Game Changer in TA: Actionable Predictive Hiring

By Archana Jerath July 27, 2018
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When an organization starts calculating the cost of turnover, it discovers the significant cost of rehiring and then re-training the new person.  There also is an opportunity cost due to a loss of business  while the person who has  left is replaced. But one of the biggest concerns is when a poor hire joins an organization.

To be sure, hiring is a risk. Predicting whether a person is the right match for a role and will perform per expectations seems difficult. Analytics, however, has shifted the hiring calculation by enabling companies to use actionable predictive hiring. At SHRMTech18, the  moderator, Rahul Mukherjee, VP - Policy & Operations, Reliance Jio, discussed this new trend in talent acquisition with panelists Ravikanth Reddy J, Founder & CEO of PQuest Human Resources Pvt Ltd, Debi Prasad Das, Founder & Chief Mentor of Talocity, and Tanuja Abburi, Founder of BeyondPinks.com.

What is Predictive Hiring?

Hiring the right person can make a big difference to an organization's business growth. "It is like flipping a coin, where there is 50% chance of hiring the right person," says Das.

Talent acquisition teams typically start the hiring process with a set of attributes that they have developed based on patterns that have been demonstrated by existing high-performing employees. This data is valuable for creating a forecast. Predictive hiring is about using that data to provide recruiters with more targeted options for potential hireswho are a close fit to the ideal candidate.

Depending on the kind of person an organization is keen to hire, analytics can be used to predict which candidates from the applicant pool are the best matches. That is what Predictive Hiring is – assessing the qualities of your existing people who are performing well, and trying to find those  qualities among prospective employees.  One key factor to keep in mind with this approach is that the defined success profile should be sharp and clear, so that it generates the right results when it shares potential candidates.

In a macro context, predictive hiring involves forecasting where your organization's business is going in the next 5-10 years and the skills that will be needed, and then hiring accordingly. Skill gaps will also need to be assessed and eliminated, which in turn will help sharpen the predictive hiring process.

"Reliance Jio did various kinds of unsupervised learning analysis to arrive at what the success profile for them would look like. Performance is one of the factors to evaluate," says Mukherjee. "Every correlation was tried in order to figure out what an engaged employee actually looks like." This depth of analysis is the basis for predictive hiring.

Alongside the attributes, the biggest parameter to be used for predicting the right hire should be culture.  "We have our own value systems internally and our own mindsets. But we need to understand what is relevant within the organization," says Reddy J. "That is what will help us take the right hiring decision. Predictive hiring, therefore, is about choosing the right parameters to utilize for decision making."

Steps that organizations can take

"Do decisions back data or does data back decisions?" asks Abburi. In other words, there is a high likelihood that some inherent biases play a role in hiring. The biases lead to decisions being made  before data is assessed to validate those decisions.  Organizations need to be aware of this issue and focus on generating the data before each candidate applies. That is the appropriate way to ensure reduction of bias. The challenge will always be to find the best ways to collect that type of data readily.

The next step is to understand the kind of business requirements needed to support how predictive hiring can help. Usually, the challenge is three-fold:  outreach, which means being able to reach out to a wide talent pool, followed by a tight time-frame within which to find the talent and, finally, the right skills for that job. This situation puts tremendous pressure on recruiters to find individuals who can be placed into required roles immediately. The impact of a wrong hire is so high in some industries, that one small mistake on the part of a person who is not a fit can lead to the loss of  significant business.

Another step that organizations need to take is to move hiring managers' mindsets towards data-driven decision making. The use of analytics in recruitment should become a natural part of hiring activity instead of a unique requirement. Most leaders tend to use their gut feeling or intuition to make hiring decisions. While experience does play a role in being able to assess a person's attributes, it needs to be backed by  data. Sensitizing managers and leaders to realize the scientific relevance and importance of making the right hiring decisions is a big step.

Organizations are constantly focused on managing  these steps effectively. They want to ensure that predictive hiring is being used to minimize business loss and potential issues. The important question that arises is how does an organization know that the process has been successful?  The answer is by putting the right metrics in place.

What are the metrics to consider?

Core metrics that should be used to assess the effectiveness of predictive hiring  starts with capacity fulfillment. This means the extent to which the required roles have been fulfilled through predictive hiring as a process.

The time taken to hire is the next metric to evaluate. This is a crucial element since one of the biggest advantages of predictive hiring is that it saves the time needed to find the right fit. Instead of looking for talent in a wide sea of applicants, predictive hiring zeroes in on a smaller and more specific sub-set of applicants who are relevant for your requirements, the panelists agree.  

Finally, measuring performance levels of those hired through this approach after a period of time is required to assess the accuracy and relevance of predictive hiring.

Predictive hiring is a boon to organizations because it gives them the chance to hedge their talent risk by being able to forecast it  effectively. It can also give answers to several questions that organizations have about the kind of talent that makes a difference to the company's goals and plans. Most importantly, it helps to uncover candidates who might be a great fit for the organization, but have been missed in the screening process carried out by recruiters, perhaps due to prevalent unconscious biases.

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