|Available for||Roles||Super Admin, Admin|
|Permissions||Cannot be accessed by users assigned to custom roles|
|Packages||Lever Basic, LeverTRM, LeverTRM for Enterprise|
||This article pertains to Lever's legacy reporting interface. Lever's newest reporting interface - Visual Insights - offers enhanced analytics functionality on par and beyond what is available in Legacy Reports. Specifically, conversion metrics can be found on the Pipeline dashboard in Visual Insights. To learn more, refer to our Pipeline dashboard help article.|
Savvy recruiters consider Lever's Conversion rates report to be their secret weapon - the most valuable tool at their disposal. The Conversion rates report identifies weak spots in your recruiting lifecycle and helps you zero in on specific changes you can make to improve your process.
Understanding conversion rates
The Conversion rates report measures candidate movement. The report only counts candidates who have moved between pipeline stages, or into or out of your archive, within the specified range of time. If candidates have remained in the same pipeline stage for the length of the specified range of time, they will not be counted in the report.
At its foundation, the Conversion rates report tells you how often candidates' opportunities where marked as 'Hired'. Start by considering a simple example: Conversion rates > by origin
The 'Percent Hired' column highlights which origins are highly efficient sources of hire. In the example above, we see that more than 1 out of every 10 internal candidates is eventually hired. Put another way, the 'Percent Hired' column represents the first column (6 hires) divided by the last column (51 candidates were moving through the pipeline during this time).
Take a look at a similar example in the Conversion rates > by source report:
In the example above, you can see that Instagram was the most efficient source of hire (keeping in mind the sample size was extremely small). LinkedIn is slightly less efficient than the jobs page, but resulted in 23 hires, while GitHub resulted in no hires.
Now that you understand the overall idea, move to the Conversion rates > Explore report, where we can dig deeper into candidate movement:
Moving from top to bottom:
These numbers change as filters and date ranges are applied. In this example, 1718 candidates moved during the specified range of time, and 55 of those candidates were marked as 'Hired'.
Note that "the pipeline" and "Hired" are actually drop down menus which can be clicked on and changed.
There are only three types of movement that a candidate can undergo: they can move forward in your active pipeline (Conversion), they can move out of your active pipeline (Archived) or they can move backward in your active pipeline (Moved Back). In this example, 236 candidates in the 'New Applicant' stage moved forward, 336 candidates moved into the archive and 26 candidates moved backward.
Hovering over any of the figures in the table reveals where the candidates moved. In this example, 70 candidates in the 'On-site Interview' stage moved forward: 68 went to the 'Offer' stage and 2 candidates skipped the 'Offer' stage and moved directly from 'On-site Interview' to be marked as 'Hired'.
Compare your numbers to industry averages
Lever's 2019 Benchmarks Report breaks down conversion rates across different pipeline stages, different company sizes and different industries and roles (pages 18-22, 42-43). Compare your own conversion rates against these benchmarks to see how you are faring against your competitors.
For example, if you are a company with 214 employees hiring software engineers, you might find the following statistics relevant:
- 0.92% of all candidates at companies sized 101-500 employees moved to 'Hired'
- 66.9% of all candidates at companies sized 101-500 employees who received an offer moved to 'Hired'
- 21.96% of all internal candidates moved to 'Hired'
Putting your metrics to work
Dig into Conversion rates > by origin to identify widespread patterns
Using the Conversion rates > by origin report, look for broad, overall patterns in your organization's recruiting activities. Say you looked at your hiring report for the last calendar year and saw the following:
Here are a few takeaways based on the numbers above:
- Based on the data within the 2019 Benchmarks Report, this organization is hiring efficiently - 2.2% of candidates who moved through the pipeline were eventually hired.
- Compared to our benchmarks, this company's offer acceptance rate is extremely low. Why might that be? Are their offers competitive? Are they doing a good job wooing candidates and providing a high-quality experience throughout the process? Does this organization's culture excite candidates - or scare them away?
- While this organization is doing a good job converting applicants into employees, compared to our benchmarks, that is still 98 out of every 100 candidates going into the archive. Could they dig into their candidate sources (see next section) and identify any channels flooding their pipeline with candidates that are not the fit?
Take a look through your own conversion rates. Do your offer:hire ratios or % hired metrics differ dramatically from those in our benchmarks?
Spend your recruiting budget on the channels that deliver good hires
Using the Conversion rates > by source report, we can see which channels delivered the highest quality of candidates - without getting distracted by the highest quantity of candidates. Many job boards promise to add hundreds of candidates to your funnel. If all of those candidates are under-qualified or otherwise a bad fit for the position, was it really worth your team's time and money? Take a look at the sources which offer a high rate of return on your money. In other words, do not just check whether they produce candidates - focus on whether they produce hires. For more this, check out our article on how to use your recruiting budget efficiently.
Identify bad data hygiene now, in order to gain accurate insights later
The Conversion rates > Explore report is a great health check to see whether your recruiters are using Lever correctly, and are using Lever in the same way. Check for two key indicators that there may be an issue:
Are recruiters skipping important pipeline stages?
Our rule of thumb is that your organization's pipeline should always reflect the reality of that candidate's experience. A candidate does not necessarily have to move through every pipeline stage if they are not doing so in reality. For example, an internal candidate could skip a 'Skills Assessment' stage if it is assumed their day-to-day work speaks for itself. If they skipped the 'Skills Assessment' stage in reality, then it is okay to skip that stage in Lever. However, skipping stages is often a sign that your recruiters are not keeping their candidates' profiles up-to-date in Lever. Consider the example shown below:
In this example, five candidates skipped directly from the 'Phone Interview' stage to Hired - no on-site interview, no offer letter.
Are your recruiters moving candidates in an illogical way?
There are very few reasons why a candidate would ever move backwards in your pipeline. There may be occasional one-off situations which would justify such candidate movement, but it is rare. Consider the example shown below:
You might ask,"why would a candidate ever move backwards from the 'Offer' stage to a 'Phone Interview'"? This could be a sign that recruiters are moving candidates forward too soon, before they have achieved consensus with their hiring managers. It could also mean that recruiters regularly schedule calls to discuss offers with candidates and therefore it would be beneficial to add an 'Offer Call' stage to your pipeline. It could also have simply been an error on the part of the recruiter. Either way, indicators such as these are worth further investigation.
||You can add stages to your pipeline via Settings > Pipeline and archive reasons. To learn more, refer to our help article on adding pipeline stages.|
Poor data hygiene makes it harder to gain real-time insight into your recruiting process. It also makes it hard to gain long-term insights from your recruiting process. Nip bad data hygiene in the bud by spot-checking your pipeline activity for illogical behaviour.
Now of course, everyone makes a mistake from time to time. If a recruiter accidentally moves a candidate to the wrong pipeline stage, they should delete the accidental pipeline stage change from the candidate's profile. Deleting this action from a candidate profile prevents the movement from being counted in your reports.
Use Conversion rates > Explore to identify gaps in your recruiting process
The Conversion rates report should be your go-to report to identify gaps in a recruiting process, inefficiencies, or opportunities for improvement. The Conversion rates report helps you dig into two key questions:
1. Why do candidates that we don't want enter our recruiting funnel?
Identify the stage in your pipeline at which most of candidates are archived. This is usually the 'New Applicant' stage or another early stage. If you lose most of your candidates later in the process, that is a red flag that you are not screening candidates effectively early on in the pipeline. It is in your team's best interest to reduce the amount of time spent wading through candidates that are a less than ideal fit. Consider the example shown below:
Here are some possible next steps a recruiting team could take in relation to this report:
- Train the team to close job postings as part of their workflow when marking a candidate as 'Hired' so candidates do not continue to apply to roles which are already filled.
- Revisit the clarity of your job descriptions - are you honest and explicit about your expectations? Give under-qualified prospects the opportunity to self-select out of the application process. To learn more, check out our help article on writing attractive job descriptions.
2. Why do candidates that we do want leave our recruiting funnel?
Candidates who make it deep into the recruiting process represent a large investment of your team's time and money. Your team should be feeling pretty good about candidates who reach the 'On-site' interview stage or later. It is in your team's best interest to convert most of these desirable candidates to employees - and investigate what happened when they do not. Consider the example shown below:
Here are some possible next steps a recruiting team could take in relation to this report:
- Ask questions about compensation during the 'Recruiter Screen' stage to set expectations earlier in the process. If a large portion of your candidates leave due to compensation, you could bring these figures to your leadership team to reassess whether your company's compensation packages are competitive.
- Reexamine the assessment used during the 'Skills Fit' stage to ensure it is truly identifying under-qualified candidates. Ask your hiring managers what they learned during the on-site interview which disqualified the candidate - how could that have been identified earlier?
- Take a hard look at the interview experience. Why did one-third of archived candidates lose interest after meeting your team, touring the office, etc.? Did they feel welcomed? Respected? Was the scheduling process convenient? Were your interviewers prepared and on-time? Did they encounter low morale? Consider auditing your interviews, or sending out surveys to your candidates asking about their interview experience.
It is important to note that both key questions require your organization to define specific and actionable archive reasons. You should be able to tell at a glance exactly why a candidate did not move forward. You do not want to look back on a year of recruiting data to see 76% of your candidates were archived with 'Not A Fit' as the reason. What does that tell you? What specific, concrete actions can you take to avoid bringing those candidates into your funnel, or to screen them out early in the process? As a rule of thumb, if you are archiving a candidate and none of your archive reasons is an immediate, obvious match, consider adding adding more specific archive reasons to your Lever instance. To learn how, refer to our help article on adding archive reasons.