Finding the right talent – and fast – is a challenge for any business.  The current job market and skill shortages require effective recruiters have as much information as possible before a recruiting cycle begins.  Companies are in a race with one another to fill vacancies, and to make matters more complex, your organization might already be trying to fill multiple of these positions at once. To top it all off, there is more hiring than ever in sectors such as healthcare, technology and finance, leading to a shortage of candidates with the right qualifications. 

 

What if you could give your recruiters an edge over the competition? Putting real-time data in the hands of your recruiting team could be the ticket to getting the right candidate at the right time.

 

Here is an example: Increased demand for prescription medications is leading to greater demand for people qualified to work in pharmaceutical services.  Your company is hiring pharmacy technicians and needs to find candidates in specific market areas with the education, experience and certification for the role, and of course you are not the only company looking at that very same pool of potential candidates.  Just a few targeted data points can give your recruiters a head start.

 

Before beginning to recruit for the position, a simple report like the one featured above can answer a lot of questions.  How many other positions like this are already open within your organization? On average, how long does it take to fill a position like this, and how many applications typically need to be reviewed before finding the right candidate?  Based on previous positions filled, are there candidates already in the ATS that might be a good fit?  The search has just begun, but already you have an idea of how long it might be before the position is filled, and a list of potential candidates in that market area who have already expressed interest in your company. 

 

Leveraging a “Silver Medalist” concept can use reporting to surface candidates your organization may have already interviewed or engaged.  Recruiting has always struggled to fulfill the promise of “we’ll be in touch if something similar comes up.”  Talent Analytics can serve up the candidates you have already invested in, as well as dramatically shrink cycle time.

 

Once the requisition is active, another set of data like the one pictured below can show the progression of candidates at a glance as well as how many candidates are active, the number of days the position has been open versus an established service level agreement, and even a risk level for filling the position.

 

Active Requisition Snapshot

 

 

With an average open requisition load of 25 positions, many recruiters struggle just to keep up.  Providing insight into earlier efforts and the complexity of filling a position better prepares the recruiter for key conversations with the hiring managers, ultimately improving the services they provide.  The two examples highlighted here create a solid foundation for understanding where resources and energy should be directed.  Furthermore, when tied to business results it can create predictive models to be used across the organization.

 

The case for recruitment analytics is clear; done well, data directs activity and energy to where it is most needed for better results.  Armed with this information, recruiters can quickly make informed decisions which in turn will speed the process of finding the right candidate.

 

A key differentiator of Taleo is the ability to retrieve and present information.  If you are not leveraging analytics like those above, it is not too late to get started.  Your system has been collecting data for as long as it has been active, meaning you likely already have a rich dataset to draw from.  The key is transforming the data into actionable intelligence.

 

Need help with your analytics?

 

Look for future posts in our analytics blog series where we dig in to common pain points in the recruiting process and show how data & analytics – applied effectively – can make a big difference.