Predictive Analysis in HR: Benefits, Models, and Real-Life Use Cases

Imagine your journey as a credit card applicant. You'll fill out an application form, and the lender will review your personal details. They'll check your credit history, see if it aligns with their required credit score, and decide whether to accept or reject your application.

This is a tried-and-testing strategy they use to determine your creditworthiness, and it's all based on analyzing data. 

Why this approach? The lender must predict how responsible you'll be with their money. They use the data to estimate if you'll make or miss your credit card repayments and calculate how much of a risk you are to them. 

And this is the essence of how predictive analytics works in any business area.

In this guide, we'll dig into predictive analysis in HR, including the many use cases and benefits it brings to an organization. 

📊 How do you define predictive analysis in HR? 

Predictive analytics uses current or historical data, statistical algorithms, and machine learning techniques to identify potential future outcomes. In HR, we can use predictive analysis to predict employee behavior based on past performance, demographics, or other factors. This helps HR professionals anticipate future trends and plan accordingly. 

How does predictive analytics work? 

Predictive analytics is science-based, but in many ways, it mimics the natural human instinct to learn from past experiences and forecast future events. 

Example 1: If a child repeatedly gets detention in school for an untucked shirt, ideally, they'll quickly learn to tuck their shirt in to reduce the chance of future punishments. 

Example 2: If a sales representative hits a potluck with a particular sales technique, they will likely apply it repeatedly to maximize their success rate. 

Predictive analytics works similarly, but we use data to draw meaningful insights instead of relying on our gut feeling or intuition. It examines how different factors interact and identifies historical patterns to make informed decisions about the future. 

🆚 Descriptive vs. predictive vs. prescriptive analytics in HR

There are three main types of analytics in HR: descriptive, predictive, and prescriptive.

Here's how HR teams may use each of these analytic techniques. 

Descriptive analytics 

Descriptive analytics looks at past data to identify trends and draw conclusions on what has happened. HR departments rely on this type of data to understand trends in turnover rates, demographics, and employee performance metrics. 

Example: Analyze this past data to determine why employees leave the company and use that information to develop retention strategies.

Predictive analytics 

Predictive analytics uses the same data to anticipate future employee behavior or outcomes based on statistical modeling. HR departments may use this technique to forecast the impact of HR initiatives on business outcomes. 

Example: Let's say you plan to introduce a new employee recognition program. You may analyze past correlations between praise or positive feedback and increased employee engagement and predict improvements in retention and productivity. 

Prescriptive analytics

Prescriptive analytics takes it one step further by using valuable data insights to provide specific recommendations for achieving a particular outcome. HR departments can use prescriptive analytics to identify areas where you might improve. 

Example: Data analysis reveals that specific teams are not meeting their performance goals; HR can use prescriptive analytics to recommend employee training and development programs to improve their skills.

➡️ Learn more about how to extract meaningful data for your HR teams to work with our in-depth guide to people analytics. 

🕵️‍♂️ 6 Use cases of predictive people analytics in HR

If you're new to predictive analytic solutions, be inspired by these six key ways that number-crunching can support your people function. 

Candidate sourcing and filtering 

Use past hiring data to build a profile of successful hires and filter through them based on factors like skillset, education level, and experience. Then, use these typical employee profiles to inform your hiring decisions and predict future job performance. This ensures that HR teams are sourcing the right people at each stage of the recruitment process. 

➡️ Learn more about how these key decisions fit into talent management in our comprehensive guide.

Social profile analysis 

Similarly, track social conversations to identify patterns in how employees talk about their experience working for you. Analyze your company's social profile on platforms like LinkedIn or Glassdoor to understand employee sentiment and engagement. This type of analysis gives an overview of your employer value proposition. It can identify areas to improve your public image. 

Remember: Social media analysis is also integral to customer feedback, so it's worth investing in the right tools to help you analyze data points and draw meaningful insights. 

Meeting analysis 

Analyze meeting data to track attendance and participation metrics from past meetings. These insights could determine who is more likely to contribute positively in future meetings. This will help HR teams identify team dynamics and build better co-worker relationships.  

Employee monitoring 

Employee well-being is a crucial focus for HR departments, and we can use predictive tools to monitor employee mental and physical health. Track specific data points such as absenteeism, vacation uptake, work hours, and pulse survey data to build an accurate picture of employee well-being and develop initiatives to address issues. 

Employee retention and attrition risks 

HR can better understand which employees will likely leave the company and explore ways to reduce this risk. 

Example: Analyze employee engagement data to reveal patterns in how employees feel about their work environment or job roles. Do these contribute to high attrition rates? Get ahead of the resignation letter with well-timed interventions to reduce churn. 

Performance management 

Discover if your employees are consciously working towards primary KPIs by using data to track and monitor performance.

Analyzing performance ratings, feedback reviews, and project completion stats can help HR teams identify areas where employees excel or need extra support. 

➡️ Learn more about how to manage a high-performing organization in our guide. 

🏆 11 Benefits of using predictive analytics in HR

Once you've incorporated predictive analytics into these key HR processes, you'll start to reap the rewards.

Here are eleven key benefits you can expect.

Faster, accurate decisions 

Data is instrumental in helping HR teams make better decisions faster. Predictive analytics provides an unbiased view of employee data points, allowing for more informed and accurate decisions. CEO Matt Teifke explains how Teifke Real Estate relies on predictive analytics: 

"Our HR team is embracing this trend by investing in data-driven decision-making and leveraging predictive analytics and machine learning to gain a better understanding of our workforce. We are also exploring the potential use of artificial intelligence (AI) for automating certain HR tasks, such as sorting through large amounts of employee data and providing more personalized feedback."

Fewer instances of human error 

Data-driven insights enable your people teams to anticipate and prepare for challenges before they arise. This reduces the risk of costly human errors.

Improved candidate experience 

Using predictive analytics to filter through candidates ensures that HR teams only interview the most qualified individuals. They'll make speedier hiring decisions, provide feedback data to rejected applicants, and improve the overall candidate experience.

Better risk management 

Take proactive steps to mitigate health and safety risks by identifying potential hazards and taking preventive measures to minimize them.

Example: You might conduct safety training for employees or update safety protocols. Use analytics to identify high-risk individuals or groups and provide targeted interventions to prevent incidents from occurring.

Enhanced productivity 

Use project resource data or employee feedback to optimize workflows and identify areas where you might encourage productivity. Data enables teams to stay on track with projects by providing valuable insights that improve team collaboration, communication, and output, resulting in happy customers.

Stronger employee retention strategies 

Uncover patterns in employee behavior to determine which factors contribute to talent retention. From here, you'll develop strategies such as compensation transparency or career advancement opportunities to keep high-performing employees engaged and motivated and part of a more robust workplace culture.

Accurate business revenue predictions 

Forecast future business revenues by analyzing data such as labor costs and employee performance metrics. The link between these two data points can offer insight into how key business decisions positively impact the bottom line.

Proactive strategy for employee issues 

Analyze employee sentiment data to understand the underlying business issues impacting employees. By doing this, HR teams can devise proactive people strategies to address any problems with job dissatisfaction or burnout.

Strategic upskilling and hiring decisions 

Trying to decide between improving the skills of your existing talent or recruiting from outside can be a head-scratcher. Data enables you to create more strategic people policies, making it easier to decide which path will significantly impact overall productivity and profitability. 

Improved company culture 

Use data to measure company culture and decipher if employees feel safe and able to express themselves in their work environment or if your culture needs a revamp. With the right insights, you can create initiatives that foster a better sense of belonging and strong team spirit.

Consistent hybrid setup 

Creating a seamless transition between working remotely and in-office, taking employee feedback and preferences into account, can be challenging. Matthew Ramirez, CEO of Paraphrase Tool, explains, 

"Hybrid work environments pose challenges for managers as they try to create consistency across the board for employees. One trend that HR teams embrace is developing a consistent set of expectations for employees, no matter where they work. Whether employees are working from the office or from home, it is important for them to know what is expected of them, how their performance will be measured, and what their work-life balance is."

By using predictive analytics, you'll better understand existing workforce dynamics and build a hybrid setup that works for everyone. 

💼 6 Steps to implementing a successful predictive HR analytics system

HR leaders can use the following six steps to launch a successful predictive analytics system for their organization.

Define crystal-clear business objectives 

A roadmap is everything. Start by defining your team's objectives with predictive analytics—and ensure they align with the organization's overall goals. 

Example: Let's say you're looking to reduce employee churn. Establish clear goals and objectives, such as reducing turnover by 10% in three months, then determine the HR metrics you want to track to meet your goal. 

Consider data ethics 

Data ethics should be top of mind when developing predictive analytics models. It's essential to consider the privacy and security of employee data while ensuring all insights revealed through data analysis are not discriminatory. Begin by taking a data inventory to understand the information you control. As detailed in "Data-driven HR: How to use analytics and metrics to drive performance" by Kogan Page, Limited:

"You cannot properly protect data or practice good data governance if you are not entirely sure which data you have. This can be a challenge for HR teams in particular because employee-related data can be housed in all sorts of departments and systems outside of the HR team itself." 

Always work with your compliance departments and legal teams to understand the implications of storing employee data and how you process or present it to stakeholders. Be aware that the laws change significantly depending on your region or even from state to state in the US.

Develop analytics competencies

Your HR team must feel confident handling and interpreting the data to make meaningful decisions. A Gallup article explains, 

"HR has quickly gotten bogged down with too much data (often surface-level data at that) and without enough, or the right, guidance. It's hard to make data-driven decisions based on numbers if you're not sure what the numbers mean."

If your Human Resources (HR) department isn't yet equipped with the necessary skills, consider hiring a data scientist to join your ranks. 

➡️ Learn how to onboard a data scientist in your organization effectively. 

Select your priority data 

Decide which data points you need to measure your objectives effectively and where to find them. HR data typically falls into the following categories: 

• Internal: Data relating to your organization, such as employee engagement scores. This can be limited if you need to compare data with other organizations.
• External: Data from sources outside your organization—for example, salaries, job posting volumes, and industry trends. 
• Structured: Data that fits neatly into a spreadsheet, such as skills aptitude scores, performance review scores, or absenteeism rates.
• Unstructured: Data that is more challenging to categorize, such as email conversations, social media posts, videos, audio files, CCTV footage, or customer reviews. 

Consider which data type is essential in meeting your long-term business objectives, then devise a strategy to mine and analyze it. For example, some organizations use AI-driven sentiment analysis to sift through emails and social media conversations.

Decide where to store your data 

Once you've decided which data to track, it's essential to consider where best to store it. If you already use an HRIS, this might be an obvious solution. Otherwise, you'll need to find a secure repository for your data—such as a data lake or warehouse—to ensure it's accessible only to those who need it. Consider the benefits (and drawbacks) of storing your data in the cloud versus on-premises. 

Remember: Regularly organize and clean your data to ensure accuracy and consistency.

Develop a predictive model 

Work with a data scientist to choose a suitable predictive model for your organization from one of the following:   

• Classification model: This generates a simple Yes/No prediction about whether something will likely happen; for example, will they be a successful hire?
• Forecast model: Use this versatile model to predict future events, such as attrition rates or turnover.
• Clustering model: This identifies groups of similar objects or individuals, allowing you to identify potential issues and opportunities.
• Outliers model: This looks for anomalies in your data that could signify opportunities or problems. 
• Time series model: This predicts future values based on past trends. Use it to identify potential employee preferences or sentiment changes over a period.
• Decision tree: This constructs a tree-like series of yes/no questions based on data inputs. Use it to identify potential risks or probabilities. 
• Neural network: This spots patterns by connecting different types of data. Use it to personalize HR strategies or create talent pipelines.
• General linear model: This identifies relationships between multiple variables and is ideal for identifying correlations. 
• Gradient boosted model: This combines several weak models to create a stronger one. Use it to predict employee satisfaction or performance potential. 
• Prophet model: This predicts future events, particularly when there is a seasonal component, such as measuring productivity during the holiday season. 

🏢 3 On-the-ground examples of successful companies leveraging the power of predictive analytics 

Google predicts quality of hire 

Google had long tried different recruitment strategies, including focusing on educational experience or asking applicants to complete complex puzzles. But they were disappointed with the results. The candidates they hired lacked team skills and didn't have the necessary attributes to succeed in the organization. 

Instead, the search engine giant focused on hiring the best candidates in the business and keeping them there. However, their initial system was resource-intensive, so they switched to a predictive hiring process based on a Frank Schmidt and John Hunter meta-analysis of how assessments forecast performance. This data-driven approach includes using work samples, structured interviews with consistent questions, and cognitive ability tests. 

In Work Rules: Insights from Inside Google That Will Transform How You Live and Lead, Laszlo Bock explains how these tests work, 

"They are predictive because general cognitive ability includes the capacity to learn, and the combination of raw intelligence and learning ability will make most people successful in most jobs."

Google has reduced the time spent on recruitment by 75% and enjoys a higher quality of hire. 

BestBuy extracts engagement data to forecast revenue 

BestBuy, an electronics retailer, used predictive analytics to determine the correlation between engagement and store sales. Their analysis revealed that a mere 0.1% increase in engagement could lead to a remarkable $100,000 increase in revenue per store.

As a result of this significant finding, BestBuy began measuring engagement regularly and identified the factors that drive engagement. They could implement HR strategies that boost engagement and store revenue by doing so. 

Credit Suisse identifies employee attrition trends 

Credit Suisse analyzed employee churn data to identify the key reasons its employees quit. To tackle its retention problems, line managers reviewed the blind data. Also, they received specialist training on how to dissuade top performers from seeking work elsewhere.

This predictive project was hailed as a success after saving the investment bank $70 million in recruiting and onboarding costs each year. 

Learn more incredible on-the-ground people strategy examples here. 

➡️ Collect and analyze meaningful data with Zavvy

Predictive analytics relies on quality information, which begins with robust data collection methods. Zavvy offers a selection of tools to support your forward-thinking organization and create positive employee experiences. 

• Analytics: view the data that matters via our customizable dashboards. Filter key data, including compensation statistics, hire dates, attrition rates, demographic information, and manager stats.  
• Pulse surveys: Check in regularly with your employees to spot trends and sentiment and take action on opportunities or issues. 
• Performance feedback: collate and assess data on review cycles, development progress, and individual KPIs
• Onboarding: analyze how quickly your new joiners settle in and become productive. 
• Learning management: understand how engaged your learners are with training materials and course content and if they're making substantial progress toward their development goals. 

📅 Book a demo to take Zavvy for a test drive today. 

❓ FAQs

Check out some of the most frequently asked questions about predictive analytics in HR.

Are there any recommended analysis techniques you can use in predictive HR analytics?

Several predictive analytics tools and techniques are available for HR departments, such as regression analysis, machine learning algorithms, and text mining. Machine learning algorithms are handy for analyzing large datasets. At the same time, text mining can help to identify patterns in employee comments or surveys. 

Why is predictive analytics important in HR? 

Using evidence-based predictions and actionable insights, HR can identify areas where they should focus their efforts and potential issues that might arise from employee turnover or engagement levels. Additionally, HR departments can use predictive analytics to better understand their workforce, identify improvement areas, and create more rewarding employee experiences. 

What is meant by predictive analysis? 

Predictive analysis is a type of data analytics that uses historical data to predict future outcomes. Organizations can use predictive analytics to better prepare for future challenges and opportunities by comparing current and past trends. Predictive analysis can be used in various industries, from finance to healthcare, to help organizations make evidence-based decisions and improve performance. 

What are the 4 steps in predictive analytics? 

The four steps in predictive analytics are data collection and organization, model development, evaluation and validation, and deployment.

1. Data collection involves gathering relevant information, such as customer behavior or employment patterns. 
2. Model development involves creating and using a predictive model to make accurate predictions. 
3. Evaluation and validation refer to testing the model to ensure accuracy and reliability.
4. Deployment consists of putting the model into production so decision-makers may use it. 

What are HR predictive analytics metrics?

HR predictive analytics metrics are data points we can use to measure and analyze employee performance. These could include turnover rates, engagement levels, or job satisfaction, among others.