blog post

Predicting Predictive Analytics


BY PARTHO GANGULY: Co-Founder GWFM; HR Tech Startup Mentor, Speaker, Mentor, Author, Design Thinker

As the world progresses and technology advances, our lives change rapidly with each passing day. The domain of recruitment is no exception. Today, the overall process of hiring and recruitment has undergone substantial changes especially with technology foraying in. Gone are the days when hiring and recruiting were largely dependent on the gut feelings of employers and they fell back on the years of experience that a candidate has. Today, analytics plays a significant role in the overall process of recruitment and has substantially redefined the practice of hiring.

Predictive analytics is emerging as an essential AI tool for employers looking to stay ahead of the competition. AI is transforming the talent acquisition process. As an AI tool, predictive analytics allows employers to use the power of data to make predictions about candidates and drive efficiencies throughout the entire talent acquisition process.

What is Predictive Analytics?

Predictive analytics is a type of data analytics that uses data to find patterns and then uses those models to attempt to predict the future. Predictive analytics can’t tell you what will happen, but it shows what is likely to happen based on past trends. It’s as close as employers can get to predicting the future. The ability to make these predictions helps shorten the entire recruiting process while making stronger hires. In a competitive talent market, this allows employers to identify the top talent for a particular role and make an offer to the best candidates faster than the competition. For employers, this results in improvements in time-to-hire and quality-of-hire, and a better candidate experience, leaving a positive impression that will factor into their decision to accept an offer. Work is in progress to predict drop outs from a list of candidates offered. Predictive analytics in recruitment is growing rapidly in the realm of HR these days, and this increased attention is due to the following reasons:

The volume of data that can be consolidated online from social media posts to purchases.

An increasing reach of workforce analytics software and technologies that are becoming more accessible and available.

The capability of companies to generate algorithms from GDP, unemployment rate, and growth, turnover rate, and other workforce trends to predict their future needs for human resources.

Companies are becoming more proactive in their process of hiring.

To understand how to engage and retain employees for a long time

Improving Sourcing Outcomes through Predictive Analytics

During the sourcing process, predictive analytics models can identify stronger candidates more quickly and accurately than traditional methods. AI sourcing that uses predictive models can provide recruiters with a solid slate of candidates as soon as a requisition is opened, giving the recruiter a strong head start to fill the role. An AI based sourcing solution that uses Predictive Analytics helps the recruiter with information about how well the candidate matches the job opening and how likely the candidate is to leave their current role. With this information, recruiters are able to work more quickly and efficiently, filling the role with the best talent in less time. In the end, it saves companies time and money.

To roll out a predictive analytics sourcing tool, employers first need to establish what makes a good hire. This requires looking back into data from previous hires that demonstrates how well those hires performed. This step is critical because employers may find that the factors that predict success are not what they thought. For instance, when filling certain roles, employers may prioritize candidates with advanced degrees. However, data may show that an advanced degree is not a reliable predictor of success. Instead, industry experience or high scores on a pre-employment assessment may better predict the success of a candidate.

Once an employer has the data to identify qualities that predict success, the predictive analytics technology that’s part of an AI sourcing tool can use that information to identify candidates who match that criteria. The technology can scour social media sites, job boards, talent communities and networking and career sites to find the best talent. Using available data, the tool will make predictions about the candidate, and the recruiter can use those predictions to determine which candidates to target for more personalized communication. As recruiters use the predictive analytics tool, they constantly feed more data into the system. This means that over time, the technology is able to learn more and make even more accurate predictions about candidate success.

When implementing a predictive analytics sourcing tool, there are a few important considerations to ensure success. The first is making sure that data you use is good, accurate data. You need accurate information about previous hires, including pre- and post-hire information. Since the recruitment team is only engaged through the hiring and on boarding process, it’s important to share post-hire data that demonstrates whether the candidate made a strong employee. Knowing about performance or employee tenure will make predictive analytics tools more powerful. To make that data work for you, it is key that you share that post-hire data with your Recruitment Team.

Automation and predictive analytics generally takes care of all the major challenges the recruiter faces starting from sourcing the most relevant , screening, scheduling and post interview tracking. However the biggest challenge of the recruiter is the final on boarding numbers. How do you predict out of the total offers made how many will join and how many will vanish?

Most AI based TA platforms are still billions of kilometers away from this part of the predictive analytics. Other functions are basic level automation of TA processes the capabilities of the products will show once they try and incorporate decision trees into their analytics and more learning’s are fed into the machines. Use cases are now being fed to the machine hope fully it will make judgmental revelations once it learns.