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Company focus

Splitgate
Product Trade-Off Hard Member-only

For Splitgate's matchmaking system, should we prioritize faster queue times or more precisely balanced skill-based matches?

Prepared by NextSprints

15 mins
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Trade-Off Analysis Metrics Definition Experiment Design Gaming Esports Online Multiplayer User Experience Product Strategy Gaming Competitive Balance Matchmaking
Product Management Trade-Off Question: Balancing queue times and skill-based matchmaking in Splitgate

Introduction

The trade-off between faster queue times and more precisely balanced skill-based matches in Splitgate's matchmaking system presents a critical decision point for the game's success. This scenario touches on core aspects of player experience, retention, and competitive integrity. I'll analyze this trade-off by examining user impact, technical considerations, and business implications to provide a strategic recommendation.

Analysis Approach

I'd like to outline my approach to ensure we're aligned on the key areas I'll be exploring in this analysis.

Step 1

Clarifying Questions (3 minutes)

  • Context: I'm assuming Splitgate is facing player retention issues. Could you confirm if this is correct, and if so, what's our current churn rate?

Why it matters: Helps prioritize between quick matches (engagement) and fair matches (satisfaction) Expected answer: Yes, churn rate is around 20% monthly Impact: High churn would lean towards faster queue times to keep players engaged

  • Business Context: Is Splitgate primarily monetized through in-game purchases or ad revenue?

Why it matters: Influences whether we prioritize session length or frequency Expected answer: Primarily in-game purchases Impact: Would focus on player satisfaction and long-term engagement over short-term metrics

  • User Impact: What's the current distribution of player skill levels? Are we seeing a significant skill gap?

Why it matters: Affects the importance of precise skill matching Expected answer: Wide skill distribution with a noticeable gap between casual and competitive players Impact: Large skill gap would emphasize the need for more precise matching

  • Technical: What's our current matchmaking algorithm's efficiency? Are there any scalability concerns?

Why it matters: Determines feasibility of improving matching precision without sacrificing speed Expected answer: Algorithm is moderately efficient but struggles during peak hours Impact: Limited scalability would constrain our ability to improve precision without affecting queue times

  • Timeline: Is there a specific upcoming event or season launch that this decision needs to align with?

Why it matters: Helps set implementation timeline and prioritize short-term vs. long-term solutions Expected answer: Major tournament in 3 months Impact: Short timeline might favor quicker, incremental improvements to queue times

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Updated Mar 29, 2025