6 MINUTE READ
Recruitment strategy looked quite different before the internet. Think bulletin boards and newspaper ads. But in the mid 1990s recruitment changed forever with the arrival of online job boards such as Indeed and Monster. Candidates could easily apply for jobs and employers had access to massive talent pools.
More than two-decades later not much has changed except for the fact that the recruitment industry now generates a huge amount of data on a daily basis. Every application procedure, screening session, communication, or review is can now be easily recorded and archived.
When creating a recruitment strategy, forward thinking companies are actively sifting through this data to create more intelligent hiring processes. Organisations now have the opportunity to test, measure, and continuously improve their recruitment strategy.
At the same time, it’s important to understand the limitations of data and analytics. While businesses try to automate as much of their recruitment strategy as possible, being overzealous actually increases the chances of making a bad hire or missing out on a great one.
This is because a CV, qualifications, and prior work experience alone have little impact on how the candidate will perform once hired. It’s very difficult to break down character traits and attitude into useable data. So embracing a data-driven approach should primarily focus on figuring out the most effective indicators of a candidate’s success at your company.
How do you switch to a data-driven recruitment strategy?
To enable data-driven decisions, everything has to be meticulously recorded. If your data is incomplete, then you end up with an incomplete picture of potential candidates. Any decision you make with incomplete data is going to be inaccurate more often than not.
As a result, organisations should aim to build a recruitment platform where all interactions (from formal communications to various touch points) are logged into the system. This is possible with the help of technologies which enable two-way synchronisation with company email systems, such as PieSync.
Once in place this approach enables a holistic view of the whole recruitment process. You don’t have to spend hours logging in all the data as the tech will automatically capture all emails, SMS, calls, internal communications, and calendar invites.
When you get this part right, you can engage in data analytics to improve your recruiting efforts by enhancing efficiency while lowering hiring costs.
During screening, it’s important to have a scorecard for each candidate at every stage of the process.These should be marked objectively against each relevant skill and a binary decision should be made at each stage – from CV screening through to interview.
Skills should be rated on an objective five to ten point scale while detailed notes for each stage should also be maintained to provide an in-depth view. Your quantitative scorecard should look something like this:
Each interviewer will judge candidates differently. To account for this you should calibrate the ratings provided by each interviewer to eliminate bias.
Measure hiring speed
To identify bottlenecks in your recruitment process, you have to start measuring hiring speed. This enables you to included a forecasted talent supply as part of your recruitment strategy.
Start tracking how long each candidate spends in each stage of your recruitment process. When you identify where most candidates get stagnant, it makes sense to allocate more resources to that particular stage of the process to speed things up.
Determine the value of each candidate sourcing channel
When building a data-driven recruitment strategy, you also have an opportunity to identify the quality of your sourced candidates across various different channels. The value of each channel can be measured by the tracking both the number and quality of candidates provided by each. This information helps you identify channels that warrant further investment and those which offer little return. In turn, this can reduce your overall cost per hire (Although as we outlined in a persious post, the ‘cost per hire’ metric has it’s issues)
The preceding steps should ultimately lead to a highly structured interview. In the ideal scenario, all candidates who make it through the previous stages should be competent in the role. Its then the interviewer’s job to decide who is likely to perform best.
It’s virtually impossible to generate usable data from unstructured interviews. This is one reason among a long list as to why you should avoid them at all costs. On the other hand, structured interviews enable you to collect reliable and comparable data for all candidates. They also steer interviewers towards making more objective decisions.
Here’s a process you can follow to implement effective structured interviews:
Define the traits you want in the candidate: Create independent traits that are essential to a candidate’s success at the job. It is important that these traits are as independent of each other as possible. Let’s say you are hiring a software developer. You are probably looking for, a person who is “a fast learner”, “analytical”, “great at task management”, “team player”, and “a great communicator”. Ideally, you should limit the list of traits to 5 or 6 independent traits.
How to measure: Figure out how you are going to analyse each of these traits. Create several questions to assess each trait and what answer should be rated a 5 versus what answer should be rated a 3. Decide on the score a candidate needs to get in order to get the job. As well as a minimum overall score, you should also implement a minimum threshold to clear for each trait.
Interviewer instructions: Write detailed outlines of the traits and a set of fixed questions for interviewers. Stick to a pre-decided order of questions. Make sure that interviewers also take detailed notes in the interview.
Getting feedback: As a policy make sure that interviewers submit the feedback within 24 hours of the interview. Interviewers will not remember the things discussed in the interview after a day.
Move fast: Once a candidate exceeds your expectation of what would it take to do the job, extend the offer. This might even be the first candidate you interview, don’t wait around to find a “better match”.
The next step
Finally, to see if your data-driven recruitment model worked, you can compare the recruitment data with actual performance data to identify what characteristics are shared by high performers in the recruitment process. Once you have developed a robust data-driven recruitment strategy, the next step is to add artificial intelligence (AI) and machine learning (ML) into the mix and take it to the next level.
Need help creating a data-driven recruitment strategy that works? Our Customer Success team can offer help and advice. Contact us on: firstname.lastname@example.org 0203 137 7005 / 07946 191 397