How can Fever improve its event discovery algorithm to better match users with niche local experiences?

Introduction

Fever's event discovery algorithm plays a crucial role in connecting users with unique local experiences. To improve this algorithm and better match users with niche events, we need to delve deep into user behavior, preferences, and the intricacies of local event ecosystems. I'll outline a comprehensive approach to enhance Fever's recommendation system, focusing on personalization, data utilization, and user engagement.

Step 1

Clarifying Questions (5 mins)

• Looking at Fever's position in the event discovery market, I'm curious about the current user base and engagement metrics. Could you share some insights on our active user count, average events attended per user, and user retention rates?

Why it matters: This information will help us understand the scale of our user base and identify areas for improvement in user engagement. Expected answer: 5 million active users, average 2 events per month, 60% 3-month retention rate. Impact on approach: Lower retention would shift focus to improving user experience and event matching accuracy.

• Considering the importance of local context in event discovery, I'm wondering about our geographical coverage and data sources. How extensive is our event database across different cities, and what are our primary sources for event information?

Why it matters: This helps us assess the breadth and depth of our event offerings, which directly impacts our ability to match users with niche experiences. Expected answer: Coverage in 50 major cities, partnerships with local venues and promoters, user-generated content. Impact on approach: Limited coverage would prioritize expanding our event database and local partnerships.

• Given the rapid changes in the event industry post-pandemic, I'm interested in understanding how user preferences have evolved. Have we noticed any significant shifts in the types of events users are seeking or their booking behaviors?

Why it matters: This insight will guide our algorithm improvements to align with current user interests and behaviors. Expected answer: Increased interest in outdoor and small-scale events, more last-minute bookings. Impact on approach: We'd focus on improving real-time recommendations and highlighting Covid-safe events.

• Considering the competitive landscape, I'm curious about our unique value proposition. What key features or aspects of our algorithm currently set us apart from other event discovery platforms?

Why it matters: Understanding our strengths helps us build upon them and identify areas where we can further differentiate. Expected answer: Personalized event scores, social sharing features, exclusive event access. Impact on approach: We'd look to enhance these differentiators while addressing any gaps in our offering.