Data, statistics, and analytics. They can inform and confuse. Data is both seen as beautiful and the cause for debate. When making evaluations in recruiting, data can supply a wealth of information about candidates but it can also confuse and overwhelm. However, when data driven recruiting is implemented, real-world statistics drive the decision-making process. Recruiters can then create hiring plans to increase efficiency, reduce costs, and improve the hiring process. Let’s take a look at what data-driven recruitment is, the benefits of data-driven recruiting, and how it can be used as part of a talent acquisition strategy.
Data driven recruiting is just what it sounds like, utilizing data and analytics to assist with the recruitment process. It allows for hiring managers to mine available data on candidates such as skill level and experience in an effort to better identify and gauge candidate interest for open roles. Once they have a sizable enough talent pool to sort through, they can then utilize technology to analyze recruitment channels and determine the best candidates for the position. Over time this process can be repeated and trends can be followed leading to new analytical insights and a better running recruitment process.
Arguably the largest benefit of data-drive recruitment is a decrease in the cost per hire. By focusing attention on the information provided by the candidate and having the ability to filter based on this recruitment data, the candidate pool will only feature the best fit candidates. By reducing the selection ratio, the hiring teams can focus on the most viable candidates rather than wasting time on candidates that aren’t a match. Since we all know time = money, decreasing the time spent creating a viable candidate pool leads to cost savings.
By analyzing the accumulated data, recruiters can make better informed decisions. This results in more relevant candidates getting selected from the candidate pool for interviewing. Furthermore, by understanding the most important recruiting metrics, recruiters can make a more informed choice even if the candidate does not interview well. Recruiters can implement assessments based on personality testing or skills competency tests. Doing so will ensure a better quality of hire.
Not only will utilizing recruiting data in hiring improve the experience for the client and the recruiting process, but a data driven recruitment process leads to a better experience for the candidate. Knowing the specifics can help to speed up the hiring process leading to better hiring efficiency metrics and a better and less distracting experience for candidates. Additionally, if candidate interest is lagging and candidates are dropping off at a certain point in the hiring process, it becomes easier to identify where the issue lies and the recruiter can hone the entire recruitment process to make it more efficient for candidates.
The last benefit we will discuss surrounds how the candidate data can be utilized for future forecasting by the recruitment team. With the known data in place, recruiters can determine the annual turnover rate, interdepartmental exchange, and the hiring process times. Further, budgeting and budget forecasting for clients becomes easier to determine. With an improved recruiting process, it becomes easier to create a data driven recruiting strategy and decide when and where to identify candidates for the best possible outcome based on data trends.
With the vast amount of data available it can be difficult to determine which areas to focus on in your data driven recruitment strategy to have a successful hiring process. Knowing the key performance indicators that are most relevant to your unique hiring situation will help to reach your recruiting benchmarks. Some of the more common recruitment key performance indicators in data driven recruitment include:
Metrics based on timing: These include time to hire, acceptance time, how quickly the candidates starts, and the time per stage.
Metrics based on quality: These include submission to acceptance rate, hiring source, number of applicants per opening, number of candidates, and retention rate.
Though these are the most important areas of data, it’s important to determine on a case by case basis which areas are the most important to track. If seeking improvement in some areas, it may be more important to focus on the statistics that provide the most feedback for this area. Optimizing your data will ensure better quality candidates and higher retention rates.
Though data never lies it can exaggerate the truth especially when each recruiting project is unique. Your data will not explain why something occurred, this is up to you to determine based on the metrics. You must come up with a hypothesis to determine if something happens then the predicted outcome will result in the common metrics you have amassed over time.
Additionally, the data you uncover will not solve all your problems. Data driven recruiting can help aid in determining trends and help for better forecasting. However as we’ve stated, each case is unique and while you can always plan for the best, the outcome may not always happen as your recruiting teams predicted.
Data driven recruitment can aid in your recruitment success rate and help enhance the entire hiring process. It’s important to recognize the metrics that are most important on a case by case basis and examine areas that are needing improvement. In addition, the utilization of the data by hiring teams will provide a better experience for both your client and the candidates. Recognizing which tools suit your needs best to examine this data is paramount. So be sure to try a variety of tools and software to determine which best suits your needs for each case.
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