How can Domestika improve its course recommendation system to better match users with relevant creative classes?

Introduction

To improve Domestika's course recommendation system for better user-course matching, we need to analyze user behavior, content relevance, and personalization strategies. I'll outline a comprehensive approach to enhance the recommendation engine, focusing on user experience and engagement.

Step 1

Clarifying Questions (5 mins)

• Looking at Domestika's position in the creative education market, I'm curious about the current user engagement metrics. Could you share insights on the average number of courses taken per user and the completion rates?

Why it matters: This helps us understand if the issue is with initial course selection or ongoing engagement. Expected answer: Average of 2-3 courses per user, with a 60% completion rate. Impact on approach: Lower completion rates would shift focus to in-course recommendations and progress tracking.

• Considering the diverse range of creative courses offered, I'm wondering about the distribution of user interests. What percentage of users enroll in multiple creative disciplines versus those who stick to a single area?

Why it matters: Determines if we need to focus on cross-discipline recommendations or depth within disciplines. Expected answer: 40% explore multiple disciplines, 60% focus on one area. Impact on approach: Higher multi-discipline engagement would prioritize diverse recommendations.

• Given the importance of user-generated content in creative fields, I'm curious about the integration of community features in the current recommendation system. How does user interaction, such as reviews or discussions, factor into course suggestions?

Why it matters: Helps determine if we need to incorporate more social proof and peer influence in recommendations. Expected answer: Limited integration of community features in the current system. Impact on approach: Strong community engagement would lead to emphasizing social signals in recommendations.

• Considering the rapid evolution of creative industries, I'm interested in understanding the content update frequency. How often are new courses added to the platform, and how does this impact the recommendation system?

Why it matters: Influences whether we need to prioritize recommending new content or focus on established courses. Expected answer: 20-30 new courses added monthly across various disciplines. Impact on approach: Frequent updates would require a dynamic recommendation system that quickly incorporates new content.