What metrics would you use to evaluate Handshake's job recommendation algorithm?

Introduction

Evaluating Handshake's job recommendation algorithm requires a comprehensive approach to product success metrics. To address this challenge effectively, I'll follow a structured framework covering core metrics, supporting indicators, and risk factors while considering all key stakeholders.

Step 1

Product Context

Handshake's job recommendation algorithm is a core feature of their platform, designed to match students and recent graduates with relevant job opportunities. This algorithm analyzes user profiles, preferences, and behaviors to suggest personalized job listings.

Key stakeholders include:

1. Students/job seekers: Motivated to find relevant, high-quality job opportunities
2. Employers: Seeking to attract qualified candidates efficiently
3. Universities: Aiming to improve post-graduation employment rates
4. Handshake: Focused on platform growth and user satisfaction

User flow:

1. Profile creation: Users input education, skills, and preferences
2. Browsing: Users explore job listings and interact with recommendations
3. Application: Users apply to jobs directly through the platform

Handshake's algorithm plays a crucial role in their mission to democratize opportunity for all students. It differentiates them from competitors like LinkedIn or Indeed by focusing specifically on entry-level and early-career positions.

In terms of the product lifecycle, the job recommendation algorithm is likely in the growth or maturity stage, depending on how long it has been implemented and refined.