How can Gloat enhance its AI-powered job matching algorithm to provide more personalized career recommendations?

Introduction

To enhance Gloat's AI-powered job matching algorithm for more personalized career recommendations, we need to dive deep into user behavior, pain points, and emerging technologies. I'll outline a comprehensive approach to improve this critical feature, focusing on user needs and business objectives.

Step 1

Clarifying Questions (5 mins)

• Looking at Gloat's position in the talent marketplace, I'm curious about the current user engagement metrics. Could you share the average time users spend on the platform and how frequently they interact with job recommendations?

Why it matters: This helps us understand if we need to focus on increasing engagement or improving the quality of existing interactions. Expected answer: Users spend an average of 15 minutes per session, 2-3 times a week. Impact on approach: If engagement is low, we might prioritize features that encourage more frequent visits.

• Considering the AI-powered nature of the job matching, I'm wondering about the data sources currently used. What types of data are we leveraging for recommendations, and are there any untapped sources we could potentially incorporate?

Why it matters: This informs the scope of improvement and potential new data integrations. Expected answer: Currently using resume data, job history, and user-declared skills. Impact on approach: If limited data sources, we might focus on expanding data inputs for more accurate matching.

• Given the evolving nature of career paths, I'm interested in understanding how often the algorithm is retrained. What's our current cadence for updating the AI model, and how do we measure its improvement over time?

Why it matters: This helps determine if we need to focus on model updating processes or performance metrics. Expected answer: Model is updated monthly, with A/B testing to measure improvements. Impact on approach: If updates are infrequent, we might prioritize a more dynamic learning system.

• Considering the competitive landscape, I'm curious about user feedback on our recommendations compared to other platforms. What's our Net Promoter Score (NPS) for the job matching feature, and how does it compare to industry benchmarks?

Why it matters: This helps identify if we need to focus on catching up to competitors or innovating beyond them. Expected answer: NPS of 30, slightly below industry average of 35. Impact on approach: If below average, we might prioritize quick wins to improve user satisfaction.