Should Degreed prioritize expanding its content library or improving personalization algorithms for existing users?

Introduction

The trade-off between expanding Degreed's content library and improving personalization algorithms for existing users presents a critical decision point for the platform's growth strategy. This scenario touches on the core value proposition of Degreed as a learning experience platform and how it can best serve its users. I'll analyze this trade-off by examining the potential impacts on user engagement, content quality, and overall platform effectiveness.

Step 1

Clarifying Questions (3 minutes)

• Context: I'm thinking about Degreed's current market position. Could you share some insights on our user growth rate and retention metrics over the past year?

Why it matters: Helps determine if we need to focus more on acquisition or retention. Expected answer: Moderate growth but room for improvement in retention. Impact on approach: Would lean towards personalization if retention is a key issue.

• Business Context: Based on our revenue model, I assume we have a mix of B2B and B2C customers. What's the current split, and which segment is showing more growth potential?

Why it matters: Influences whether we should prioritize enterprise or individual user needs. Expected answer: 70% B2B, 30% B2C, with B2B showing stronger growth. Impact on approach: Might favor content expansion if it aligns with enterprise client needs.

• User Impact: I'm curious about our user engagement metrics. What's the average time spent on the platform per user, and how does this vary between active and less active users?

Why it matters: Indicates whether we need to focus on activating less engaged users or deepening engagement with active ones. Expected answer: Wide gap between power users and occasional users. Impact on approach: Could suggest personalization to increase engagement across all user types.

• Technical: Regarding our current personalization capabilities, what's the primary limitation – data quality, algorithm sophistication, or processing power?

Why it matters: Helps assess the feasibility and potential impact of improving personalization. Expected answer: Algorithm sophistication is the main bottleneck. Impact on approach: Would influence the resources and timeline needed for personalization improvements.

• Resource: Can you give me an idea of our current content acquisition budget versus our engineering team capacity?

Why it matters: Determines which option is more feasible given our current resources. Expected answer: Balanced resources, slight edge in engineering capacity. Impact on approach: Might lean towards personalization if we have strong engineering resources.