Performance measurement and data gathering are huge trends in every aspect of business, and recruiting is no exception. Recruiting generates massive amounts of data, and understanding the data and using it to measure quality and performance promises a more efficient, effective recruiting process. Using this data wisely can highlight where good sources and hires are easily identifiable, while elimininating waste in time and money can be eliminated. In this week’s RPOA Roundup, we take a lot at how data and metrics affect recruiting efforts and industry trends.
Recruiting is one of the most important areas of a company, and companies need to be able to measure the effectiveness and success of recruiting efforts. This article suggests adopting a six-step process to accurately measure return on recruiting efforts. The steps include defining your goals, defining your metrics, defining your infrastructure, defining the solution, executing your plan, and revising and repeating the process.
Big data, which this article describes as “structured and unstructured data that is so big, and moves so fast, that it’s difficult to process using traditional processing techniques,” is here to stay. This article explores the concept of big data, its importance for company competition and growth, and how it affects the staffing industry. The article suggests that the talent shortage of today will combine with the advent of big data to create a dearth of skilled workers who can successfully leverage big data’s potential. It also takes a look at the question of what departments within a company will take ownership of recruiting data, and the potential implications for data mining success.
Measuring effectiveness is the best way for business functions to improve and increase their return on investment and success rate. However, according to this article, recruiting is the only major business function to not consistently measure performance and quality. This article proposes three reasons why businesses must measure the performance of new hires, and thus the effectiveness of recruiting. They are that the performance improvement of hires reveals business impact, that you can’t improve a process without measuring the quality of your output, and that being a data-driven function adds to recruiting’s ability to influence managers. The article then goes on to suggest a quality of hire measure that companies can easily implement, and rebuts arguments why recruiting cannot measure quality.
In recruiting, the quality of your sources is essential to your overall success. A source that yields twenty candidates that pass the initial screening is a better source than one that yields one hundred candidates that don’t make it past the application review process. This article suggests three metrics related to source quality that recruiters should be tracking to help identify good sources. These metrics include recruiting qualified candidates, or candidates that make it past the initial screening, team accepted candidates, who passes the phone interviews with the hiring team, and the cost per candidate, which can be calculated by dividing the total amount spent sourcing by the number of candidates you received.
Data promises wonderful things, but it’s important not to rush in without a clear idea of what you’re doing. This article emphasizes the importance of careful thought and taking the time to understand your data, which yields the most insight and value. It references the dot com boom of the early 2000s as a warning against jumping on bandwagons, and gives three reasons that companies risk losing out in the long run by diving headfirst into the data trend. These include companies not knowing what they want from their data, making short-sighted investments in data systems, and thinking that data is the magic wand that will solve all of their problems in one go.