Predictive analytics are a rapidly upcoming trend in Human Resources (HR). Even though a lot of people talk about predictive analytics in HR, hardly any organizations apply them to their workforce. In this blog I will explain what HR predictive analytics are and how they can be a real game changer for HR departments.
I will also discuss 7 real life examples of predictive analytics in HR, two of which are detailed case studies.
The logic behind HR predictive analytics
Do you know what your personal credit score, the Oakland Athletics baseball team manager Billy Bean from the movie Moneyball and your Match.com profile have in common? They all combine big data and predictive analytics in order to predict the (near) future.
Predictive data analytics are everywhere. It is in its essence a technology that learns from existing data and it uses this to forecast individual behavior. This means that predictions are very specific. In the movie Moneyball, predictive analytics were used to predict the potential success of individual baseball players. In a similar way, your personal credit card score uses historic data from millions of people in the past to predict whether or not you can pay back the loan you want to take out for your new car.
Predictive analytics involve a set of various statistical (data mining) techniques used to predict uncertain outcomes.
An example with kids
Say there is a playground next to your house. For the past two weeks, you wrote down if there were kids playing on the playground or not. You also wrote down if it was sunny, rainy or cloudy, the temperature and the humidity. Based on the data you collected, would you be able to predict if kids will be playing on the playground on a specific day?
Your Weather spreadsheet
This is a tricky question. Obviously, these weather conditions have something to do with whether kids are playing outside or not. If the weather forecast is rainy, it will probably rain, meaning that kids are less likely to play outside. When it is hot, kids will probably play outside. But does your spreadsheet with information of fourteen consecutive days hold sufficient data to make an accurate prediction on whether or not kids will play outside?
This data may seem rather insignificant compared to the large amount of HR data available at your company. It is, however, a good example. Let us find out what we can do with these 14 days of data.
The decision tree
A common and rather simple method of creating a predictive model is the decision tree. A decision tree is a tree-like model consisting of decisions and their possible consequences. In the decision tree, every node represents a test on a specific attribute and each branch represents the possible outcomes of this test.
I made a decision tree on our weather data set by applying some simple data mining techniques. The decision tree was computed using a specific decision tree algorithm, called C4.5. This decision tree model fits the data well: it is able to predict whether kids will play on the playground with a 71% accuracy. This is much better than guessing, which has a 50% accuracy.
The decision tree is practically self-explanatory if you take a close look at it.
There are two strong predictors in the decision tree. Outlook is the first predictor. Kids will play on the playground 4 out of 5 times when the weather outlook is sunny. When the forecast is rainy, the kids do not play outside. In case the outlook is cloudy, humidity is the second predictor. Kids are not likely to play outside if humidity is high (which it usually is when it rains). However, when humidity is normal kids are likely to play outside.
In other words: the weather forecast and humidity can be used to rather accurately predict whether kids will play on the playground outside.
Even though this simple example might seem very logical, it does show how predictive analytics work. Algorithms that learn from existing data are used to make specific predictions about the (near) future. Eric Siegel (2013) compares this to a salesperson. Positive and negative interactions teach a salesperson which techniques work and which do not. In a similar way, predictive analytics is a process that enables organizations to learn from previous experiences (data).
How HR predictive analytics apply in practice
Now, how do predictive analytics apply to HR? As I wrote in a previous blog, HR possesses a massive amount of people data. By applying predictive analysis to this data, HR is able to become a strategic partner that relies on proven and data-driven predictive models, instead of relying on gut feeling and soft science. HR predictive analytics enable HR to forecast the impact of people policies on the well-being, happiness and bottom line performance of employees.
However, only few organizations are capable of producing predictive models for HR. According to Deloitte’s Global Human Capital Trends (2016) report, only 8% of the organizations worldwide had this capability in 2015. This number doubled compared to the year before.
This rapid growth can also be seen in the number of companies who consider people analytics as an important trend. Figure 1, taken from the 2015 Deloitte Global Trends report, shows the perceived importance of these analytics.
A lot of organizations still have a long road ahead of them before they can produce predictive people analyses. Despite this, early adopters already show some very interesting results. Let’s take a closer look at some of them.
Real life examples of predictive analytics in HR
In his book Work Rules! (2015), Laszlo Bock, Senior Vice President of People Operations (HRM) at Google, writes that the most important instrument of Google’s People Operations is statistics. The questions interviewees get asked in Google’s hiring process are all fully automated, computer-generated and fine-tuned in order to find the best candidate.
On top of that, Google estimates the probability of people leaving the company by applying HR predictive analysis. One of Google’s findings is that new salespeople, who do not get a promotion within four years, are much more likely leave the company.
2. Facebook pages
Do your recruiters check the Facebook pages of applicants? Maybe they should. A 2012 study revealed that it is possible to predict someone’s personality and future work performance based on their Facebook profile (Kluemper, Rosen & Mossholder, 2012). In this study, a number of participants gave hirability ratings based on Facebook profiles. These ratings predicted 8% of manager-rated job performance for these people.
8% is not that much. For instance, a standard personality test has a higher predictive value for performance compared to looking at someone’s Facebook profile. However, the literature shows time and time again that the best predictive models for future job performance combine various predictors, such as IQ tests, structured interviews, and personality tests together. Looking through a Facebook profile could be an additional instrument to scan candidates.
3. US Special Forces
During the highly selective training, the U.S. Special Forces predict which candidates are most likely to succeed. Two key predictors are ‘grit’ and the ability to do more than 80 pushups. Grit was actually a more accurate predictor of training success than IQ. Check Angela Lee Duckworth’s Ted Talk if you’re interested to know more.
Wikipedia editors, or Wikipedians, create and edit articles to keep the world’s largest encyclopedia up-to-date. Each day, over 800 new pages are created and 3,000 edits are made on the English Wikipedia alone. Wikipedia is able to predict who of its 750,000 editors is most likely to stop contributing. I am not sure how Wikipedia acts on this information, but I think a ‘thank you for your contributions so far’-email could do wonders in appreciating and re-engaging these Wikipedians.
5. Best Buy
Best Buy (a leader in HR predictive analytics) can accurately predict how employee engagement impacts the performance of their stores. A 0.1% increase in employee engagement results in an increase of over $100,000 in the store’s annual income. The enormous impact of engagement prompted Best Buy to make its engagement surveys quarterly instead of annually. Measuring the impact of employee engagement on bottom line performance is difficult to do but certainly possible, as this example shows.
Retaining employees at Hewlett-Packard
Hewlett-Packard (HP) is a company with over 300,000 employees and has always been a leader in the HR predictive analytics field. HP’s management experienced a high level of employee turnover. Indeed, turnover rates of 20% were not uncommon in some sales divisions. This means that employees would leave on average within 5 years of joining!
High turnover generally leads to high recruitment costs and lost revenue due to productivity loss and onboarding. Additionally, leaving employees take their knowledge and network with them, and sometimes even customers. It is estimated that the cost of replacing mid-level employees is upwards of 150% of their annual salary. This can cost a company millions of dollars.
In 2011, two scientists at HP combined data of the previous two years and attempted to predict who would leave the organization. By using predictive models, they generated what they called a “Flight Risk” score. This score predicted the likelihood of leaving of each of HP’s 300,000+ employees.
Their findings were groundbreaking. Based on the data they could see why employees would leave HP. Higher pay, promotions and better performance ratings where, for instance, negatively related to flight risk. However, there turned out to be intricate relationships between those findings. For instance, when someone received a promotion but did not get a substantial raise, this person would still be much more likely to quit.
As you can imagine, there were a number of practical and privacy-related problems with this Flight Risk score. The worst possible thing a manager could say to an employee during his/her annual performance review is: “I see you are likely to quit. Why?”
This is why access to this data is only granted to a select group of high-level managers. These managers could only see the scores of the employees under them. They also received training in interpreting Flight Risk scores so they would understand the potential ramifications and confidentiality issues that come with this data.
Additionally, the system informs these managers what the key risk factors of employee attrition are. This way, the system exerts pressure on managers to develop strategies to retain their staff. This helps to reduce costs and maintain business continuity.
In the end, Flight Risk scores help managers make better-informed decisions. The scores act as an early warning signal and prompt managers to intervene before it is too late. Or, when the loss of an employee is unavoidable, to react accordingly. According to Siegel (2013), HP was able to save an estimated $300 million by applying HR predictive analysis in HR to calculate this flight risk.
Never hire toxic people
The final example of HR predictive data analytics I came across, is a case-study published by Cornerstone (2015). Cornerstone studied the impact of toxic employees on the workplace. Toxic employees are employees who are most likely to engage in toxic behavior. Examples of these behaviors are fraud, drugs or alcohol abuse, and sexual harassment.
These people are not only damaging to the company; they are highly toxic to the general work environment. Previous research suggested that one toxic employee in a team would cause productivity to decrease by 30% to 40%. On top of that, good employees are more likely to quit when they have to work together with toxic colleagues.
Cornerstone used a dataset of 63,000 employees. In this dataset, they marked which employees were involuntarily terminated due to workplace violence, falsification of documents, drugs, and alcohol abuse, and other policy violations. Based on these criteria, around 4% of all employees could be classified as being ‘toxic’.
After analyzing the dataset, Cornerstone identified a number of key characteristics of toxic people.
1. are self-proclaimed rule-followers;
2. score low on attendance and dependability;
3. and have a low service orientation.
Remarkably, the study did not find the previously reported high levels of productivity loss in the short term. However, it did find toxic behavior to be contagious. People who work together with toxic colleagues are also more likely to quit. Additionally, the study hypothesized that toxic colleagues contribute to long-term stress and burnout among other employees.
In the end, Cornerstone proved that hiring a toxic employee will cost the employer $12,800 on average, versus an average of $4,000 for a non-toxic employee. This excludes the long-term (and costly) productivity loss through burnout and other negative effects. By fine-tuning the hiring process, companies can prevent hiring candidates who are likely to become toxic and create a healthier working environment.
A game changer for HR
As these previous examples show, the results of applying predictive people analytics can be astonishing. HR departments can potentially save (or earn) their company millions of dollars. Additionally, HR can help their managers and executives make better decisions by applying the predictive analytics and <href=”https://www.analyticsinhr.com/blog/14-hr-metrics-examples/”>using smart HR metrics.
The potential of predictive HR analytics demonstrated by these business cases makes it clear that predictive HR analytics are here to stay. They are the game changer that enables HR to not only assess how employees work, but also to predict and optimize the impact of people policies on both the employees and the business.
To learn more about the application of predictive analytics and HR analytics in general, check out these 5 online HR analytics courses, or check out our book on the Basic Principle of HR analytics!
Cornerstone (2015). Toxic Employees in the Workplace.
Eric Siegel (2013). Predictive Analytics.
Laszlo Bock (2015). Work Rules!: Insights from Inside Google That Will Transform How You Live and Lead.
Data mining with Weka. To learn more about taking raw data and transforming it into something useful check out the ‘Data mining with Weka’ course. In this free course you will also find an alternative version of the weather spreadsheet. The course teaches you the basics of data mining, including how to make a decision tree!