How can Degreed improve its skills assessment feature to provide more personalized learning recommendations?

Introduction

To improve Degreed's skills assessment feature for more personalized learning recommendations, we need to dive deep into user behavior, pain points, and the current state of the product. I'll outline a strategic approach to enhance this feature, focusing on user needs and data-driven insights.

Step 1

Clarifying Questions (5 mins)

• Looking at Degreed's position in the learning and development space, I'm curious about the current user engagement metrics. Could you share the average time users spend on skills assessments and how frequently they engage with learning recommendations?

Why it matters: This helps us understand if users find value in the current assessment and recommendation system. Expected answer: Users spend an average of 15 minutes on skills assessments monthly, with 30% following through on recommendations. Impact on approach: Low engagement would suggest a need for more engaging assessments and relevant recommendations.

• Considering the evolving nature of skills in today's job market, how often is Degreed updating its skills database and assessment methodologies?

Why it matters: Ensures the relevance and accuracy of skills assessments. Expected answer: Skills database is updated quarterly, with major methodology revisions annually. Impact on approach: Infrequent updates might require a more dynamic, AI-driven approach to skills identification and assessment.

• Given the rise of microlearning and just-in-time learning trends, how are users currently accessing and consuming the recommended learning content?

Why it matters: Helps align our solution with user preferences and behavior. Expected answer: 60% of users access content on mobile devices, with a preference for short-form content. Impact on approach: Would focus on mobile-first, bite-sized learning recommendations.

• In terms of data collection, what types of user data does Degreed currently leverage for its recommendation engine?

Why it matters: Determines the depth and breadth of personalization possible. Expected answer: Currently using user-inputted skills, completed courses, and basic job role information. Impact on approach: Limited data might suggest expanding data collection points or integrating with other systems.