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Product Management Analytics Question: Evaluating metrics for Discord's server discovery feature

what metrics would you use to evaluate discord's server discovery function?

Product Success Metrics Medium Member-only
Metric Selection Data Analysis Product Strategy Social Media Gaming Communication Platforms
User Engagement Product Analytics Social Platforms Discord Community Growth

Introduction

Evaluating Discord's server discovery function requires a comprehensive approach to product success metrics. This feature plays a crucial role in Discord's ecosystem, connecting users with new communities and enhancing overall platform engagement. To assess its effectiveness, I'll follow a structured framework that covers core metrics, supporting indicators, and risk factors while considering all key stakeholders.

Framework Overview

I'll follow a simple success metrics framework covering product context, success metrics hierarchy, and strategic initiatives to improve the feature.

Step 1

Product Context

Discord's server discovery function is a feature that allows users to find and join new servers based on their interests, categories, or keywords. It's designed to help users expand their community connections and discover content relevant to their preferences.

Key stakeholders include:

  1. Users: Seeking new communities and content
  2. Server owners: Looking to grow their communities
  3. Discord: Aiming to increase user engagement and retention
  4. Advertisers: Potentially interested in promoted server listings

User flow:

  1. Users access the discovery page
  2. They browse categories or use the search function
  3. Users view server details and decide whether to join

This feature aligns with Discord's strategy of fostering community growth and increasing user engagement. Compared to competitors like Slack or Microsoft Teams, Discord's server discovery is more robust and community-focused, catering to a broader range of interests beyond professional use.

Product Lifecycle Stage: The server discovery function is in the growth stage, with ongoing refinements to improve user experience and discoverability algorithms.

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