How can The Cole Group identify and mitigate inefficiencies in the executive recruitment process?
Context and Background
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, there was a single fundamental challenge: clients were slow to make a hire. Few CEOs have been in this position before and made such an important hire… Understandably, they want to make the right decision.
House-hunting is a helpful analogy:
Purchasing a home is one of the biggest decisions most people will make. As a result, few feel comfortable making an offer on the first or second home they see—especially first-time buyers. They want to “get a feel for the market” before deciding. The same applies to the executives TCG works with.
For homebuyers, though, new tools like Redfin and Zillow offer access to far more data than ever before: price estimates, detailed descriptions, rooms, square footage, walk scores, school districts, etc. As a result, homebuyers can understand the overall market—and the realistic options available—better and faster. That, in turn, instills the confidence to make an offer far more quickly.
In short, I was hired to build an equivalent tool for executive recruiting.
The most important element of the platform we built—which we called “Rolodex”—was the cohort analysis. The goal was to communicate to customers, “here’s how companies similar to yours have hired.” For example, you’re a FinTech company doing $50MM ARR looking for a CMO with a large budget and experience managing social and influencer campaigns. Based on the job spec, the cohort analysis tool would show us the distribution of candidates meeting X/Y criteria (e.g., 10 candidates meet 8/13 criteria. It would also help clients understand details about hires, like the distribution of more junior people coming from larger companies, or more senior from smaller companies.
Research & Data
The biggest challenge was getting the research and data right.
Internally, we had twenty years of prior placement data, which was a great start (although it wasn’t organized or structured, unfortunately!). We also had a lot of data from Venture Capital partners (hiring talent for portfolio companies is a big part of 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 approach 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. We developed an astonishingly accurate revenue estimation algorithm as well, which required very few data points, to estimate size of companies we didn’t know (and we knew a lot).
We also had to develop and test hypotheses around what made for—and even what constituted—a “good hire.”
At Cole, I started by interviewing all the recruiters, their EAs, and even accounting. I also talked to clients. After identifying common patterns, I created a task flow and identified use cases, user stories, and challenges in the process from start to finish.
The challenges identified in this process helped to inform our goals. After discussing with our CEO, I segmented these into product, process, and research goals. In order to prioritize them, I used a four quadrant matrix that compared value and difficulty.
Once prioritized, the goals got put into a roadmap, which was high-level and shareable with executives (since no one outside of product or research wants to have to log into yet another tool to look at or give feedback on a roadmap). Roadmap conversations took place in a Google sheet:
Individual tasks under each goal were scored (in an attempt) to remove bias and subjectivity from more granular prioritization. (Since each goal typically has many related tasks, 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 did most of the design in Sketch—working within a pre-existing-though-somewhat-limited design system—and worked with engineers in two-week sprints, at the end of which we had demos and then release new features. Some features would make it into production sooner, if urgent.
We worked on an extremely diverse set of features beyond cohort analysis, including:
- Candidate profiles, assessment pages,
- Company profiles
- Search and search results pages
- Candidate reference forms/processes
- Email/drip campaign management
- Pipeline overview and analytics
We used a number of third-party integrations like RocketReach and even facial recognition (while it’s a touchy subject, a number of companies wanted to be sure to include “diverse” candidates, and FR helped code women and minority candidates—it had a 99% accuracy rate, although we were able to manually update assessments). And we had data, processes, and integrations to “auto-assess” as much as possible, to minimize the manual assessment otherwise required.
The investment paid off.
Most importantly, revenue increased 16%.
Recruiters were closing searches 11% faster, could take on 20-25% more concurrent searches, customer satisfaction was 7% higher.
We even rolled completely off of Salesforce, since we eventually baked the features we needed into Rolodex (besides, the SFDC lightning integration had occasional issues, which were nice to resolve). The cost savings there alone was in the six figures.