How can GLG improve its expert network platform to facilitate faster connections between clients and advisors?

Introduction

To improve GLG's expert network platform for faster connections between clients and advisors, we need to analyze the current user experience, identify pain points, and develop innovative solutions. I'll approach this by examining user segments, mapping the journey, and proposing data-driven improvements that align with GLG's business objectives.

Step 1

Clarifying Questions

• Looking at GLG's position in the expert network market, I'm curious about the current user base composition. Could you share the ratio of clients to advisors on the platform?

Why it matters: This helps us understand if we need to focus more on attracting advisors or clients. Expected answer: 60% clients, 40% advisors Impact on approach: If there's an imbalance, we'd prioritize growing the underrepresented group.

• Considering the speed of connections is our focus, I'm wondering about the current average time from a client's request to successful advisor matching. What's our benchmark for this metric?

Why it matters: Establishes a baseline for improvement and helps set realistic goals. Expected answer: 48-72 hours on average Impact on approach: If it's already quite fast, we might focus on quality of matches rather than just speed.

• Given the evolving nature of expert networks, I'm interested in understanding our primary competitors and their unique value propositions. Who are our main rivals, and what sets them apart?

Why it matters: Helps identify areas where we can differentiate and innovate. Expected answer: AlphaSights with AI matching, Guidepoint with specialized industry focus Impact on approach: We might explore AI integration or niche expertise areas to stay competitive.

• Considering the platform's evolution, I'm curious about the most requested features from both clients and advisors in the past year. What improvements have users been asking for?

Why it matters: Aligns our improvements with user demands and expectations. Expected answer: Clients want more precise matching, advisors seek more flexible scheduling Impact on approach: We'd prioritize these areas in our solution design.