Should NetEase prioritize expanding its music streaming catalog or improving recommendation algorithms?

Introduction

The trade-off between expanding NetEase's music streaming catalog and improving recommendation algorithms is a critical decision that could significantly impact user engagement and retention. This scenario touches on the core value proposition of a music streaming service: content breadth versus content discovery. I'll analyze this trade-off by examining the current product landscape, potential impacts, and experimental approaches to inform our decision.

Step 1

Clarifying Questions (3 minutes)

• Context: I'm assuming NetEase is facing increased competition in the music streaming market. Could you provide more context on our current market position and primary competitors?

Why it matters: Helps frame the urgency and potential impact of this decision. Expected answer: NetEase is a strong player but facing pressure from global giants entering the market. Impact on approach: Would influence whether we prioritize differentiation or feature parity.

• Business Context: Based on our business model, I'm thinking this decision could impact our revenue streams differently. How does our current revenue split between subscriptions and advertising?

Why it matters: Helps align the solution with our primary revenue drivers. Expected answer: 70% subscription, 30% advertising. Impact on approach: A higher subscription percentage might lean towards improving recommendations for retention.

• User Impact: I'm considering the different user segments we serve. Can you share insights on our user base composition, particularly the split between casual listeners and power users?

Why it matters: Different user segments may value catalog size vs. recommendations differently. Expected answer: 60% casual, 40% power users. Impact on approach: A higher percentage of power users might favor catalog expansion.

• Technical: Considering the scale of our platform, I'm curious about our current technical capabilities. What's our current recommendation system's performance in terms of accuracy and processing time?

Why it matters: Helps assess the potential gains from improving algorithms. Expected answer: Moderate accuracy with room for improvement, processing time is acceptable. Impact on approach: Significant room for improvement might favor algorithm enhancement.

• Resource: Thinking about implementation, I'm wondering about our team's current capacity. Do we have more bandwidth in content acquisition or machine learning engineering?

Why it matters: Helps determine which option is more feasible with current resources. Expected answer: Balanced capacity, but slightly more in machine learning. Impact on approach: Might lean towards algorithm improvement if ML team has more bandwidth.