Evolution of Recruitment Analytics
The late William Edwards Deming, who was a renowned American statistician, professor, author, lecturer, and consultant, is credited with the phrase, “In God we trust, all others bring data.” Without data and analytics it’s impossible to accurately forecast what your organization’s recruitment strategy should look like, let alone make directional decisions about capital investments or branding.
Today’s applicant tracking systems (ATS) and candidate relationship management (CRM) systems can contain as many as 2,500 data points about each prospective candidate or applicant, and there are as many if not more data points about how the individual came to be an applicant in the first place, so recruitment departments have no shortage of data to analyze. Yet organizations are still struggling with recruitment analytics because often they don’t know which questions to ask, which data points are the most relevant, and the difference between data analysis and predictive analytics.
In order to understand how to best make progress in this area, it is important to align your desired outcome with the most relevant business activities. For example, if you are looking to develop predictive analytics then traditional reporting won’t help, and if you are looking to understand historical data points then extensive data reporting is probably overkill.
Once organizations align and settle in on their desired outcomes, asking the right business questions will help to frame what is being measured, what should be analyzed, and what kind of predictive analytics can help to provide the organization a competitive advantage
Analytics can be structured as historical, relational, and predictive, and while not all-inclusive, they can measure satisfaction, workforce productivity, sourcing, and recruiter performance. Each area in itself can have dozens of measurement points so it is critical for people to analyze what is important and let machines analyze the superset of information, which could be too onerous for people to analyze using traditional tools and techniques.
Big Data has ushered in the possibility of making recruitment departments more strategic, and effective at the same time, by granting access to data previously unobtainable. As a result, recruitment analytics have evolved beyond traditional information such as source of hire or time to fill a vacancy, time to source candidates. Today’s recruitment analytics platforms can get much deeper and broader to enable you to strategically target geographies, even specific neighborhoods or competitor office buildings, to attract top talent. You can also know which social media channels are most effective for filling specific roles, and even the best day to Tweet your jobs.
Beyond recruitment analytics is an area called predictive analytics, which uses sophisticated algorithms supported by machine learning to help organizations predict future outcomes. For example, organizations can predict which sourcing channel will provide top talent; they can match ideal candidates to open positions; they can know which candidates to target first based on their job-seeking behavior; or even predict ideal times to contact potential candidates with a new job offer enabling better workforce planning.
The recruitment industry has made great strides towards fostering development of strategic and predictive recruitment analytics, and underlying technology that can animate static data into actionable plans. The next decade promises to be an exciting time to be in the field of recruitment, as the rate of technology innovation is making the recruitment function within organizations smarter than ever before.