The job market is booming. Companies are growing at eye-watering It’s harder than ever to prioritise headcount, managers are vying for priority and there’s growth on all fronts. It’s hard to know how to sequence hires when there are requirements across the business. If you grow too fast without a person in place, it could spell disaster. Or if you have disproportionate teams, it could overload other functions (support/operations). We’ve just been through the journey of creating our first headcount plan, starting from a blank slate. I wanted to share our experience using market data to help other folks create their headcount plans for 2022.
During our seed funding round, an investor asked us to produce a headcount plan. We’d scribbled down a headcount plan before but only the sort fit for the back of a fag packet. Not something we’d want to present to a prospective investor. In order to try and put the right amount of rigour into it, we asked for advice from some People leaders and investors. But we were surprised to hear this was more of an art than a science. In fact, our first couple of passes at this were pure guesswork. Coming up with whatever job titles we could recollect and randomly placing them in the timeline. It didn’t fill us with confidence.
We studied other startups and read everything we could find, but didn’t feel confident shooting from the hip on this. We decided to try and put some more science behind the process by analysing some data on LinkedIn to back up these big calls.
We’ve plotted the first hire in each function for 12 seed stage companies and 6 ‘successful’ series A companies. Successful in terms of raising money from VC’s and employee satisfaction, not in terms of customer impact. Not factoring in the date founded to avoid putting too much emphasis on growth rates being the main measurement of success. These teams have grown at different rates.
Each bar represents when the role is hired in the business. The series A businesses are in blue and the Seed companies are in grey. I’ve grouped B2B SAAS companies, that’s companies that’s primary product is a web/mobile/desktop business application. And I’ve grouped B2B API companies together, their primary product is an API or infrastructure.
The job titles have been fuzzy matched and synonyms of each job title have been grouped. For example: Sourcer, Talent, Candidate Attraction have all been grouped under Recruiter.
We gathered all current employees and previous employees' work dates from Linkedin. There is a % of people that are not on Linkedin and there is a percentage of people that will have churned from the company by the time the hire is made. The average churn rate was 23% across the companies.
Churn rates ranged from 9% to 88% across the companies. Hat tip to Duffel with the lowest all time churn rate of 9%. The role with the highest churning role was Business Development and the lowest was Operations.
The first designer is employee number 19 on average. In this batch, series A companies hired their designer earlier as employee number 18 and at seed businesses a designer was hire number 20. Companies selling API products hire their first designer later, which isn’t hugely surprising.
The first sales hire on average is hire number 9 for startups. If you take the anomaly out, it’s hire number 7.
For SAAS companies it’s hire number 6 on average and 15 for API companies. I’m surprised the parity isn’t larger here given how trendy it is to reach the later stages being purely product led.
The first product hire is on average hire number 25. This was higher in the series A organisations, who made the hire at employee number 37 on average. This may speak to a shift in hiring product people earlier, since the Seed companies were founded on average 3 years later. The series A companies were founded in 2015 on average and the Seed companies in 2018. But there is a real mix here.
Stripe famously delayed hiring their first product person until 5 years after founding the company and they’re by no means a shabby startup so there’s more than one way to skin a tech startup.
The first Recruiter is on average hire number 35, consistently later than the previous roles looked at. This role was only hired in the first 20 hires on one occasion. External talent folks are listed as talent employees in 41% of cases. I’m sure the rate of organisations using external help for talent behind closed doors is much higher.
The first HR hire on average is hire number 39. It’s hire number 44 in series A companies and 36 in the seed companies. On average this is 4 hires later than the Recruiter hire. It’s brought into the first 20 hires on two occasions. Anecdotally it seems common to bring a People/HR & Talent/Recruiter generalist in to play both roles.
The Chief of Staff hire is on average hire number 32 in the companies that have made this hire. Albeit, only 27% of organisations brought someone into this role which speaks to how new it is.
The first DevOps hire is employee number 43 on average. But since these teams have all hired a good number of people into Developer roles. Unlike Talent or HR, there are other team members who have professional experience in DevOps activities. I’m sure there will have been Developers doing a flavour of DevOps work. It’s interesting that API companies weren’t hiring DevOps engineers earlier than SAAS companies with arguably more complex infrastructure. I think this chart speaks more to when technical organisations start to split off into specialist roles.
For more of a birds-eye view, here’s an overview of the numbers in each functional area. These are based on current employees, unlike the above graphs which include previous employees.
The split is similar across tech, commercial and support roles and in most organisations. The function split is relatively evenly distributed, especially in the ‘successful’ businesses that have done a series A raise.
As expected the API product organisations are heavier on the tech side and lighter on the commercial and support side in comparison to SAAS products. More surprisingly, they are lighter on the management side too. Often with an equal split between support and operations for the 2nd largest function, this may speak to the need for fewer managers per head in technical teams.
Here are six learnings for your headcount plan:
Contact me if you’d like any data on other job titles. If you’ve experienced bringing someone into a role drastically later/earlier than the norm, please do share your experience.
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This post was written in collaboration with ().