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Recruitment is one of the most challenging and complex tasks, as it requires a lot of data, analysis, and strategy to make informed decisions. You also need to be able to anticipate the future trends and needs of the talent market, and align their recruitment strategy accordingly. This is where predictive HR analytics can be a game-changer for recruiters.
Let’s dig a bit deeper, learn more about predictive HR analytics, and see how you can use it to elevate your recruitment game.
Predictive HR Analytics is the use of data mining, statistical techniques, and machine learning to analyze historical and current data, and predict future outcomes and behaviors.
Predictive HR Analytics is a branch of People Analytics that focuses on forecasting future outcomes and behaviors based on historical and current data. It uses various methods and tools to collect, process, and analyze data from various sources, such as resumes, assessments, interviews, performance reviews, surveys, social media, etc.
Then, predictive HR analytics applies algorithms and models to identify patterns, trends, correlations, and causal relationships among the data. Based on these insights, it can generate predictions about future events and actions, such as who is likely to apply for a job, who is likely to perform well in a role, who is likely to leave the organization, etc. Predictive HR Analytics can also provide recommendations and suggestions for improving the recruitment process and outcomes, such as which candidates to target, which channels to use, which messages to send, which offers to make, etc.
Predictive HR Analytics can provide many benefits for recruiters and HR professionals. Some of the main advantages are:
Predictive HR Analytics is not a one-size-fits-all solution. There are different types of predictive models and methods that can be applied to different aspects and stages of the recruitment process. Some of the common types of predictive HR analytics are:
This type of predictive analytics helps recruiters to identify the best sources and channels for finding qualified candidates for each role. Sourcing analytics can also help you optimize your employer’s branding and marketing strategies, as well as attract more passive candidates.
Screening Analytics is designed to help recruiters filter and rank candidates based on their fit for the role and the organization. It can use various data points, such as resumes, assessments, video interviews, etc., to evaluate candidates’ skills, competencies, personality, values, and cultural fit.
With hiring analytics, recruiters can make better hiring decisions based on data-driven insights. Hiring analytics uses various data points, such as performance reviews, feedback, surveys, etc., to predict candidates’ potential, performance, retention, and impact on the organization.
This aspect of predictive analytics helps you improve your relationship with candidates throughout the recruitment process. it pulls information from various data points like email open rates, click-through rates, response rates, etc., to measure and optimize candidates’ interest, satisfaction, and loyalty.
Predictive HR Analytics is not a theoretical concept. It is already being used by many organizations across different industries and sectors. Here are some examples of how predictive HR analytics is applied in real-world scenarios:
The search engine Titan is one of the pioneers and leaders in using predictive HR analytics for recruitment. Google uses an algorithm called QDroid to screen and rank candidates based on their resumes and online assessments. It can predict candidates’ future performance and retention based on their past achievements and behavior patterns, which helps improve the quality of hire for Google. [1]
Another good example is IBM, which utilizes predictive HR analytics to improve its talent acquisition and retention strategies. The international business machines firm leverages a tool called Watson Talent Frameworks to create customized job descriptions based on the skills and competencies required for each role. The tool also helps IBM ensure the right talent by suggesting the best candidates for each role based on their fit with the job requirements and the organization’s values. [2]
Last but not least, the household name in the e-Commerce game uses predictive HR analytics to improve its recommendation system for customers. Amazon applies a tool called Item-to-Item Collaborative Filtering to analyze customers’ purchase history, ratings, and browsing behavior, and recommend products that they are likely to buy. This tool can also be used to recommend jobs to candidates based on their skills, interests, and preferences. [3]
To implement predictive HR analytics effectively, you need a reliable tool that can collect, process, analyze, and visualize data from various sources.
An ATS can help recruiters to automate their tasks, streamline their workflows, track their progress, and enhance their candidate experience. Manatal is a cloud-based, AI-powered recruitment software that comes fully equipped with reports and analytics features you can use to measure and optimize your recruitment efforts.
Manatal offers a range of features and benefits for recruiters, such as:
Imagine having the power to streamline the recruitment process while being able to conveniently pull out important data and come up with a stronger hiring strategy within a few clicks. Sounds good, right? All of that can be possible with Manatal.
Don’t wait any longer. Start a 14-day Free Trial today, discover Manatal’s innovative features, and see how it can transform your recruitment within a few clicks!
Citations:
1. Here's Google's Secret to Hiring the Best People | WIRED
3. The history of Amazon's recommendation algorithm - Amazon Science
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