We make a lot of important decisions in life based on data. An example: What’s the first thing you do when you feel something in your body’s aching? You turn to Google indeed. Depending on the information you find there you may then go to an actual doctor.
The same thing goes for your mortgage application. An algorithm decides whether or not you’re eligible for a loan, not the friendly chap behind the counter.
In the HR industry, and particularly in the world of recruitment, things are different however. The use of data isn’t customary yet and decisions are often based on gut feeling rather than data. A real shame, because data in recruitment has a lot of useful applications, especially in high volume recruitment. Here’s what you need to know.
High Volume, High Turnover
When we talk about high volume recruitment, this can be anything from several hundreds till a few hundred thousand applicants a year. Numbers that seem crazy, but are completely normal for many big enterprises such as Target or Kohl’s, especially during seasonal hiring periods. In general, high volume hiring is characterized by any number larger than usual.
When you’ve got that many CVs coming in, it’s impossible to screen all of them manually. Let alone read cover letters – there simply aren’t enough hours in a day. So on the one hand we’ve got the volume issue.
At the same time, turnover rates among this group of (low entry) employees are high and therefore a big problem, in particular during the first three months. There are several reasons for this, a wrong fit being one of them. We’ve talked about the importance of company culture and finding people who fit your organisation’s culture before (go here if you missed it).
Another reason for the high turnover is the fact that many candidates don’t know what they’re signing up for when they apply for the job. Often they send out loads of CVs, almost on autopilot, and have no clue what the position entails. It’s more about finding ‘a’ job than it is about detecting ‘the’ job. Furthermore, the candidates often aren’t qualified for the job they’re applying for.
Unfortunately, this high turnover business costs companies on average about 50 percent of an annual salary of an entry-level employee. Multiply that with the number of people you lose to turnover every year and you can do the math. This is why you want to recognize the right fit right from the start.
Why Datafied Preselection Works
If you’re dealing with thousands of applicants in your high volume recruitment process, you have no choice but to automate parts of your selection. To find good quality candidates among hundreds of applicants, you can use various tools to increase your efficiency, including chatbots, programmatic advertising, or ATS, which is now a must have.
One of those tools is a preselection software with a digital application environment. Using such software can bring you and your candidates several benefits. Which, you ask?
Such software can help you assess various skills of the candidates, instead of trying to verify them in resumes or prescreening calls. These assessments include situational judgement, multitasking, and more.
The preselection software allows you to gather different data and based on that, algorithms predict how likely it is for a candidate to succeed in a particular role. You can then make a (non) hiring decision that is backed by data.
How to prevent wrong fit?
To avoid hiring people who are not suited for the job or the company culture, it’s good to use a benchmarked preselection tool. It means you can stipulate your terms, making sure you only pick candidates that fit your company’s requirement and hence lower your turnover rate.
One way to benchmark your requirements is to assess your high performing employees and base your prerequisites on the level of their skills and characteristics. That way, you’re making sure that people you’re hiring have high potential of performing well in the job.
But a wrong fit isn’t just caused by a lack of preselection tools. As we mentioned earlier, when it comes to low entry jobs, people haven’t necessarily looked into the specifics of the job. That is why it is important that your applicants experience exactly how it is to work for your organisation and what the job entails.
Zappos built a customized application flow and achieved 97.4% candidate satisfaction.
Now, if they have to go through an automated preselection process, they will know exactly what to expect from the job as well as the company: it will confront them with several real life scenarios, showing them what they will be dealing with both in terms of their future customers, as well as their colleagues.
Not only does this benefit the applicants, it’s also good for the company’s image, especially since many organisations want to focus more on improving candidate experience.
And it doesn’t end there. A datafied preselection funnel provides you with the candidates’ test scores. This means you can give them something in return, even if they’ve been rejected for the job: the test report. That way, candidates also learn something about themselves and know which skills they can improve.
This, in turn, is good for business: a positive candidate experience makes applicants more likely to buy goods or services from your company and even recommend your organisation to their friends and families.
What more is there?
When you’re hiring a larger number of people than usual, you’re facing specific challenges. Time and recruiting staff are limited and cost per hire increases. However, you can prepare for the high volume recruitment process by choosing the right preselection software. Utilizing such tool will not only help reduce bias in your hiring process but also empower your decision-making with data and create better candidate experience.
Moreover, you can gather data throughout your high volume recruitment process and refine your requirements for the roles you will try to fill in the future. Better insights into who the people applying for the roles at your company are will help you improve your overall high volume recruitment strategy.