How would you measure the success of Meetup's event recommendation algorithm?

Introduction

Measuring the success of Meetup's event recommendation algorithm is crucial for optimizing user engagement and driving platform growth. To approach this product success metric problem effectively, I will follow a simple product success metric framework. I'll cover core metrics, supporting indicators, and risk factors while considering all key stakeholders.

Step 1

Product Context

Meetup's event recommendation algorithm is a core feature of the platform, designed to connect users with relevant events and groups based on their interests, location, and past behavior. This algorithm plays a crucial role in enhancing user experience and driving engagement on the platform.

Key stakeholders include:

1. Users: Seeking relevant event recommendations to connect with like-minded individuals and explore new interests.
2. Event organizers: Aiming to reach their target audience and increase event attendance.
3. Meetup: Focused on increasing user engagement, retention, and platform growth.

The user flow typically involves:

1. User logs into the Meetup app or website
2. The algorithm analyzes user data (interests, location, past events)
3. Personalized event recommendations are displayed to the user
4. User interacts with recommendations (views, RSVPs, attends)

This feature aligns with Meetup's broader strategy of fostering real-world connections and building communities around shared interests. Compared to competitors like Eventbrite or Facebook Events, Meetup's algorithm focuses more on recurring group events and niche interests.

In terms of product lifecycle, the event recommendation algorithm is in the growth stage. It's a well-established feature, but there's ongoing refinement and optimization to improve its effectiveness and user satisfaction.