product management, User Research

Addressing Recruiter Inefficiencies w/ Research & Data

The Cole Group is an executive recruiting firm that places CMOs and CROs at high-growth, VC-backed companies. I previously led product there.

When I was first approached about a job, it was to address a specific challenge: clients were often slow to make a hire. Few CEOs have made such an important hire before and, understandably, they want to make the right decision.

Consider house-hunting as an analogy.

Purchasing a home is one of the most important decisions people can make. As a result, few feel comfortable making an offer on the first or second home they see—especially not first-time buyers. They want to “get a feel for the market” before deciding.

However, new tools like Redfin and Zillow give buyers access to far more data than ever before, like sales comparisons, detailed descriptions, walk scores, school districts, and price estimates. As a result, homebuyers can understand the overall market and it instills the confidence to make an offer far more quickly.

In short, I was hired to build the Zillow of executive recruiting.

Example of high-level cohort analysis, which shows how similar companies have hired (matched by criteria like size, industry, business model, growth trajectory).


The biggest challenge was the research and getting the data right.

Internally, we had twenty years of prior placement data, which was a great start. We also had a lot of data from VC partners (for those who don’t know, hiring talent for portfolio companies is a big part of the VC world). We also combed through public sources for additional details of executive hiring.

We worked with mostly objective data, like the size, vertical, or GTM of the hiring company, since subjective data is extremely difficult to apply at scale or be consistent about in terms of methodology. We even created formulas to build on that data, like estimating seniority based on normalized titles and company type/size.

And of course, we had to develop and test hypotheses around what made for—and even what constituted—a “good hire.”

I’m not at liberty to share all details, but I wanted to share how I approached the problem and a little bit of where that journey led.


I recently gave a presentation about my product management “strategy.” Overall, it’s pretty simple:

  • Have a clear vision
  • Understand users & market
  • Develop goals and metrics
  • Prioritize and roadmap
  • Plan sprints and releases
  • Test, measure, improve
User task flow

Other than this, I think Design Thinking and Lean Methodology are important frameworks to keep us focused on user needs and efficient in our approach.

At Cole, I started by interviewing all the recruiters, their EAs, and even accounting. I created a task flow to identify use cases, user stories, and challenges in the process from start to finish. A task flow made it easy to identify and verify where the actual roadblocks were.

The challenges identified in the task flow helped to informed product goals, which I prioritized in a matrix comparing value and difficulty.

Once prioritized, the goals got put into a roadmap. A roadmap should be high level and shareable with executives. No one outside of product—especially not executives—want to have to log into yet another tool to look at or give feedback on a roadmap. We finalized and corresponded about the roadmap in a Google sheet:

Individual tasks under each goal were scored, which removes bias and subjectivity from prioritization. Each goal typically has many related tasks, and these frameworks are very helpful in deciding what to approach first, determining criteria, and thinking through dependencies. All tasks were put into our task management tool, Jira.


In terms of execution, I worked with our designer and engineers in two-week sprints, at the end of which we’d have demos and then release new features. Some features would make it to production sooner, if necessary.

Here are a few screens that show some of the features we shipped during my time (sorry I had to blur some of the content, some things I can’t share).

Candidate profiles. We incorporated a certain amount of “auto-assessment” based on the companies they worked for and the role.

We worked on an extremely diverse set of features beyond cohort analysis, like candidate references, email/drip campaign management, and analytics. We used a number of third-party integrations like RocketReach and even facial recognition.

Putting together a job overview

The investment paid off.

Recruiters were starting to close searches faster, customer satisfaction increased, and recruiters could take on more concurrent searches.

We even rolled completely off of Salesforce, since we eventually baked in the features we needed there (and the SFDC lightning integration had occasional issues). The cost savings there alone was substantial.