Effective Recruiting Metrics For Fast-Growing Start-ups
Posted on 6th June 2017
Working in a high growth start up is hard. There are challenges everywhere. Process challenges, product challenges, people challenges. There is a constant game in play where you have to service a continuous geometric increase in problems with a discrete increase in resources. The decision making impact is critical and therefore more important than in large established businesses.
With that in mind, hiring in high growth start ups is one of the most important functions to consider. There is a constant need to hire fast, hire well, and hire cost effectively. Key recruiting metrics help to find a balance between these trade-offs and to optimise for the best possible outcomes. Too often companies adopt unstructured processes to hiring, significantly reducing their chances of hiring the right talent to help achieve product market fit and achieve scale before the cash runs out.
With the right recruiting metrics in place, high-growth start ups can optimise their recruitment processes and find the right balance between competing trade-offs – quality of hire, time to hire and cost per hire.
All trade-offs are important and heightened in fast-growing start-ups, where the impact of decision making is more volatile than in established large businesses.
A lot of applicant tracking systems have reporting functions in place, making it easier to track certain recruiting metrics. However, to get a detailed analysis, you will often need to bring data from disparate sources together. The up front work will be worth it, considering the benefits it can bring in optimising the three key competing recruiting trade-offs.
Below are some key recruiting metrics to consider, together with considerations about further analysis that can be undertaken.
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There are a number of different on and off-line platforms you can choose to source talent. LinkedIn, Angel List, Facebook, job boards, Github, careers fairs, networking events and more.
Where you choose to spend your time should be dependent upon where you are finding the highest quality candidates, together with the total number of applications received per source and the time and cost spent engaging each candidate.
It is worth segmenting this analysis by Job type (Temporary, Permanent, Contract), Job Seniority, and Job Role (programmer,sales, finance etc), as it is likely that different platforms attract different people.
Take a look at the matrix below:
Knowing which platforms produce the most applicants at the highest quality enables you to determine where to focus your time sourcing and what decisions to take regarding the other platforms.
For example, if you have a source that has a high number of total applicants and a high level of quality candidates, invest time and money here. Mine the source and build a talent pool. If it works, do more of it!
If you have a source that has a low number of applicants but high quality candidates, it’s worth spending time here too. However, you’ll need to ask some further questions. Could you be getting more applicants with better copy or more money invested in ads? Are there a low level of applicants because they are shifting to a new platform? Is this a sustainable source of candidates over time? With your assumptions from these questions, it may be worth testing the source to see if you can turn it into a star, or developing a plan to move away from the source over time.
If you have a source that has a high number of applications but low quality candidates, you have some digging to do. Could you rewrite the copy to attract better candidates? Are there any specific reasons the wrong quality of candidates are applying? Are you on the wrong platform for these types of roles? Again, you’ll need to come up with some tests and spend a little time experimenting. Based on the results, you could either turn this platform into a star or dump it.
If you have a source with low quality applications and low total applications, stop spending your time and resources there! Instead, spend it mining platforms with high quality and high total applicants and testing platforms where the quality is good but the numbers are low.
It’s key to remember that these sourcing patterns are dynamic and shift over time. Another key metrics is to measure the change in total applications and quality of applications over time. This may indicate a movement under way where the best candidates hang out.
Finally, to truly optimise your recruitment processes, you need to overlay this data with the data you have about cost per hire per source and the time to hire per source.
Cost Per Hire
Average cost per hire = total recruiting cost / number of hires made
Total recruiting costs include:
- Sourcing fees (job boards, platforms, ad spend, careers fairs spend)
- Employee salaries (time spent on hire as a proportion of salary (inc time spent interviewing)
- Employee referral costs
- Indirect costs of hire (lunches, travel costs etc)
Also, you need to consider the decrease in effectiveness of interviewing staff as a result of cognitive switching from one task to another.
It’s worth segmenting cost per hire by seniority type, as there may be different recruitment process in place, requiring more staff in attendance at interview or indeed more interview stages.
To optimise cost per hire, it’s worth analysing where the key costs are spent and comparing this to the funnel metrics of the recruitment process. Are there any areas you can test to try and decrease costs without decreasing conversions? For example, one telephone screening instead of two, or two people present in an interview instead of three.
Time To Hire
Getting quality candidates in a timely manner is essential, and has heightened importance in a high growth start up where additional people are constantly required to service greater demands.
By measuring time to hire at each stage of the recruitment process, you can identify bottlenecks and try to find ways to remove them.
Stages include job listing to application (or job listing to time to source on each platform), application to phone screen, phone screen to on site, on site to offer, and offer to hire.
With time to hire metrics, you can see what sources take longest to translate into phone screens. By overlaying that with the source data above, you can find the total number of quality applicants per unit of time by job type. With that data, you can further optimise which platforms to focus your sourcing efforts on.
You can also compare this data to your Conversion Funnel (see below). Are you taking too long between stages? Are candidates going elsewhere? If there is a time gap between stages and a high drop off between conversions, can you better interact with candidates to keep engagement levels high?
You conversion funnel measures the amount of candidates advancing to each stage of your recruitment process
This begins with the number of applications per job, which you can overlay with the source data explained above.
With analysis, you may find bottlenecks in the hiring process. For example, the number of candidates advanced from a phone screen to on site interviews may be particularly low. This raises some questions. Are we sourcing the wrong type of applicants to be phone screened? Is the phone screener being particularly stringent in their criteria? Does the phone screen assessment differ to others within the company? Are candidates who don’t make it through phone screens going to our competitors? Are candidates losing engagement between the two stages? Could we be doing something to better promote the company? And so on.
Keeping an eye on your conversion funnel allows you to ask the deep questions that will allow you to further improve your recruitment processes.
In high growth start-ups that have a limited cash and time frame to achieve scale, hiring decisions are critical. The three main trade-offs to balance for recruiting are quality of candidate, costs per candidate and time to hire. Establishing a recruiting metrics framework can help to optimise the recruitment process.
Key metrics to do deep analysis on include the sources used to find candidates, the average cost to hire a candidate, the time to hire a candidate broken down by recruitment stage, and the recruitment conversion funnel metrics.
With all of this data, you will be able to build a forecasting dashboard, anticipating recruitment requirements and comparing to actuals by looking at the source, funnel, cost and time metrics.
Having onboarded staff, you can then begin to look at quality of hire in depth, by using a performance framework. You can assess performance against predicted ability in the hiring process to see if you had any flawed assumptions. You can track candidates missed out on the re-engage at a later date and to see if they end up at competitors.
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