Predictive Analytics in HR Examples (Complete Guide)
Do you want to learn how to implement HR predictive analytics in your organization? This guide will show you how to get started.
Predictive analytics in HR is a set of techniques for analyzing data to predict future events in an organization. These predictions are then used to improve decision-making processes.
This article provides a step-by-step guide on implementing HR predictive analytics framework and using HR metrics in your organization.
How Does Predictive Analytics in HR Work?
You first need to understand the difference between descriptive statistics and HR predictive analysis. Descriptive statistics describe past events, while predictive analytics use the same methods to predict future events.
Descriptive Analytics
Descriptive analytics is used to understand what has happened in the past. For example, if we looked at employee turnover rates over time, we would use descriptive statistics and HR metrics to see which employees have left or been fired each year.
Predictive Analytics in HR
Predictive analytics are used to predict what will happen in the future. Suppose we wanted to indicate whether an employee would likely leave their job within 12 months. In that case, we could use predictive statistics to determine whether they had previously left their position within the last 12 months.
So, how can you integrate the two in your HR work? The answer lies in using both descriptive and predictive analytics together. First off, predictive analytics involves collecting historical data from multiple sources. For it to be helpful, you must assemble any HR data needs correctly and analyze it thoroughly.
Once the data has been collected, clean it up and transform it into an easy-to-understand format. Finally, analyze the historical data using descriptive statistics to create a model. Once data analytics is complete, you decide based on the predicted outcomes.
On the other hand, descriptive analytics is all about understanding what has already occurred. It doesn’t matter if the event has already taken place; what matters is that the information is available. The data collection process is much more straightforward than predictive analytics because no prediction is involved. Instead, the focus is purely on gathering as much information as possible.
Therefore, predictive analytics work is more important in your HR work since it allows you to take action before it occurs. However, descriptive analytics is still essential because you won’t make informed decisions without it.
Why is Predictive Analytics Good for?
There are many reasons why organizations should consider applying a predictive analytics framework to their business strategy. Here are just some of them:
1. Improved Decision Making
Data-driven decisions are more accurate than those made based on intuition alone. Using predictive HR analytics to make better decisions allows businesses to save money, increase productivity, and improve customer satisfaction.
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2. Reduced Risk
Risk reduction is one of any business’s primary goals. Data-driven decisions reduce risk by allowing companies to identify potential problems before they occur.
3. Increased Productivity
When decision-making becomes more efficient, productivity increases, and when people aren’t figuring out what to do, they can spend more time doing the tasks.
4. Fewer Disasters
Disaster prevention is another reason why the predictive analytics framework is so valuable. They help prevent disasters by identifying risks early enough to avoid them altogether.
5. Better Decisions
Decision-making is a complex task. Using predictive analytics in business intelligence helps ensure the best decision is made.
Real-life Examples of Predictive Analytics in HR
The following predictive analysis HR examples show how companies have utilized predictive analytics in human resources:
Example 1: HP Predicting and Preventing Employee Turnover
In 2008, Hewlett-Packard discovered that its employees were leaving the company at an alarming rate. At first, the company thought the problem was due to poor management or employee dissatisfaction. But after conducting research, HP realized neither of these factors didn’t cause the pain. Instead, the issue was that HP had no way of predicting when employees would leave the company.
To solve this problem, HP developed a predictive analytics program called “Project Insight. ” Project Insight uses a combination of statistical modeling and text mining techniques to predict future outcomes, including determining which employees will quit within the next six months. HP then sends emails to warn employees who are most likely to leave based on this data analysis.
This program has helped HP significantly decrease employee turnover rates. For example, between 2009 and 2011, the number of employees quitting per month decreased from 20% to less than 10%.
Example 2: Google’s Prediction Engine To Predict Future Outcomes
Google is known for hiring top talent. The search engine giant employs thousands of new employees yearly, but only about 30% of them stay with the company for over two years. This means that Google loses millions of dollars yearly because it doesn’t know which candidates will succeed until it is too late.
To address this problem, Google created a predictive analytics program called Google Prediction Engine. It analyzes job history, education, skills, and personality traits to determine how successful the applicants will become at Google. This information is vital to other HR leaders as well.
Based on this information, the system predicts whether an applicant will become a great employee. If the forecast is favorable, Google then offers the candidate a position. However, if the prediction is pessimistic, Google does not provide the person with a job.
This program has been very effective at reducing the number of unsuccessful applications. In fact, since 2007, Google has hired over 100,000 new employees using the Google Prediction Engine.
Example 3: BestBuy Predicting Business Outcome Using Engagement Numbers
Best Buy is one of America’s largest electronics retailers. The company used a predictive analytics framework to identify customers who were unsatisfied with their purchases, improve customer service, and enhance business outcomes.
Using collected or existing data from online surveys, Best Buy determined that customers who purchased items like TVs, DVD players, and computers were likelier to complain about delivery delays, product defects, and unsatisfactory customer service.
Based on this knowledge, Best Buy sent personalized emails to customers who bought these products. These emails contained links to online videos explaining how to resolve problems. Additionally, Best Buy offered free shipping on all orders placed through its website.
The results have been impressive. Since implementing this strategy in 2010, Best Buy has seen a significant increase in customer satisfaction ratings and enhanced its business outcomes. According to a report published by Customer Experience Management Magazine, Best Buy increased its average order value by $50 million.
Example 4: Amazon’s Recommendation System
Amazon is another popular e-commerce retailer. Like Best Buy, Amazon also uses predictive analytics to help customers make better online shopping decisions.
For instance, Amazon can tell other people’s thoughts about a particular item. Amazon can determine which products are similar to yours and recommend those products to you by analyzing historical purchase data.
If you buy something based on a recommendation, Amazon earns money. For example, Amazon makes money when you buy a Kindle eBook reader after seeing someone else purchase one.
In addition to recommending products to customers, Amazon also recommends books and movies to users. Based on your viewing habits, Amazon determines which books or movies you might enjoy.
Amazon’s recommendations are so good that many people say they’re getting personal advice from the site.
Example 5: Wikipedia Editor Contribution
Wikipedia is a global encyclopedia containing thousands of articles written by volunteers. Anyone can contribute to the site, which aims to create a comprehensive resource for anyone interested in learning about any topic.
However, some topics help us know what others think before writing an article. For example, if you write an essay about the Battle of Waterloo, you should check if other people have covered the subject.
To prevent duplicate content, Wikipedia uses “collaborative filtering.” This method collects information about every user who has ever contributed to the site. The algorithm then looks at each new contributor’s past contributions and matches them up with existing contributors.
This matching process helps Wikipedia determine whether two users will work well together. If the algorithm finds a match, it suggests that both users write an article about the same topic.
Collaborative filtering works because people tend to share common interests. So, suppose you’ve read several articles about the Battle of Waterloo and haven’t found anything interesting, according to the predictive model.
In that case, chances are you won’t find much helpful information on Wikipedia either.
Example 6: Using Facebook To Make Better Hiring Decisions
Facebook is a social networking website that allows members to connect with friends, family, and co-workers. It was founded in 2004 by Mark Zuckerberg and his college roommate, Eduardo Saverin. Today, Facebook boasts more than 1 billion active monthly users worldwide. That’s roughly half of all Internet users. That is why HR departments are using it for hiring purposes.
The most popular use of Facebook is connecting with friends and family. However, businesses can also use the site to promote their brand.
For example, companies can post pictures of themselves working hard while promoting their business. They can even ask potential employees to send in resumes. Businesses can also use Facebook as a recruitment tool. They can advertise job openings and accept applications through the site. By posting jobs on Facebook, companies can reach many qualified candidates. And since everyone on Facebook knows one another, this type of advertising is highly effective.
Example 7: Predicting impact At Nielsen
Nielsen is a company that measures television ratings, including how often shows are watched and how long viewers watch programs. Advertisers use it to decide whether to buy airtime during a particular show. Nielsen is so important to advertisers that they pay $1 per viewer. Nielsen collects predictive HR data using TV sets connected to its servers.
These TVs collect information such as the channel being viewed, when the program starts, and how long viewers watch the program.
In addition, Nielsen tracks how often a person watches a specific show or movie online. Using these statistics, Nielsen can use a predictive model to tell which shows will become successful. The company then shares this information with media outlets and advertisers.
Advertisers use Nielsen’s predictions to decide on buying time for commercials during specific programs.
Conclusion
The predictive analytics framework helps HR professionals make better and wiser decisions. This technique helps make effective decisions, reduces risks and challenges, and enhances productivity. If you want to start this strategic process, this article with predictive analytics HR examples is perfect for leveling up your knowledge.
FAQs about Predictive Analytics in HR
Here are the most frequently asked questions about predictive analytics in HR.
What is predictive HR analytics, and how does it help organizations?
Predictive HR analytics refers to the use of data, statistical methods, and machine learning to anticipate future trends and outcomes in human resources. By leveraging historical HR data, companies can forecast employee performance, retention risks, and workforce planning needs. This proactive approach empowers organizations to make data-driven decisions that enhance efficiency and employee satisfaction.
How does predictive analytics improve employee performance?
Predictive analytics helps HR professionals identify factors that influence employee performance. Organizations can predict which strategies will improve productivity by analyzing historical HR data, such as past performance reviews, training records, and project outcomes. This allows businesses to tailor training programs, identify high-potential employees, and ensure the right individuals are in roles that maximize their strengths.
Can predictive analytics enhance employee engagement?
Yes, predictive analytics can significantly boost employee engagement. By analyzing data on employee behavior, feedback, and engagement survey results, HR teams can identify trends that lead to disengagement. For example, predictive models might pinpoint which teams are at risk of burnout or which employees are likely to feel undervalued. This insight enables organizations to implement targeted interventions to improve morale and satisfaction.
What role does historical HR data play in predictive analytics?
Historical HR data serves as the foundation for predictive analytics. By analyzing past trends—such as turnover rates, absenteeism, and recruitment success—HR teams can identify patterns and predict future outcomes. This data helps organizations make strategic decisions, such as forecasting hiring needs or understanding the root causes of employee turnover.
Why should companies invest in predictive HR analytics?
Companies should invest in predictive HR analytics to stay ahead in a competitive job market. This technology enables organizations to anticipate challenges, optimize workforce planning, and improve employee engagement and performance. By leveraging data-driven insights, businesses can reduce costs associated with turnover and training while building a more resilient and satisfied workforce.
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