Your manager at Netflix asks about Ratings: should we implement more Netflix rating factors that slow suggestions, or maintain current recommendation speed?

Introduction

The trade-off we're examining today is whether Netflix should implement more rating factors to enhance our suggestion algorithm, potentially slowing down recommendation speed, or maintain our current recommendation speed with the existing factors. This decision is crucial as it directly impacts user experience, engagement, and ultimately, our retention rates.

I'll approach this analysis by first clarifying the context, then examining the product ecosystem, identifying key metrics, designing an experiment, and finally providing a recommendation with next steps.

Step 1

Clarifying Questions (3 minutes)

• Based on recent user feedback, I'm thinking this might be driven by a perceived lack of personalization. Could you share any insights on user satisfaction with our current recommendation system?

Why it matters: Helps understand the urgency and scale of the problem Expected answer: Slight decline in satisfaction scores related to content discovery Impact on approach: Would influence the priority of implementing changes

• Considering our content acquisition strategy, I'm curious about the balance between recommendation accuracy and speed. How does this align with our content investment priorities?

Why it matters: Ensures alignment between recommendation system and content strategy Expected answer: Increasing focus on niche content requiring more precise recommendations Impact on approach: Might justify more complex rating factors despite speed trade-off

• Looking at our technical infrastructure, I'm wondering about the current load on our recommendation servers. What's our current capacity for handling more complex algorithms?

Why it matters: Determines feasibility of implementing more sophisticated rating factors Expected answer: Some headroom, but significant increase would require infrastructure upgrades Impact on approach: Would influence the scope and timeline of potential changes

• Considering our product roadmap, I'm thinking about how this fits with other planned features. Are there any upcoming releases that could impact or be impacted by changes to our rating system?

Why it matters: Ensures coordination with other product initiatives Expected answer: Plans for a UI refresh in Q3 that could incorporate new recommendation features Impact on approach: Might suggest timing the rating system changes with the UI update

• Given the potential impact on user experience, I'm curious about our current A/B testing capacity. How many concurrent tests can we run on recommendation changes?

Why it matters: Determines our ability to iterate and validate changes quickly Expected answer: Capacity for 3-4 major recommendation tests simultaneously Impact on approach: Would influence the granularity and pace of our experimentation