Predictive Analytics: Does Your Hiring Strategy Need It?
We’d all like to know what the future will bring. If you had the power to peer even one day into the future, can you imagine how much more effective your decision-making process would be?
Of course, as mere mortals we can only venture guesses about what is to come. Nevertheless, we can make those guesses as educated as possible by means of a powerful tool — namely, predictive analytics.
What is Predictive Analytics?
Simply put, “predictive analytics” is a way to harness historical data to make future predictions. In the context of HR and recruitment, predictive analytics can help a hiring manager to make the best possible decisions when filling the company’s pipeline of candidates, choosing between job applicants, and determining where the next hiring need may arise.
Predictive analytics is not to be confused with prescriptive analytics. What is the difference between predictive and prescriptive analytics? Think of it this way: predictive analytics is concerned with the question: “What will happen?” In contrast, prescriptive analytics seeks to answer the question: “How can we achieve the best outcome?”
Why is Predictive Analytics Important?
Predictive analytics is an important part of any effective hiring strategy for three primary reasons:
- The job market is increasingly competitive.
- Recruiters are increasingly sophisticated.
- Artificial intelligence and hiring are now deeply interconnected.
Research bears this out. For instance, top job candidates only stay on the market for an average of 10 days before getting snapped up. That’s not a lot of time for a hiring manager to find them, vet them, and recruit them!
With predictive analytics, managers are able to make faster and more accurate predictions, which in turn lead to quicker and better hiring decisions. Interestingly, Gartner reports that only 21% of HR leaders believe that their companies are making good use of talent data. In other words, effective use of predictive analytics can potentially catapult your organization far above your competition. (You’ll be the rock star of HR!)
Where Does the Data Come From?
The raw data that serves as the foundation for predictive analytics can come from a variety of sources. For example, a hiring manager that wants to identify the job applicants that are most likely to stay with a company for 5+ years can assess previous hires within the organization. The manager can compare qualifying characteristics and identify high-risk indicators for early departure. The more data that the manager collects, the more likely it is that significant patterns will emerge.
Such data can be compiled from applicant information on file, or even sourced from surveys of current employees. In addition, you can collect data from external sources, such as social media profiles of potential candidates, fluctuations of competitors’ stocks, and the employee turnover rates of other organizations.
How Can Predictive Analytics Help Your Hiring Strategy?
There are several potential advantages associated with the use of predictive analytics in the hiring process. Here are just 4 key benefits to consider:
1. Improved Quality of Hire
Perhaps the single most important function of predictive analytics is to inform and improve your choice of hire. By integrating data points from production performance, attrition and turnover data, engagement survey information, and other sources, you can create an “ideal candidate profile” for an open position. Then, you’ll be able to match applicants to the profile and predict their future performance. (Of course, this ideal profile should be constantly refined for optimal results.)
2. More Efficient Sourcing
Talent sourcing is closely linked to quality of hire. Moreover, hiring managers should never overlook speed of hire as a measure of their strategy’s success. Predictive analytics can help on both counts.
For example, by compiling and analyzing data on various hiring sources (such as staffing agencies), you can determine which firms have yielded top quality talent, and which ones have offered poor candidates. Then, by eliminating those sub-optimal sources, you can contribute to the effectiveness of the overall hiring process. The same principle can be applied to in-house recruiting, job boards, etc. (This process of collecting and analyzing sourcing data can also help you to negotiate better pricing with third party recruiters.)
In addition, many predictive analytics tools allow hiring managers to identify “passive candidates” (i.e., candidates that are not currently searching for a job) that are most likely to become active candidates in the near future. As a simple example, suppose that an employee at a competing company is becoming increasingly frustrated with his working conditions — and voices some of those frustrations on social media. An advanced predictive analytics tool can search and analyze the employee’s public account, and flag him as a prospective candidate in the next 90 days.
3. A Faster Hiring Process
As you continue to develop your predictive analytics model, you’ll gain deeper insights into which candidates offer the highest upside in terms of performance. Then, you’ll be able to use those insights to deploy screening tools that can serve as indicators of each applicant’s potential. Examples of these tools could include:
- Personality assessments
- Skills questionnaires
- Education/experience requirements
Whatever the case may be, once you identify a candidate that meets all of your criteria for a solid hire, you can quickly engage with them and move them through the hiring process. This will eliminate a lot of the guesswork and “judgment calls” out of your hiring strategy, and everything will speed up as a result.
4. Reduced Employee Turnover
A high employee churn rate can incur significant costs for a business. When you factor in the time and resources needed to hire a replacement, it’s no surprise that employee turnover can cost approximately 150% of an employee’s annual salary.
The thing is, most causes of employee turnover are preventable — and predictive analytics can help you identify those causes and “nip them in the bud.” For instance, predictive analytics tools can analyze such factors as an employee’s current market compensation ratio, the elapsed time since the employee’s last raise, the employee’s tenure, and so forth, and estimate the level of risk for departure.
Wrap-Up: Predictive Analytics
It’s true that no one knows what tomorrow will bring. But with the help of predictive analytics, you can at least make an educated guess — and your hiring process will be much smoother as a result.