June 23, 2021
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 and how hiring results correlate to successful business practices. Likewise, big data analytics are increasingly being adopted in business and operation, as there could hardly be a department that would not benefit from utilizing alternative data and analysis tools.
Ultimately, when applied to hiring, this data-driven approach constitutes recruitment analytics. This procedure investigates all parts and aspects of the hiring process in the company, aiming at forecasting future hiring needs and overall improving the 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.
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 and other tools to efficiently find the right candidates for the role and, more importantly, their company. 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 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.
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.
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.
Historical data can also be used to forecast how much hiring will need to be done in particular upcoming periods of time. Predictive analytics uses statistical modeling and machine learning methods to analyze such historical 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.
Recruitment analytics is all about 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.
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.
Recruitment analytics can achieve the benefits listed above by applying the scientific method – measuring restlessly. 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 important insights. Here are some metrics that are commonly used to measure recruitment.
Time to hire measures how much time passes from publicly announcing an open position to successfully filling the role.
Hiring costs include all resources used in order to hire, for example, 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.
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 an applicant tracking system.
Offer acceptance rate measures what share of job offers made by the company has been accepted.
This is a complex metric that can be defined differently depending on what 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.
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.
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.
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 alternative data. In this case, it is important to acquire high-quality datasets from reputable providers.
Particular recruiting metrics may be linked directly to general business objectives, which means that recruiting analytics 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 reputation and overall brand image. Therefore it would be helpful to collect feedback from job candidates.
Using predictive analytics to anticipate hiring needs is the first step towards designing an optimal hiring plan. 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.
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 best hiring results.
Data-driven hiring and recruitment analytics are sure to be a commonplace part of HR in the future. Analysis of the recruitment data provides insights that stretch far beyond the HR department and are relevant to the general company goals.