Should Rakuten's e-commerce platform prioritize expanding its product catalog or improving personalized recommendations to enhance user engagement?

Introduction

The trade-off between expanding Rakuten's e-commerce product catalog and improving personalized recommendations presents a critical decision point for enhancing user engagement. This scenario touches on the core aspects of Rakuten's platform strategy, balancing breadth of offerings with depth of user experience. I'll analyze this trade-off by examining the current product landscape, potential impacts, and data-driven decision-making processes.

Step 1

Clarifying Questions (3 minutes)

• Context: I'm thinking about Rakuten's current market position. Could you share our market share compared to major competitors like Amazon or local players?

Why it matters: Helps determine if we need to focus on breadth or depth to compete effectively. Expected answer: Rakuten has a strong position in Japan but faces increasing competition. Impact: A weaker position might favor catalog expansion, while a stronger one could prioritize personalization.

• Business Context: Based on our revenue model, I assume a significant portion comes from transaction fees. Is this correct, and are there other major revenue streams we should consider?

Why it matters: Aligns solution with primary revenue drivers. Expected answer: Transaction fees are primary, with advertising as a secondary stream. Impact: High reliance on transaction fees might favor catalog expansion for more transactions.

• User Impact: I'm curious about our user retention rates. Do we see higher churn in any particular user segments?

Why it matters: Identifies areas where improved engagement could have the most impact. Expected answer: Higher churn among new users or in certain product categories. Impact: High new user churn might prioritize personalization to improve onboarding experience.

• Technical: Regarding our recommendation engine, what's our current capability for real-time personalization?

Why it matters: Assesses feasibility and potential impact of improving recommendations. Expected answer: Basic capability exists, but significant room for improvement. Impact: Limited current capability might suggest focusing on this area for quick wins.

• Resource: Can you give me an idea of our current team structure and any constraints in engineering or data science resources?

Why it matters: Determines feasibility of implementing either option effectively. Expected answer: Balanced team, but limited specialized ML engineers. Impact: Resource constraints might favor the less technically demanding option.