In AngelList's investor matching system, how do we weigh the benefits of showing more potential matches against the risk of overwhelming users with too many options?

Introduction

The trade-off we're examining today is how to balance showing more potential matches in AngelList's investor matching system against the risk of overwhelming users with too many options. This scenario touches on key aspects of user experience, engagement, and the platform's core value proposition. I'll approach this by analyzing the product context, identifying key metrics, designing experiments, and providing a data-driven recommendation.

Step 1

Clarifying Questions (3 minutes)

• Context: I'm assuming this is for the main AngelList platform, not a specific vertical. Could you confirm if we're looking at the entire investor-startup matching ecosystem or a particular segment?

Why it matters: Helps focus the analysis on relevant user groups and features Expected answer: Entire ecosystem Impact on approach: Would require a more holistic solution considering various user types

• Business Context: Based on AngelList's model, I'm thinking this directly impacts deal flow and platform liquidity. How critical is increasing match volume to our current revenue targets?

Why it matters: Aligns solution with business priorities Expected answer: High priority, core to revenue model Impact on approach: Would justify more aggressive testing and resource allocation

• User Impact: Considering the diversity of investors on the platform, I'm curious about our user segmentation. Can you share insights on how matching preferences vary across different investor types (e.g., angels vs. VCs)?

Why it matters: Informs personalization strategies in the solution Expected answer: Significant variation in preferences and behaviors Impact on approach: Would lead to a more nuanced, segment-specific recommendation

• Technical: Given the complexity of matching algorithms, I'm wondering about our current system's scalability. What are our technical constraints in terms of processing more matches or implementing more sophisticated filtering?

Why it matters: Determines feasibility of potential solutions Expected answer: Some limitations, but room for optimization Impact on approach: Would influence the complexity of proposed changes

• Timeline: Considering the potential impact on core platform metrics, I'm curious about the urgency of this initiative. Are we looking at a near-term optimization or a longer-term strategic shift?

Why it matters: Affects the scope and depth of the proposed solution Expected answer: Mid-term priority, looking for sustainable improvements Impact on approach: Would balance quick wins with longer-term strategic changes