Predictive Analytics Are the Future of HR
A Guide to HR Predictive Analytics
In a digital world, it is hard to find an industry that doesn’t use predictive analytics in some capacity. From government, marketing, science, finance, and even sports – it is obvious that utilizing predictive analytics is a necessity for every modern business today to optimize operations and remain competitive in a data-driven era.
In this article, we will give an overview of how predictive analytics is transforming the HR and recruitment industry and explain some of the many advantages that can be gained by their implementation.
What Are Predictive Analytics in HR?
Most people are familiar with the basic concepts of descriptive statistics, which are metrics and indexes used to summarize datasets. The main measures in descriptive statistics are those concerning central tendency (averages) and dispersion (variance and standard errors). Although descriptive statistics can convey useful information about a particular sample of observations, these values alone do little to help us plan ahead.
Descriptive statistics often focus on retrospective analysis. But how can we convert data into future insights? That is where predictive analytics come into play.
Predictive analytics is essentially the application of inferential statistics with the assistance of computer software. It can be used to make projections about future events via statistical probabilities and modeling.
Key techniques in HR predictive analytics are:
Decision tree analysis – a schematic diagram that branches out for every possible outcome resulting from each decision. It is used to help determine a course of action in order to obtain a specific outcome and the probability of this outcome occurring.
Regression modeling – used to explore the relationship between two or more variables and make estimates or predictions of one variable based on the others.
Neural networking – a series of algorithms that maps the underlying relationships within a dataset and processes them in such a way that mimics the human brain.
Using methods of predictive analytics on historical data, we can determine trends, identify patterns, and build statistical models that enable us to forecast outcomes and behaviors before they happen. Such forecasting techniques provide probability-based insights that help us prepare for eventualities and assess the likelihood of their occurrences. It is an invaluable tool that can aid us in making smarter, more informed decisions.
Type of Predictive Analytics in HR
The prevalence of predictive analytics in human resources has grown exponentially in recent times due to its increase in accessibility and proven results of effectiveness. All aspects of human resource operations within an organization can be drastically improved with the application of predictive analytics. Here are some of the ways:
A high turnover rate can be extremely costly for any organization. Predictive analytics can examine data on variables known to have a negative effect on turnover, such as commute time, performance issues, and role changes. Pinpointing where the problems lie can help you to understand which areas need improvement to reduce resignations, terminations, and panic hiring.
Analysis of exit data can predict if employees will continue to leave a company if specific aspects of your company and its operations are left unchanged. Altering elements which influenced employees to leave can reduce costs and disruptions resulted from attrition.
HR predictive analytics can give insight into which types of employees are at a high risk of leaving a company. Armed with this knowledge, HR can formulate their retention strategies to focus on these employees with the goal of increasing their job satisfaction and happiness, which, in turn, will lead to a higher chance of them staying.
In addition, HR predictive analytics on data collected from onboarding surveys can predict an employee’s future success within the company. Research suggests that experiences in the first thirty to ninety days of a new employee are integral to their job performance and perceptions of the company itself.
A poor onboarding experience is likely to set a new hire up to fail. So it is important to identify if there are any issues pertaining to the availability of resources and support. It can also indicate inadequate initial training, and perhaps a more intensive or longer training period is required to prepare those new to the role. Addressing these issues as quickly as possible can make all the difference in driving down future attrition.
The use of predictive analytics on data regarding previous job postings and other sourcing operations can help to determine where the best candidates can be found. Narrowing this down will help reduce the time it takes to find a quality hire for similar open positions in the future. Thus, your hiring strategy will be more streamlined and cost-effective.
The abundance of information available about the labor market can also be used to build predictive models that can forecast where candidates with a particular skill are most likely to be acquired.
This will give organizations the ability to target their search. For example, the data may indicate that a strong workforce specializing in software development can be located overseas. An organization looking for software developers can then use this information obtained by predictive analytics to expand their recruitment overseas.
The very building blocks of candidate screening strategies are based upon HR predictive analytics. We can compare the skills and traits of candidates to those of current and previous successful employees or to a well-defined criterion of desired characteristics. Based on profiling similarities, you can:
Deduce which candidates have the most potential to also be successful should they join your workforce.
Determine the probability that new hires have the right values. Even the most qualified and competent employee will become unmotivated in an environment with conflicting values to theirs. This will likely result in social or professional frictions and ultimately end in a resignation or termination.
Predict and assess candidate role suitability and fit in terms of company culture. The algorithms which analyze and score the multivariate data psychometric tests generate can also facilitate in weeding out candidates more accurately than traditional and manual forms of assessment; it is much harder to fool or over-prepare for these tests than an in-person or phone interview.
Eliminate candidates with undesirable personalities who appear to exhibit behaviors that are not conducive to a productive and healthy work environment. Studies show that people who engage in toxic behaviors, such as fraud, sexual harassment, or drugs, are not only damaging to the company but can decrease productivity by up to forty percent. Moreover, valuable employees may have a higher probability of resigning if working in proximity with a toxic employee. By identifying candidates who are likely to become toxic employees, we can prevent the loss of productivity, reduce turnover, and eradicate associated costs they would potentially cause.
An added bonus to automated screening is the elimination of bias that may occur. Computers do not discriminate either consciously or subconsciously, unlike their human counterparts.
Human judgment is subjected to bias and error that cannot be measured, whereas the bias and error of HR predictive analytics can be calculated with precision and minimized to a negligible value or reduced to zero. Easier quantifying and tracking of results combined with machine learning also provides the ability to learn from and improve decisions made via predictive models over time.
This impartiality will bolster cultural diversity within the company by ensuring equal consideration is given to all candidates; new hires are selected solely on merit. It will also prevent from turning away any candidate that is right for the role for the wrong reasons.
A key question that often plagues many HR departments: Should we invest in upskilling existing employees or finding new hires? The use of predictive analytics in HR can answer this question. By taking factors into account that directly correlate to the availability of specific skills in the labor market, it can be determined whether or not a skill gap exists. If there is a scarcity of individuals with these specified skills, it may be a better option to look at upskilling your existing staff.
Predictive analytics can once again be used to identify employees that would benefit most from upskilling and predict their ability to successfully upskill after completion of a training course. We can even evaluate the effectiveness of the training courses themselves.
Equipped with data on your employees about factors such as preferences, existing skills, education, and aptitudes, we can also ascertain the potential an individual has in another position. This can inform decisions about promoting from within, which can often be preferable to an external hire. Lowering recruitment costs and optimizing utilization of existing talent is one of the most transformative uses of predictive analytics in HR for a company.
If absenteeism rises above the benchmark, it can have a chain reaction of negative effects on every business operation within a company. If a person does not show up to do their job, all tasks they are responsible for will be delayed. A replacement employee will need to found to fill in. All of this results in costs to the company both directly and indirectly.
It is always in a company’s best interest to investigate why absenteeism is happening. Implementing predictive analytics in HR can help uncover the underlying reasons behind the cause and deploy necessary preventions.
Keeping tabs on daily performance metrics can help to prevent slips before they become problematic for the entire company. A thorough analysis can aid in the development of fluctuation models that can give inferences in relation to staffing levels.
Using HR predictive analytics, we can also build regression models that can delve into the relationships between a response variable (output value) and explanatory variables (input values). A response variable of significant interest is often productivity. HR departments are frequently concerned about what has direct effects on the productivity of business outcomes and performances.
An explanatory variable of interest may be employee performance. With a regression model, we can quantify the effects of employee performance on productivity. It can also give insight into what could an increase or decrease in employee performance have on productivity. This can allow for optimizations and benchmarks to be set to maximize productivity. It can also determine a minimum value for employee performance to achieve an established level of productivity.
Commonly considered the holy grail of HR, a high employee engagement is positively correlated with better overall work performance. An engaged employee is also statistically less likely to quit implying that employee engagement is an influencing factor in turnover and retention rates.
Predictive analytics can provide a deeper look into what motivates your employees and keeps them loyal.
Work performance can also directly impact company revenue, and good work performance can increase profits. Due to this established relationship, we can predict revenue by measuring engagement.
How Can HR Impact a Company’s Bottom Line With Predictive Analytics?
The examples detailed above are just a preview of how utilizing predictive analytics can improve HR policies and processes. With countless other beneficial possibilities still to be explored, it is no wonder the implementation of predictive modeling has become a trending topic of discussion amongst HR professionals. The reality is that data-led insights give us a deeper and more precise understanding of what is working and what isn’t.
Predictive analytics in Human Resources truly is a game-changer in forming and recalibrating human resource strategies. Predictive analytics yield far more accurate predictions and produce better results than traditional methods based primarily on human intuition.
Technological advancements in the field of data science have radicalized the ways in which we can utilize data. Thanks to the development of user-friendly predictive analytics software, crunching the numbers has never been easier. Having the ability to foresee the future is now just a click away!
By harnessing the power of predictive analytics in HR, we can:
Make data-driven decisions to save on significant potential loss of revenue
Identify problems that are likely to occur ahead of time and implement preventions
Align competencies of your workforce to business objectives and drive performances
Determine the best course of action for a desired future business outcome
Streamline the efficiency of hiring processes
Optimize the impact of policies on employees
Replicate desired results for future growth
The scope of HR strategies has evolved, and it is time to evolve with it.
Bring your organization into the 21st Century and capitalize on innovative ways to be prepared for uncertainty. Take advantage of the latest technology in predictive analytics by adding this must-have tool to your HR arsenal. Predictive analytics is the future of HR, don’t be left in the past! Contact Lanteria today for more information; we predict that we’ll hear from you soon!