How should Dream Games balance increasing difficulty in Royal Match to drive in-app purchases versus maintaining player retention?

Introduction

Balancing game difficulty in Royal Match to drive in-app purchases while maintaining player retention is a critical trade-off for Dream Games. This scenario involves carefully adjusting the game's challenge level to encourage monetization without frustrating players to the point of churn. I'll analyze this trade-off by examining the product, identifying key metrics, designing experiments, and providing a data-driven recommendation.

Step 1

Clarifying Questions (3 minutes)

• Based on the game's monetization model, I'm thinking in-app purchases are a primary revenue stream. Could you confirm the percentage of revenue that comes from in-app purchases versus other sources like ads?

Why it matters: Helps prioritize the importance of driving in-app purchases versus other revenue streams. Expected answer: 80-90% from in-app purchases. Impact on approach: Higher percentage would justify more aggressive difficulty scaling.

• Considering user segments, I'm assuming there are different player types with varying spending habits. Can you share information about the distribution of paying vs. non-paying users and their retention rates?

Why it matters: Allows us to tailor difficulty scaling to different user segments. Expected answer: 5-10% paying users, higher retention among paying users. Impact on approach: Would inform targeted difficulty adjustments for different segments.

• Looking at the technical aspects, I'm curious about the current difficulty scaling mechanism. Is it linear, exponential, or dynamically adjusted based on player performance?

Why it matters: Helps understand the flexibility and potential for fine-tuning difficulty. Expected answer: Currently linear with some dynamic elements. Impact on approach: More dynamic systems allow for more nuanced difficulty adjustments.

• Regarding resources, I'm wondering about the capacity of the game design and data analysis teams. Do we have dedicated resources for continuous difficulty optimization?

Why it matters: Determines the feasibility of implementing and monitoring complex difficulty systems. Expected answer: Small dedicated team with support from larger data science department. Impact on approach: Would influence the complexity of the proposed solution and monitoring system.