Is the Future of Hiring in Predictive Analytics?

Predictive analytics is rapidly transforming the recruiting process, helping organizations anticipate their hiring needs and identify best-fitting candidates with a high degree of accuracy. If you use any software to assist with hiring, like an ATS, chances are you’re already using some form of predictive analytics in your workflows, even if you’re unaware of it. 

With machine learning and artificial intelligence tools becoming more advanced daily, predictive analytics’ role in hiring is only set to grow. We’ll cover how organizations can benefit from it, share how to incorporate predictive analytics into your recruiting strategy, and identify the top challenges to be aware of as you adopt new technology. 

What is Predictive Analytics?

Predictive analytics is a form of data analysis that uses historical data, complex algorithms, and machine learning to identify patterns and predict future outcomes. Companies can leverage predictive analytics to pinpoint business trends, forecast future events, and make more informed operational decisions. Recruiting is just one of the many areas in which predictive analytics can improve business outcomes.

Predictive Analytics in Recruiting

Incorporating predictive analytics into recruiting processes offers numerous benefits for organizations, including the ability to: 

Forecast staffing needs

Data models can leverage historic staffing data, employee performance metrics, and information about market conditions to make intelligent and highly accurate predictions about who companies should hire and when. For example, predictive analytics could identify an uptick in customer activity in a certain region at a specific time of year, indicating precisely when and where you need to ramp up hiring. It can even forecast when employees are likely to be job searching, informing your retention and strategic succession planning efforts. 

Identify hiring criteria

Predictive analytics can analyze the characteristics of an organization’s most successful employees–say, high-performing salespeople—to create a shortlist of traits recruiters should prioritize when hiring. More precise screening leads to a stronger talent pool. 

Related: How to Shortlist Job Candidates

Predict candidate success

Data modeling can predict a candidate’s aptitude for success in a particular role, alignment with a company’s culture, and likelihood of accepting an offer. Greater accuracy in these areas reduces the incidence of bad hires and can facilitate a smoother onboarding process. 

Make hiring more strategic

Whereas traditional recruiting methods rely heavily on hiring managers’ decision-making abilities, predictive analytics allow you to make data-based decisions. This makes hiring more strategic, promotes greater efficiency, and reduces rushed decision-making that can lead to hiring mistakes. 

Benefits of Using Predictive Analytics in Hiring

Be proactive versus reactive

Traditional hiring is often reactive–you hire only when you realize you have a need, which means you’re already behind. Predictive analytics allow you to anticipate the need before you start to feel the pain of a staffing gap, allowing you to take your time narrowing the field to just the right person instead of rushing to hire someone out of urgency. 

Improve accuracy

We’ve known for a while that data leads to better hiring decisions–just look at the prevalence of pre-hire assessments for objectively judging candidates’ skills. Predictive analytics foster hiring decisions that are rooted in data rather than a hiring manager’s instinct, which results in more accurate placements. 

Reduce costs

With the help of predictive analytics, you can hire when you need to and not when you don’t, avoiding unnecessary hiring costs. And, because AI tools help you approach recruiting with a clear picture of what’s ahead, you’ll also avoid the expense of last-minute hires, which can be costly. 

Lower turnover

One of the most common reasons people say they’d quit a job is if the position doesn’t match what they expected. Predictive analytics help you select candidates who are better aligned with your open roles, resulting in stronger new hire satisfaction and less risk of turnover. 

Lighten workloads

Predictive analytics and other AI tools have immense power to tackle time-consuming screening tasks. This can reduce the burden on busy recruiters, freeing up more of their time to interact with candidates and speak with a greater volume of people. 

Personalize candidates’ experience

Personalization is the crux of an excellent candidate experience. Machine learning technology helps companies tailor the experience candidates have based on what they’re most likely to engage with. Sending the right communications at the right time boosts your ability to win top talent. 

Downsides of Using Predictive Analytics in Hiring 

Infrastructure

Depending on your needs and goals, implementing AI recruiting tools can require complex data management systems and specialized software. In addition to substantial setup costs, you’ll also need to budget for ongoing maintenance and troubleshooting.

Resistance to change

As with any new technology, there will likely be hurdles in getting buy-in from company leaders, stakeholders, and the staff who will use it. You’ll need to invest resources to educate team members on the new tools and workflows and provide ample training. 

Privacy and security 

The volume of data being stored and accessed by predictive analytics systems is massive, which raises a new level of concern for its security and the privacy of those it belongs to. Organizations must be vigilant about obtaining employee and candidate consent, following relevant data protection regulations, and being transparent about how data is used. 

Quality control

The outcomes of machine learning tools are only as good as the data that goes into them. This requires companies to be attentive to data integrity and implement regular audits and monitoring strategies. 

Lack of human element

There’s no doubt that hiring great people requires a human touch. Too much reliance on technology can mean organizations lose the human element that separates great recruiting departments. For example, a machine can’t replicate the nuance of an in-person conversation or gain the same level of context that we can (at least, not yet) from body language and non-verbal cues. 

Strategies for Adopting Predictive Analytics in Hiring

Gather data systematically

Robust, high-quality data is the foundation of any successful predictive analytics recruiting initiative. Implement systems for capturing complete data across all departments and sources involved in the hiring process, including resumes, applications, applicant tracking systems, HR software, email communications, assessments, performance reviews, etc. 

Choose technology wisely

It might be as simple as choosing the right software for small to medium-sized businesses. For enterprise organizations, this may require working with data scientists to develop effective models customized to your needs. As you select the right technology, consider ease of use, ability to integrate with other HR systems, scalability, and support options. 

Provide proper training

As we’ve already touched on, proper training is crucial to overcome initial resistance and get the most from your predictive analytics solutions. The data produced by predictive analytics is often complex; thus, it’s critical to ensure that HR professionals and hiring managers have a thorough knowledge of how to interpret and act on the insights. Provide continuous learning opportunities to keep team members current with new technological developments. 

Incorporate slowly

Don’t overhaul your entire hiring process overnight. Introduce AI tools gradually to avoid disruptions and minimize pushback. Focus on one specific goal at a time, assessing progress, troubleshooting issues, and gathering feedback before moving on to another new system. 

Refine over time

Using predictive analytics in hiring isn’t a one-time implementation. Instead, it requires continuous refinement through ongoing monitoring, updates to data models, and input from your team. Staying responsive to changes in technology and the recruiting industry will ensure your use of predictive analytics produces long-term results. 

The Future of Predictive Analytics in Recruitment

So, is predictive analytics the future of hiring? The answer is a resounding yes. 

Organizations across industries have already begun transitioning to data-based hiring, with many early adopters reporting impressive outcomes. As success stories become more prevalent, more companies will adopt the technology. As algorithms become more refined and effective, predictive analytics will expand into other HR functions like employee development and engagement.

To be sure, there will always be a human element in hiring. Talented recruiting teams are necessary to manage, optimize, and get the most out of even the most advanced AI systems. However, the skills required for the next decade of recruiting will be different from the last, with a heavier emphasis on data analysis and knowledge of data modeling. On the flip side, uniquely human skills like communication and empathy will also set great hiring teams apart. 

Starting today, incorporating predictive analytics into your hiring strategy will help you make smarter, data-driven hiring decisions that contribute to a dynamic workforce and long-term success.