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AI Predictive Analytics for HR: 7 Best Practices and Use Cases

AI Predictive Analytics for HR


Companies worldwide leverage predictive analytics to forecast future events and make better decisions. This process uses emerging technologies like machine learning and artificial intelligence (AI) to turn historical data into actionable insights.


In HR, predictive analytics can streamline recruitment, reduce turnover, and boost employee retention. It can also help organizations improve their diversity, equity, and inclusion (DE&I) efforts.


By reviewing the data, HR teams can determine how to attract a more diverse set of potential candidates. After that, they’ll take the steps needed to increase diversity in the workplace, which can benefit the company’s bottom line.


Whether a startup or an established business, you can use AI predictive analytics to improve HR performance. Here’s what you should know about it.

What Is AI Predictive Analytics?


AI predictive analytics uses historical data to uncover trends and patterns that can help forecast future outcomes. Simply put, it provides a data-driven gaze into the future.


This technology has applications in various industries, from sales and marketing to manufacturing. HR departments can use it to detect patterns, correlations, and upcoming trends based on past and present data, such as:


  • Employee performance reviews
  • Job applications
  • Recruitment practices
  • Employee engagement
  • Turnover rates


Predictive analytics tools can collect, process, and analyze this data within minutes and generate insights.


For example, it can help identify the skills or personality traits required for a specific role. HR teams can also leverage predictive analytics to forecast workforce demand or employee attrition.


How HR Can Benefit from Predictive Analytics


One in four organizations uses automation or AI to streamline HR operations. This trend is more prevalent among large enterprises, but any company—big or small—can leverage AI to optimize its recruitment and hiring practices.


Predictive analytics enables HR teams to identify and manage talent, reduce time-to-fill, forecast turnover, and develop better training programs. For example, if your employees perform differently than expected, you can use predictive analytics to identify areas where they may be struggling.


With this approach, you’ll analyze their project completion rates, performance ratings, and other data to find the root causes of the problem. These may include heavy workloads, skill gaps, ongoing workplace conflicts, a lack of training, etc.


Now, let’s see other potential uses of predictive analytics in HR:


  • Identify high-potential candidates
  • Predict future skill shortages
  • Develop employee training programs to address specific skill gaps
  • Address potential biases in HR processes
  • Foster a diverse and inclusive workplace
  • Optimize overhead costs by predicting future staffing needs
  • Identify and mitigate compliance risks
  • Make proactive decisions by forecasting workforce trends
  • Determine appropriate salary ranges for each role based on industry benchmarks


According to a SkyQuest Technology survey, 70% of companies use workforce analytics (which includes predictive analytics) to cut costs and improve decision-making.


After implementing workforce analytics, one organization experienced a 10% improvement in talent quality and a 5% reduction in time-to-hire. Another saw annual savings of $30 million due to lower turnover and more efficient hiring processes.

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How to Get the Most Out of Predictive Analytics


Predictive analytics can help reduce turnover, streamline the recruitment process, and allow employers to understand their staff better. But first, you need to know how to use these tools to their full potential.


With that in mind, here are seven best practices for implementing a predictive HR analytics system.




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1.      Define Your Business Objectives


Think about what you want to achieve with predictive analytics. Whether improving talent acquisition, reducing turnover, or enhancing performance, you must set clear, realistic goals.


Let’s say you’re trying to find the right way to deal with micromanagers. Your team leaders may be good at their jobs, but their behavior affects employee morale. So, you want to address this issue without turning them off.


First, gather historical data on team interactions, work processes, and other relevant aspects. This information may come from employee surveys, performance reviews, project logs, and HR reports.


You’ll also need to look for common indicators of micromanagement, such as frequent status check-ins, high intervention rates, and poor employee morale. Leverage historical data to collect these insights.


Next, use AI-powered predictive analytics tools to identify the root cause of this behavior, its impact on work performance, and when it’s most likely to occur.


Based on these insights, develop targeted interventions to end micromanaging in your organization. These may include manager leadership training and coaching programs, one-on-one meetings, or other strategies.


Monitor the results while updating your predictive models to ensure their relevance.


Understanding what you are solving for matters most. Identify the problem you’re dealing with, define the expected outcome, and use predictive analytics to decide on the next steps.


2.      Choose the Right Tech Stack


Predictive analytics isn’t a standalone tool but a technology that requires integration with various platforms and tools. Therefore, you must evaluate your current HR tech stack and see what else you need to make predictive analytics work.


Start by asking yourself the following questions:


  • What kind of software are you using for HR tasks?
  • Does it have the ability to analyze data and make predictions?
  • Does it have built-in data modeling tools?
  • How do you plan to collect data?
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Moving forward, you may need to upgrade your tech stack or invest in additional tools for:


  • Data mining
  • Text mining
  • Data visualization
  • Data warehousing and integration
  • Statistical analysis


For example, data enrichment tools can provide additional information on potential or current employees and other businesses in your industry.


With these insights, you can build more accurate predictive analytics models and enhance your decision-making process. Similarly, sales and marketing teams can leverage data enrichment to target the buying committee and close deals faster.


3.      Define Your Data Sources


The more data you have, the more accurate your forecasts are likely to be. But you must also consider data accuracy, relevance, and completeness. For best results, collect and analyze data throughout the entire talent lifecycle, from recruitment to offboarding.


Take recruitment data, for example. These insights can help you identify the most effective channels for candidate sourcing, such as job boards, social media, or employee referrals. You can also use this data to optimize your candidate screening process, predict the likelihood of candidate success, and forecast time-to-fill.


Performance data can help you determine which candidates would be a good fit for your team.


Based on this data, predictive analytics can forecast future engagement levels, identify turnover risks, and uncover areas where employees may benefit from training.




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4.      Build Cross-Functional Teams


According to SkyQuest Technology, 39% of the companies that don’t use workforce analytics cite a lack of skills as an obstacle. About 40% said they didn’t have the budget to implement this technology, and 31% admitted they needed more data.


Most HR teams need the technical know-how to implement predictive analytics. Therefore, you may need to outsource this process or hire specialized staff.


Generally, it’s best to form cross-functional teams with expertise in HR, IT, business strategy, and data science. This practice ensures a holistic approach, allowing organizations to get the most out of their data.


If that’s not an option, contact a third-party provider like Helmes, Flatworld Solutions, or ScienceSoft. Outsourcing is often cheaper and more convenient than hiring an in-house team, but you must choose a partner that follows data privacy regulations.

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5.      Consult Your Legal Team


Data privacy should be a top concern when implementing predictive analytics. Depending on your company’s location and industry, you may need to comply with the GDPR, the CCPA, the HIPAA Privacy Rule, and other data protection regulations.


Simply put, it’s essential to collect, store, and use employee data in accordance with the law. Otherwise, you may be subject to fines or other penalties.  Stay safe and consult your legal team or an outside attorney.

If you have the budget for it, consider hiring a data compliance specialist.


6.      Check for Bias


AI systems, including those used for predictive analytics, learn from training data. Therefore, they are only as good as the data that you provide the systems to analyze.


A system trained on incomplete or biased data will generate errors and inaccurate outcomes. AI bias can result in discriminatory hiring practices, keeping organizations from attracting qualified talent.


One solution is to perform bias checks regularly. Review your data every few months to ensure it’s complete, accurate, and consistent.


7.      Commit to Continuous Improvement


Building predictive analytics models is an ongoing process. Not only is it necessary to test and validate the model before deployment, but you must also refine it continuously.


As mentioned earlier, AI systems learn from the date we provide them. Since data is constantly changing, companies need to adapt their analytics strategies accordingly.


Also, remember you must comply with the ever-changing data protection laws and regulations. That’s one more reason to review and optimize your predictive models constantly.


Streamline Your HR Processes with the Latest Technology


AI predictive analytics are reshaping the HR landscape. This technology can lead to faster, more accurate decisions, reduce human error, and provide strategic insights. Plus, it may help improve the candidate experience and boost employee productivity.


One thing to keep in mind is that predictive modeling requires a human touch. Its accuracy depends on your data and other aspects, such as your tech stack and analytics strategy.


You also need to continuously refine your analytics models, perform validity checks, and comply with data protection regulations. Most importantly, use this technology to support your HR team, not to replace the human element.



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Is your workplace truly amazing? It’s time to find out! Take the Amazing Workplaces® survey and certification – your chance to benchmark your company against top performers, uncover hidden strengths and weaknesses, and embark on a journey to build a workplace that’s the envy of your entire industry. Know more



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