100 hires.
If historical data for a similar profile and market shows a conversion rate of 9%, we’ll need roughly 1,100 applications to reach that target. Factor in the average cost per hire and we have a basis for the budget. Spread the required number of applications across the time available and we also know the pace we need to maintain.
It’s not a forecast that guarantees the result. But the target is no longer just a number. We have a plan, and we can see early enough if we’re falling behind.
Why I track progress weekly, not at the end
In large recruitment projects, the final result comes too late to be the only measure of success. If we reach the end of the month and find that we’ve hired 70 people instead of the planned 100, we know we’ve missed the target. What we don’t know is where we went off track, and we’ve already lost some of the time we had to respond.
That’s why I tracked progress weekly on larger projects. Once we know the expected conversion rate and the number of applications we need, we can see early on whether we’re moving at the right pace. If we’re not, there is still time to increase campaign activity, reallocate budget, activate additional candidate sources or search existing databases more intensively.
The same data can also lead to the opposite decision. If we’re reaching the target faster than expected, there’s no reason to keep spending simply because the budget has been approved. For profiles where candidates were relatively easy to find, we could rely more heavily on lower-cost or free sources and stop a campaign once it had done its job.
When more applications aren’t the answer
Now take the opposite scenario. Applications are coming in at the expected pace and the numbers at the top of the funnel look good, but the number of people who actually start work is still below target. More applications won’t solve that problem. They’ll simply increase the cost.
At that point, we need to break the process down into its individual stages:
application → qualified candidate → selection → offer → accepted offer → start date
Where we see an unusual drop tells us where to look next. If we have plenty of applications but few qualified candidates, we look at the sources. If candidates are dropping out during selection, we examine that part of the process. If they accept offers but don’t start work, the problem is happening later.
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Send enquiryI had exactly that situation on one project. The number of accepted offers wasn’t the problem, but fewer people than expected were showing up on their first day. Because of the scale of the project, recruitment started well ahead of the actual start date, while contracts were signed closer to it. In the meantime, we were losing some candidates who had already confirmed. The answer wasn’t to generate more applications. It was to sign contracts earlier and strengthen communication with candidates in the period leading up to their first day.
The same result (not enough people hired) can have very different causes. Get the diagnosis wrong and we’re likely to spend our time and money in the wrong place.
The numbers show you where, but rarely tell you why
Even a detailed analysis of the process won’t always give you the full answer.
On one project, I noticed an unusual drop in candidates after a particular stage of the selection process. The data clearly showed where the problem was happening, but it was only through conversations that we discovered the real reason. The issue wasn’t the selection criteria. It was the way the role was being presented to candidates.
The presentation was making the role seem unnecessarily complex, and candidates were dropping out at that stage as a result. The information itself remained the same and completely transparent. We only changed the way it was presented.
That’s why I’ve never looked at conversion rates in isolation. The numbers can show us quite precisely where something is going wrong, but understanding why usually comes from conversations with candidates, recruiters, team leaders and colleagues in operations.
When you hit the target but still have a problem
There’s another number that’s easy to overlook when the focus is on hiring at scale: how many people are still there after the first few weeks.
On paper, the hiring target may have been met. But if a large number of new hires leave within their first few weeks, it’s difficult to call that a successful result.
When early attrition was higher than usual, we looked at what was happening after the first day. Conversations with people who were leaving sometimes showed that the problem wasn’t candidate attraction or selection at all. It was what happened after they joined – onboarding, preparation for working independently, or a gap between the expectations created during recruitment and the reality of the job.
In that situation, the answer doesn’t necessarily lie in changing the recruitment process. It may need to be addressed in operations.
If we track how many people we hire but not what happens after their first day, we may be measuring recruitment performance accurately without necessarily measuring the business outcome that recruitment was meant to support.
Fewer reports, better questions
Over the years, I’ve had access to a lot of reports. I didn’t use every one of them. The ones that mattered were those that helped me ask the right question early enough to still change the outcome.
On large projects, I mainly needed the data to answer a few questions:
- Are we moving at the pace needed to hit the target?
- Where are we falling behind?
- Do we know what is causing it?
- Is there anything we can still change while there is time for it to make a difference?
The answer to that last question rarely depended on the recruitment team alone. That’s why regular, transparent communication with operational leaders and other colleagues involved in the project was just as important as the data itself.
If the problem was in a part of the process the recruitment team could change, we could act quickly. If the cause lay in how the role was presented, training or the experience after joining, we needed to involve the people who could influence that part of the process.
“The value of data isn’t in explaining why we missed the target after the fact. It’s in giving us enough time to still change the outcome.”