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HR & Recruitment

How to Use Recruitment Analytics to Boost the Hiring Process

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Coresignal

September 08, 2022

Big data analytics are increasingly being adopted in businesses as there could hardly be a department that would not benefit from utilizing public web data and analysis tools.

In this article, you will learn about recruitment analytics, best practices, and metrics to measure success.

What is recruitment analytics?

Recruitment analytics is the process of collecting and analyzing data with hiring cycle metrics to improve recruitment procedures and enhance hiring results. Recruitment analytics is utilized primarily to analyze how hiring results correlate to successful business practices.

This procedure investigates all parts and aspects of the hiring process in the company, aiming to forecast future hiring needs and improve the overall process.

Let's take a closer look at how data-driven recruitment works, its benefits, and how companies measure the success of their recruitment analytics processes. 

The rise of data-driven recruitment

Ever since its emergence, big data's impact on business has been transforming various aspects of business operations, from hiring to HR procedures and to daily internal operations.

Since its introduction into the business world, big data and analytics processes have shown potential for improving decision-making within HR departments and have been a hot topic for discussion.

This is no surprise, as with the growing accessibility of software and hardware resources, qualified specialists and employees are the finite resources that companies are competing for.  

Therefore, companies turn to applicant tracking systems, talent sourcing, and other tools to efficiently find the right candidates for the role.

It is estimated that 88% of firms globally are already using AI for HR tasks, at least to some extent. As both the technology and its acceptance in hiring develop, the role of recruitment technology vendors and data in HR decision-making will only rise.

The proliferation of data used in hiring provides perfect conditions for the beneficial adoption of recruitment analytics. The more the recruiting process itself is based on working with data, the better equipped we are to look at it from the analytics perspective.

Recruitment analytics is a necessary supplement to data-driven hiring because it reveals the business impact of particular choices. This allows arriving at fact-based conclusions about what needs to be done to ensure better hiring outcomes.

Benefits of recruitment analytics for the hiring process

Before detailing how recruitment analytics work, it's worth noting some of the benefits that highlight its importance. Below are a few ways how recruitment analytics improve hiring.

Better-quality hires

HR professionals have identified improving the quality of the hires as the number one recruiting priority for 2021. Recruitment analytics can assist with this task by identifying the standard features of a high-quality hire.

This is done by analyzing historical company recruitment data to discover which features most commonly recur in the best hires for particular positions. Once these patterns have been discovered, businesses leverage these insights for future hiring decisions.

If you’re looking to improve the quality of your hires, Coresignal’s employee data is here to help you out. Data points such as name, location, experience, employment length, and more will help you find the best talent for your company and reach business goals.

With employee data, you can enable a data-driven recruiting process and remove the tedious work of screening hundreds of resumes that are not qualified for the position you need to fill. By doing so, you will save both time and resources that could be directed to other tasks that bring revenue to the company instead of spending excessive money on bad hires.

Predicting future hiring needs

Historical data can also be used to forecast how much hiring will need to be done in particular upcoming periods of time.

Predictive analysis uses statistical modeling and machine learning methods to analyze such data and determine the likelihood of specific future outcomes.

Thus, when applied to recruitment, it reveals the statistical probability of future hiring needs—knowing when and how many new hires they will need will allow HR specialists to plan ahead and prepare viable hiring strategies.

Improved efficiency

Recruitment analytics plays a significant role in answering hiring-related questions by looking at the data on the hiring process. With these answers at hand, HR professionals are able to make hiring decisions much faster and with valid confidence.

This means hiring more efficiently, which allows avoiding prolonged periods of understaffing and improving the candidate experience.

Constant evaluation and improvement of the process

Finally, recruitment analytics is all about constantly scrutinizing every aspect of the hiring process to discover opportunities for improvement. This is done not merely as far as it directly concerns HR but also in the broader context of company business goals.

This allows them to constantly refine the recruiting process to ensure that these goals are advanced as much as possible.

Person measuring with measuring tape

Key metrics for measuring recruitment

Recruitment analytics can achieve the benefits listed above by applying the scientific method – measuring. But, of course, to measure something as multi-layered as recruitment, first, it's necessary to identify the relevant, measurable features.

HR departments that apply recruitment analytics have found many such features, known as recruiting metrics, to be useful in providing actionable insights. Here are key recruitment metrics that are commonly used to measure recruitment.

Source

First and foremost, you need to check what sources bring you the best candidates. Several examples could be job boards, your own website (careers page), and referrals.

Time to hire

Time to hire measures how much time passes from publicly announcing an open position to successfully filling the role. 

Cost per hire

Hiring costs include all resources used in order to hire, for example, the cost of ads, cost of tools used for hiring, administrative costs, etc. As this metric refers to the average cost to fill an open position, it's measured by dividing the total hiring costs by the number of hires.

Source of hire

This metric tracks how many hires come from which sourcing channels. It can also be measured by calculating the percentage of all hires from particular sources, such as utilizing ATS metrics (applicant tracking system). 

First-year attrition

This metric tracks employees that leave the job position in less than a year. It's especially important to track this one because the hiring process can get fairly expensive and cost a lot of time and resources.

Offer acceptance rate

The offer acceptance rate measures what share of job offers made by the company have been accepted.

Candidate experience

This metric helps you see how candidates feel about the recruitment process. It could go a long way—if the candidate felt that the interview wasn't organized or coordinated properly, they might discourage their peers from applying to job ads that your company puts out or vice versa. If the candidates drop, it's still in your best interest to make sure that the talent acquisition teams make the experience as smooth as possible.

Age of job

If the job ads stay on the board longer than expected, it signals that your recruitment process might be lacking or maybe the job descriptions are not as good as competition's.

Quality of hire

This is a complex metric that can be defined differently depending on what a good hire means for a particular company. Thus, to measure the quality of hire, it has to be broken down into smaller parts first. Some of the most common of these smaller parts are retention rates and new-hire performance.

These are just some of the most fundamental metrics commonly considered to be most helpful. Their importance is clear, as they refer directly to the resources companies use to hire and what they get out of it.

However, many different metrics can be defined to measure more particular aspects of the process. For example, one may want to know what share of candidates that start filling the application form abandon it without finishing. If this rate is high, it indicates that there is an issue with this particular part of the process.

Team building and planning meeting

Best practices for using recruitment analytics

Defining clear analysis goals

Firstly, it is important to note that the metrics collected and measured during the analysis process depend upon the hiring goals and HR's KPIs.

For example, if our new hires are of very different quality, we may want to look at the sources of those hires to see if some sources generally provide better candidates.

This could lead to focusing more on this source, thus overall improving the quality of hires.

Prioritizing data quality

The results of any analysis are only as good as the data that's used. Thus, it's crucial to work on maintaining high data quality.

When training algorithms in order to use them for predictive analytics, we may want to enhance the forecasting power by supplementing internal historical data with varied public web data.

In this case, it is important to acquire high-quality datasets from reputable providers.

Identifying key metrics for particular business objectives

Particular recruiting metrics may be linked directly to general business objectives and long-term workforce plans, which means that a recruiting analytics solution can help to advance them.

For example, if a company wants to cut business expenses, one should look at the cost per hire to see whether they are getting their money's worth or overspending in that department. If building a brand is one of the company's central objectives, they will want to take care of their employer's reputation and overall brand image.

Therefore it would be helpful to collect feedback from job candidates.

Creating and evaluating hiring strategies

Using predictive analytics to anticipate hiring needs is the first step toward designing an optimal hiring funnel. Once a hiring plan is created, HR can build hiring strategies by making decisions such as which sources to use and to what extent, the content of application forms, and how to rank the qualities of the candidates.

Evaluating these strategies, for example, by employing the above-mentioned A/B testing, would ensure that companies are utilizing the best hiring option.

Promoting collaboration between humans and AI

Human HR specialists and AI tools have different strengths when advancing hiring goals. It is important to promote their proper collaboration to use HR analytics to its fullest potential.

On the one hand, this means always being on the lookout for the best affordable AI solutions.

And, on the other, it means promoting a data-driven outlook in HR that would make specialists eager to use those tools for the best hiring results.

Levels of reporting and analytics

There are three main levels of hiring analytics: operational reporting, advanced reporting, and predictive data analytics in recruitment.

Operational reporting

The first level entails descriptive analytics, mostly consisting of common recruiting metrics such as cost of hire, source of hire, selection ratio, and other metrics listed above.

Recruitment analytics software, such as the applicant tracking system (ATS), can be used to easily and effectively capture the listed metrics. These measurements are based on historical recruitment data and almost no other calculations are needed to make reports.

Advanced reporting

The second level is a little bit more complex because it requires a combination of several data sources.

For example, analyzing candidate experience might require the use of surveys, questionnaires, or other sources for recruiting data analysis to accurately connect the information.

There are several other metrics that can be used for this level of reporting, such as cost per candidate on different sourcing channels, recruitment funnel conversion, employer branding, and more.

Predictive analytics in recruitment

The third and final level consists of statistical analysis, segmentation, and building different people models. Predictive analytics revolves around building predictive models and strategic business goals to help find the best candidates that can fulfill them.

For instance, when it comes to segmentation, the recruiting team can boost hiring efforts by establishing target groups for specific job openings. It helps optimize the costs and conversions by testing different job seekers against different job ads.

Also, it helps to look for qualified candidates who could help achieve the strategic business goals defined by the company.

person analyzing information

A few more ways to use recruitment analytics

One key metric that's important to the HR department is employee turnover rate. It is calculated by dividing the number of employees who left the company in a specified period of time by the average number of employees during that period. To get the percentage, the result is then multiplied by 100.

As changing employees is costly, it's important to keep track of it. By employing predictive analytics, companies are able to not only measure turnover rate but predict someone leaving. This allows us to take adequate measures and improve retention rates.

Recruitment analytics is also used when applying A/B testing in HR. For example, HR can test two new hire skill development programs against each other to see which leads to better results. In order to do this, it's necessary first to measure the possible impact of various recruiting metrics on the results.

Wrapping up

Data-driven hiring and recruitment analytics are some of the recruitment solutions sure to be a common part of HR in the future. It helps the overall talent acquisition process and helps build viable recruiting strategies.

Analysis of the recruitment data provides insights that stretch far beyond the HR department and are relevant to the general company goals.

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