Should Vivino prioritize expanding its wine selection or improving the accuracy of existing wine ratings?

Introduction

The trade-off question at hand is whether Vivino should prioritize expanding its wine selection or improving the accuracy of existing wine ratings. This scenario involves balancing the breadth of product offerings against the quality of user-generated content, which are both crucial aspects of Vivino's value proposition. I'll analyze this trade-off by examining the product context, potential impacts, key metrics, and experimental approaches to inform a strategic recommendation.

Step 1

Clarifying Questions (3 minutes)

• Context: I'm thinking Vivino's current market position is strong, but there might be competitive pressures. Could you share insights on our market share and primary competitors?

Why it matters: Helps gauge the urgency of expansion vs. quality improvement Expected answer: Market leader with 70-80% share, facing new entrants Impact: High market share might favor improving accuracy over expansion

• Business Context: Based on Vivino's business model, I assume wine sales are a primary revenue driver. How significant are affiliate commissions from wine purchases compared to other revenue streams?

Why it matters: Determines whether expansion or accuracy is more likely to drive revenue Expected answer: 60-70% of revenue from wine sales commissions Impact: High dependence on sales would lean towards expansion

• User Impact: Considering user behavior, I'm curious about the correlation between ratings and purchase decisions. Do we have data on how rating changes affect conversion rates?

Why it matters: Indicates the potential ROI of improving rating accuracy Expected answer: 0.5 star increase leads to 10-15% higher conversion Impact: Strong correlation would prioritize accuracy improvements

• Technical Feasibility: Regarding our rating system, I'm wondering about the current accuracy level and the technical challenges in improving it. What's our current margin of error, and what resources would be needed to significantly improve it?

Why it matters: Assesses the feasibility and potential impact of accuracy improvements Expected answer: Current accuracy is 85%, significant improvement requires ML investment Impact: High improvement potential would favor accuracy focus

• Resource Allocation: Thinking about our team structure, I'm curious how our resources are currently split between catalog expansion and rating system maintenance. What percentage of our product and engineering teams are dedicated to each area?

Why it matters: Indicates current priorities and potential for reallocation Expected answer: 60% on expansion, 40% on rating system Impact: Even split might suggest maintaining current balance