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Product Management Trade-off Question: Netflix recommendation system balancing content diversity and relevance

Your director at Netflix asks about Recommendations: should we show more Netflix diverse content with lower relevance, or focus on higher-relevance recommendations?

Product Trade-Off Hard Member-only
Data Analysis Product Strategy Experimentation Streaming Services Entertainment Technology
User Engagement Personalization Netflix Recommendation Systems Content Strategy

Introduction

The trade-off we're examining today is whether Netflix should prioritize showing more diverse content with lower relevance or focus on higher-relevance recommendations. This scenario touches on the core of Netflix's recommendation system, balancing user engagement with content diversity. I'll approach this analysis by examining the business context, user impact, technical considerations, and potential experiments to inform our decision.

Analysis Approach

I'd like to start by asking a few clarifying questions to ensure we're aligned on the key aspects of this trade-off. Then, I'll walk through my analysis framework, covering product understanding, hypothesis formation, metrics identification, experiment design, and ultimately, a recommendation with next steps.

Step 1

Clarifying Questions (3 minutes)

  • Context: I'm thinking about the current state of our recommendation system. Could you share any recent changes or performance metrics that might be driving this consideration?

Why it matters: Helps understand if there's a specific issue we're trying to address. Expected answer: Recent metrics show high engagement but low content diversity. Impact on approach: Would focus on balancing diversity without sacrificing engagement.

  • Business Context: Based on our strategic priorities, I assume increasing watch time and subscriber retention are key goals. How does content diversity factor into our current business objectives?

Why it matters: Aligns solution with overall business strategy. Expected answer: Content diversity is important for long-term subscriber retention. Impact on approach: Would emphasize long-term benefits in the analysis.

  • User Impact: I'm curious about our user segments. Are we seeing different behaviors or preferences across various user groups regarding content diversity?

Why it matters: Helps tailor recommendations to different user needs. Expected answer: Newer subscribers tend to explore more diverse content. Impact on approach: Would consider segmented recommendation strategies.

  • Technical: Considering our current recommendation algorithm, what's the feasibility of incorporating a diversity factor without major overhauls?

Why it matters: Determines the scope and timeline of potential changes. Expected answer: Moderate changes required, but feasible within current architecture. Impact on approach: Would focus on incremental improvements rather than complete redesign.

  • Resource: In terms of our product and engineering capacity, do we have dedicated resources for this initiative, or would it compete with other priorities?

Why it matters: Helps scope the solution within available resources. Expected answer: Limited resources available, needs to be balanced with other projects. Impact on approach: Would prioritize high-impact, low-resource solutions.

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