For Fever's event recommendations, should we optimize for user engagement or for maximizing ticket sales and revenue?

Introduction

The trade-off between optimizing for user engagement and maximizing ticket sales and revenue for Fever's event recommendations is a critical decision that impacts our product strategy and business model. This scenario involves balancing short-term revenue goals with long-term user satisfaction and platform growth. I'll analyze this trade-off by examining key metrics, stakeholder impacts, and potential outcomes to provide a strategic recommendation.

Step 1

Clarifying Questions (3 minutes)

• Context: I'm assuming Fever is a event discovery and ticketing platform. Could you confirm if this is correct and provide any additional context about the company's current market position?

Why it matters: Understanding the company's position helps frame the trade-off in terms of business priorities. Expected answer: Confirmation and brief overview of Fever's market position. Impact on approach: Would influence the weight given to short-term revenue vs. long-term growth strategies.

• Business Context: Based on the focus on ticket sales, I'm thinking revenue is primarily driven by commissions. What's our current revenue model and how significant are ticket sales to our overall business?

Why it matters: Helps quantify the potential impact of prioritizing ticket sales over engagement. Expected answer: Breakdown of revenue streams, with ticket sales being a significant portion. Impact on approach: Would determine how aggressive we can be in pushing ticket sales without risking user experience.

• User Impact: I'm considering the different user segments we serve. Can you share insights on our user base - are we primarily serving frequent event-goers or occasional users?

Why it matters: Different user segments may have varying needs and behaviors regarding event recommendations. Expected answer: Mix of user types with data on engagement patterns. Impact on approach: Would inform how we balance recommendations for high-value vs. casual users.

• Technical: Considering the recommendation system, I'm curious about our current capabilities. How sophisticated is our algorithm in terms of personalizing recommendations?

Why it matters: The technical foundation affects our ability to balance engagement and sales effectively. Expected answer: Overview of current recommendation system capabilities and limitations. Impact on approach: Would influence the feasibility of implementing more nuanced recommendation strategies.

• Timeline: Given the nature of this trade-off, I'm thinking this might be a ongoing optimization rather than a one-time decision. What's our timeline for implementing and evaluating changes to our recommendation system?

Why it matters: Helps frame the scope of initial changes and the cadence of iterations. Expected answer: Rough timeline for implementation and evaluation cycles. Impact on approach: Would determine the depth of initial changes and the frequency of reassessment.