The recruiting process seems pretty simple on the surface; read on to discover what and how to use data and why you can’t afford to.

Finding the right employees in a timely, cost-effective fashion can make all the difference in the success (or lack thereof) of a company. Data can help you achieve just that. However, with the wealth of technological advancements available to us today, it can sometimes appear overwhelming:  What data points should I track, and how can I interpret them to create actionable improvements? In this article, we’ll take a closer look at how data can and should be used to improve the recruitment process.

Link to the royalty-free image by Nick Fewings here

Data in recruitment and beyond

Finding the right applicant for the job is only part of the process (admittedly a very important part). Afterward, the applicant needs to be onboarded, trained, and integrated. And yet the process doesn’t end there. Throughout the entire span of an employee’s time with the company, he or she needs to be monitored to make sure they stay engaged, meet their targets, and continue forward on the path that is projected for them.

In every step of this process (the duration of an applicant’s, and then employee’s, stay with the company) actions are taken and results are achieved (both positive and negative). Every result yields a data point. The accumulation of data points should then create actionable information to help you identify weaknesses in the recruitment process and beyond.

It is for this reason that when we talk about using data to help in the recruitment process, we are not concerned exclusively with the data points that pertain to the recruitment process only. We need to also take into account the data points accumulated over the entire duration of an applicant/employee’s connection with the company. 

Employees who are successful in the company should share commonalities that are either binary or quantifiable. Likewise, employees who are not successful with the company will also share points in common. By tracking these commonalities, you will get a much clearer idea of what to look for in future recruits. Additionally, you will be able to identify any potential lapses or shortcomings in employee engagement, employee performance, and employee retention.

For data to be actionable, it needs to be analyzed. And that is often a field all on its own. Here is a list of analytics terms you should know that can help you get a useful crash course in business intelligence.

Data points to consider in the recruitment process

• The Hiring Channel – which platforms or job portals are successful recruits coming from
• Motivation Factors – which aspects of the job offer most appeals to successful applicants
• Application to Interview Rate – The number of applicants called in for an interview versus the number of candidates who are not can inform you about the quality of your job offer or description and the effectiveness of the portals you post in.
• Acceptance Rate – the number of employment offers versus the number of rejections and the reason for rejection
• Time-to-Hire – how much time is spent from drafting the job offer to hiring the applicant
• Cost per Hire – how much money is spent from drafting the job offer to hiring the applicant
• Drop-Off Points– during which steps in the recruitment process are you losing applicants 

Data points to consider over the lifespan of the employee

• Duration (Employee Retention) – how long the employee stays with the company
• Promotions and Bonuses – These are good quantifiable ways to monitor an employee’s success with the company
• Attendance and Productivity – how often an employee misses work and how productive they are when they don’t
• Employee Net Promoter Score (eNPS) – to measure an employee’s level of satisfaction; information on methodology, calculation, and tips for improving the eNPS can be found in this guide to the employee net promoter score

Identify the right candidate

Link to the royalty-free image by Cytonn Photography here

Despite the pervading presence of technology in the workplace, recruitment remains a very person-centric process. However, this does not preclude the use and analysis of data. In fact, using the right technology is an important step in improving your human resources department.

While companies across all sectors of activity stand to benefit from more diversity in the workplace, successful employees do still share points in common – past employment history, academic background, soft skills, technical skills, etc.

Identifying which points successful employees share in common will help you identify what criteria future recruits should meet.

Identify where the Good candidates come from

Overwhelmingly, companies tend to recruit online from job seeker portals such as Lensa. As more and more job portals are being developed, the tendency is for them to focus on a particular niche. Others lose their popularity or attract candidates that may not be ideal for your needs.

A good data-driven recruitment strategy will take note of which portals or media the applicants come from. Additionally, it would be wise to take note of where the good candidates come from so as to better target your recruitment.

Reduce wasteful spending

Eliminating the job portals that attract a high number of unqualified or less-than-ideal candidates will help reduce your overall recruitment cost. 

Eliminate biases – both explicit (conscious) and implicit (unconscious)

Data – unlike humans – are completely neutral. When it comes to selecting which applicants should be called in for an interview and which applicants should be eliminated from the process, it’s completely normal – even unavoidable – to be influenced by biases you may not even know you have.

For example, if a candidate has the same first name as someone you had a bad experience with, you may have a negative impression of that candidate, which is completely unjustified, unfair, and unlikely to help you to find the right person for the job. This is an example of an implicit bias. And everyone has them. But data points do not.

Using data to filter candidates at the outset is a must. In this stage – when you know little about the candidate but are expected to form a certain idea – is where your biases – conscious and unconscious – are most likely to come into play. 

Avoid Being Misled by Your Biases

All humans hold biases that result from past experiences. More often than not, these biases are misleading – not to mention unfair to the applicants. Rely on data, especially in the initial stages, and avoid being misled by biases you may not even know you have.

Identify problematic steps in the recruitment process

Invariably, you will lose a few candidates over the course of the recruitment process. This most likely occurs when a candidate realizes they are not a good match for the company or if they get the impression that the company doesn’t meet the criteria they had in mind.

The impression the candidate has of the recruitment process is known as Candidate Experience. A high rate of candidates dropping out of the process is likely indicative of a problem or weakness in the process. This, in turn, will have a negative effect on candidate experience.

More and more today, job seekers – as well as employees – share their experiences either on job search forums, blogs, or social media. Word gets around quickly. A flawed recruitment process risks earning your company a negative reputation and making it more difficult to attract good candidates in the future.

Improve candidate experience

Monitor where in the recruitment process candidates tend to drop out. You can use surveys to gain feedback on what the candidates think of the process, but this type of data should be used in collaboration with hard data – that is to say data that is not subject to interpretation or feelings.

Stay ahead of the curve – recognizing patterns leads to actionable predictions

Link to the royalty-free image by Calin Stan here

There’s an old saying in business that goes: You can only manage what you can measure. The inverse is also true: How can you expect to manage what you don’t bother to measure?

When it comes to recruitment and subsequent employee success – employee engagement, employee performance, and employee retention – all of these elements are measurable. As a consequence, as you strive to recruit candidates who will be successful at the company and will stay with the company for a long time, data can reveal patterns or similarities in a successful employee’s profile.

• What kind of background do successful employees tend to have?
• How much prior work experience do successful employees tend to have?
• What skill sets do successful employees tend to share in common?
• How much technical know-how do successful employees tend to have when they start off at your company.

Example #1

It could be that the more experienced candidates – or the ones with more technical know-how – tend to grow bored of the job and don’t stay with the company long enough to be considered a successful hire. When you’re looking over applications, you might be enamored with a particular candidate’s experience or know-how. However, the data suggests that it is unlikely this candidate will turn into a successful hire.

This is an example of the data making a prediction that might otherwise be counterintuitive.

Example #2

It could be that the employees who perform best at the company tend to excel in certain soft skills that might not be immediately apparent to the success of the job. For example, they tend to be good at public speaking. Even though public speaking may not be part of the job description, the data suggest that that particular skill translates to success on the job.

Once the data have suggested this pattern, you may wish to look for candidates who also possess this particular skill.

A large enough collection of data points will reveal patterns that may not otherwise be apparent to the hiring manager. These patterns allow you to make predictions about the future success of a given candidate. These predictions are actionable, helping you to identify elements that should be present in a candidate’s profile so that you can deliberate among them accordingly.

Use data responsibly

While it is undisputable that data can help you to monitor job applicants, there are a few pitfalls you should look out for. Firstly, data can be vulnerable to theft or corruption. As data are important to business, secure your data with the right vulnerability management.

Secondly, data become more reliable the greater the sample size. Conversely, if you do not have a sizeable collection of data points, it is unlikely you will be able to use data to help you to make informed decisions. Be wary of small sample sizes. In these cases, data can often be misleading.

Thirdly, while data may be neutral, the people who interpret or analyze data are not. Be wary of what is called confirmation bias.

Confirmation bias is when we have an idea, belief, or notion about how something works, then, we tend to focus on the data that confirms our beliefs while disregarding the data that refutes them. To avoid confirmation bias, it is best to tally your data before making any kind of hypothesis. 

Make sure you don’t rely on only one source of data, especially in regard to how the data is collected. For example, self-reported measurements (asking the survey subject for measurement, i.e. On a scale of 1 to 10, how happy are you with your job?) are less reliable than direct measurements (taking measures that do not depend on the survey subject’s feedback, i.e. How long did the employee stay with the company?).

In a nutshell

Using data to monitor job applicants can help you to reduce costs, speed up the hiring process, and find even better recruits. The data you collect should reflect the entire duration of an employee’s stay at the company. 

Once you’ve accumulated a large enough collection of data points, the data will reveal to you patterns and commonalities that can help you to make better decisions about recruits and the recruitment process as a whole. However, be wary of small sample sizes. And be wary of confirmation bias.