Case study · Entertainment · 16 Weeks

Tubi - Personalization 2.0

Tubi - Personalization 2.0

Tubi - Personalization 2.0

Role

Lead Product Designer

Platform

Mobile (iOS & Android)

Duration

2 Years (25+ iterations)

This overview is intentionally designed for a quick read in under 3 minutes. For the full story, please review the attached case study deck.

This overview is intentionally designed for a quick read in under 3 minutes. For the full story, please review the attached case study deck.

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I led the end-to-end product design strategy, building personalization from the ground up. I worked closely with product, engineering, and data science to shape the roadmap, validate ideas through experimentation, and align the team around a long-term vision. The outcome was a more personalized experience that helped users discover relevant content much faster.

I led the end-to-end product design strategy, building personalization from the ground up. I worked closely with product, engineering, and data science to shape the roadmap, validate ideas through experimentation, and align the team around a long-term vision. The outcome was a more personalized experience that helped users discover relevant content much faster.

Tubi Personalization preview

Starting point

Personalization didn’t exist when I joined the team. Finding something to watch became increasingly difficult as the catalog grew to over 100k+ titles. Every user saw the same experience, regardless of their interests, creating an opportunity for us to make content discovery more relevant while helping the business increase engagement and viewing time.

Phase 1 : Proving the value

Rather than redesigning the experience all at once, I identified key moments throughout the journey where we could collect meaningful signals. Each experiment helped us validate assumptions while gradually improving recommendation quality.

Design explorations

I explored three interaction patterns to optimize onboarding for speed, focus, and recognition.


• Pills — Fast and playful, but difficult to scan.

• List — Familiar and detailed, but slower to browse.

• Grid — Fast to scan, recognizable, and scalable.

Phase 2 : The Framework

Our initial experiments proved personalization worked, but they also exposed a limitation—we were asking users to define themselves only once. The bigger opportunity was building a system that continuously learned from their evolving behavior.

This incremental approach increased completion from 66% to 79% while growing preference signals from 3 to 10+ selections. Each iteration built on previous learnings, refining both the interaction model and the quality of signals captured.

Takeaway

Personalization evolved through a series of carefully measured experiments rather than a single solution.

Results

Personalization became a core pillar of Tubi's discovery experience, reducing time to relevant content through continuous experimentation and iterative improvements. The initiative earned executive support, expanded beyond onboarding into multiple product surfaces, and established a scalable foundation for future personalization.

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Back to work

If you think I'd be a great fit for your team, I'd love to connect.
Email me: agenticram@gmail.com

© 2026 Ram

If you think I'd be a great fit for your team, I'd love to connect.
Email me: agenticram@gmail.com

© 2026 Ram

If you think I'd be a great fit for your team, I'd love to connect.
Email me: agenticram@gmail.com

© 2026 Ram