Growth

Driving Conversations

My Role
Product Manager
Timeline
Spring

Problem

  • Only approx. 1/10th of customers had scheduled at least 1 interview in the past
  • A fraction of those users had at least 1 scheduled interview
  • A little over half of the users scheduling actually showed up to their interviews

Situation

Indeed is evolving from a job site towards being an epicenter of connection. This evolution began with their Hosted Interview Platform. Think zoom for interviews. It’s expanded to included hosted phone and other automated experiences. Most the technology enabling connection either exists in the candidate experience owned by my team or at least relies upon our candidate experience.

As the owner of the candidate dashboard, the experience where employers manage their applicants, it was our responsibility to create an ecosystem that enabled efficient connections and helped the company achieve its lofty goal of XMilion interviews.

  • Discoverability - Employers are not aware of Indeed’s interviewing tools, and end up wasting valuable time coordinating with candidates that are uninterested, unreliable, and unresponsive
  • Education - Employers are aware of our offerings, but do not understand the functionality we offer
  • Suboptimal candidate review experience - Employers’ experience on Indeed before the interview is suboptimal, so they leave before time to schedule

Approach

How did we solve the discoverability problem?
  • Remove secondary competing actions
  • Position CTAs after explicit interest or assumed interest (resume review)
  • Add URLs to downloaded files

How did we solve the education problem?

  • Accrue data that proved the value of our scheduling tools
  • Showcase these data points to users contextually
  • Center data points to user pain

How did we solve the experience inefficiency?

  • Improving the resume review process
  • Improving the display of candidate data
  • Investing heavily in UXR and design to rethink our experience

Outcome

+XX% more connections

Methodology

Iterative and multivariate A/B testing, crowdsourcing data across PMs, and iterative user research