Recruitment analytics can appear to require specialist knowledge, training and expensive software to even attempt. This coupled with questions surrounding whether it benefits HR effectiveness leads to the question: why do it?
At my organisation we followed similar recruitment practices for a long time, using advertisement locations and agencies we had always used. However we found we were overspending on our recruitment budget and re-advertising roles too frequently. We were not achieving the positive results we required.
We needed to examine our recruitment practices, to evaluate whether we were using our resources effectively and providing managers with the most effective way of finding the best staff. Therefore we developed analytics to examine what we were doing, inform real decisions and make our recruitment processes as effective as possible.
To say this would be simple would be lying. Filling the pieces of the analytic puzzle is a fundamental next step.
Identify the question to be answered. There is no point in running analytics if there isn’t an objective. Although this appears obvious, with so much information available within HR systems it is crucial to only focus on data that can help develop the HR practice that requires improvement. Our objective was to evaluate whether our advertising mechanisms were effective. Therefore the only data we required to download was:
- The vacancy
- Each applicant
- The stage of recruitment that the applicant reached (not shortlisted/invited to interview/offered the job)
- The advertisement source where the applicant saw the role.
Not breaking the bank. A misconception is that only new software will enable the development of analytics. Whilst software can be an effective, our limited resources meant purchasing a new system wasn’t an option, and it wasn’t necessary. The HR system we already have contains a wealth of crucial information, added to on a daily basis, and which can be extracted as required.
Statistics knowledge. You don’t need a PHD or a maths degree to conduct analytics. All that is required is identifying what data to evaluate and how this can provide answers to the questions that need answering.
So what are the first steps:
Evaluating what you have
To use data within the HR system there is a need to ensure that the data is accurate and fit for purpose. Small changes made in the HR system lead to dramatic improvements in the quality and accuracy of data . For example:
Misspelt or inconsistent inputting of data will hinder evaluation, as trends won’t be accurate. Therefore our solution was to change the field on the HR system; by implementing a drop down list rather than a field where the name was manually typed. This simple switch ensured consistency in spelling and allowed accurate analysis of advertisement mechanisms.
Blanks in the system mean it is impossible to accurately analyse the success of different recruitment methods. A whole picture is not provided. Therefore changing the ‘applicant source’ field to a mandatory field, and removing ‘other’ as an option in the drop down list, ensured that blanks were minimised and vague answers eradicated.
This of course means that it is crucial to obtain ‘buy in’ from colleagues entering the data on the system; explaining what data needs to be collected, why the data is being collected and detailing any changes to HR processes.
Collecting the data
These changes take a while to affect the quality of data in the system. We began running reports using the data after a couple of months but waited a year to conduct an overview trend analysis.
Crucially during this construction it is important to ensure clean data. This means regularly running reports and tidying discrepancies. Once again complex statistics aren’t required to identify mistakes. Using Excel and ‘conditional formatting’ to highlight duplicates will make sure recruitment sources are inputted consistently – with unhighlighted (unique) options either being a sole advertisement source or something that has been misspelt in the system, and which therefore requires correcting.
Importantly data will never be perfect. There will always be a risk of errors but there is a need to regularly check data and amend mistakes in the system itself, to reduce the risk as much as possible.
Designing the report – extracting data
To allow evaluation over time a template data download is required, which provides all data in one place. Crucially it is important to not overcomplicate the report. Data should only be included if it can help answer the question “what needs to be solved, and how can the data help solve the issue”.
Excel – manipulating data
A final report must be simple for the whole team to download and use, and present all information as simply and effectively as possible. Learn pivot tables! Although pivot tables have a reputation for being complicated they are a really simple way to evaluate data and highlight effectiveness, without developing any formulas. There are many YouTube videos that explain how to use pivot tables. Importantly, pivot tables can add layers to evaluation to not only show overall recruitment effectiveness but also easily breakdown evaluation to specific departments or jobs.
This all means that when speaking with line managers you can present actual advertisement effectiveness data for similar roles, and use data to help managers work out the most effective way to recruit. We found this approach gave us confidence to develop new processes and try new advertisement mechanisms (i.e. national radio advertisements). Therefore it ensures that we are being as helpful as possible whilst using the organisation’s resources effectively. At our organisation we were within our recruitment budget this year and the number of re-advertisements dropped by 40%.
Analytics doesn’t require specialist knowledge or systems, it just requires a specific question to be asked and the use of data you already hold to help answer it.
Alex Taylor will be speaking at our leading HR innovation in recruitment forum on the 28th September.
- Recruitment and analytics: developing hiring practice - Tuesday, September 19, 2017