How would you measure the success of Info Edge's Naukri.com job matching algorithm?

Introduction

Measuring the success of Naukri.com's job matching algorithm is crucial for Info Edge's product strategy. To approach this product success metrics problem effectively, I'll follow a structured framework covering core metrics, supporting indicators, and risk factors while considering all key stakeholders.

Step 1

Product Context

Naukri.com is India's leading online job portal, connecting job seekers with employers. The job matching algorithm is a core feature that recommends relevant job openings to candidates and suitable candidates to employers.

Key stakeholders:

1. Job seekers: Looking for relevant job opportunities
2. Employers: Seeking qualified candidates
3. Naukri.com (Info Edge): Aiming to increase user engagement and revenue

User flow:

1. Job seekers create profiles and upload resumes
2. Employers post job listings with requirements
3. Algorithm analyzes profiles and job listings
4. Matches are presented to both job seekers and employers
5. Users interact with recommendations (apply, shortlist, etc.)

The job matching algorithm is central to Naukri.com's value proposition, differentiating it from competitors like Monster and Indeed. It's in the growth stage of its lifecycle, with ongoing refinements to improve accuracy and user satisfaction.