visibility, increased complexity
Anchored model, but over engineered for MLP
Balanced interaction model (MLP)
Final Experience
Define, Valitation, iterate
We started with low-fidelity wireframes to explore multiple layout directions. Each iteration was reviewed internally, then tested with users. Feedback was incorporated continuously, allowing the design to converge toward a clearer and more scalable solution.
What this means
These metrics show that readers didn’t just save content, they actively used it.
Referencing became part of the reading experience, supporting behaviors like studying, comparing ideas, and revisiting key information without breaking flow.
Product impact
Established a new interaction model for in-book experiences
Influenced broader investment in interactive and AI-powered reading features
Helped shift Kindle from linear consumption → active engagement
This work reinforced that meaningful innovation in reading isn’t about adding more features, it’s about rethinking how interaction fits within the experience itself.
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