There is immense potential in uncovering patterns and relationships between key pieces of information about people and teams. Predictive analytics allows businesses to tap into this hidden value, using existing data to drive better decision-making and long-term success. By identifying trends and predicting future outcomes, HR can move from reactive to proactive strategies. In this blog, we’ll briefly understand what is predictive HR analytics, what are its applications, and how can you implement it in your organization.
What is predictive analytics?
Predictive analytics is a method of forecasting future events by analyzing patterns in past data. Whether it’s used by businesses to predict customer behavior, doctors to anticipate health risks, or individuals to plan personal investments, it helps make smarter decisions by offering a glimpse into what’s likely to happen next.
In more sophisticated uses of predictive analytics, large repositories of historical data are analyzed by applying statistical techniques and machine learning algorithms. This approach allows for more accurate predictions on a larger scale. Examples of predictive analytics in business include customer churn prediction, sales forecasting, fraud detection, supply chain optimization, etc.
Statistical techniques in predictive analytics
Statistical techniques are the methods applied to historical data to identify patterns, relationships, and trends. These techniques use mathematics, probabilities, and algorithms to make sense of large datasets. Here are some of the techniques used in predictive analytics:
1. Regression Analysis:
A technique to predict a dependent variable (like sales) based on one or more independent variables (like price or marketing spend).
2. Time Series Analysis:
Used for forecasting future values based on historical data over time, such as sales trends. It’s especially useful for identifying seasonal patterns and long-term trends.
3. Decision Trees:
This technique segments data into branches to show possible outcomes based on different decisions or variables (e.g., yes/no, true/false).
4. Cluster Analysis:
It groups data points with similar characteristics together to understand group behavior.
5. Principal Component Analysis (PCA):
It is a method used to simplify complex data by reducing the number of variables. It transforms the original variables into a new set called principal components, which capture the most significant information.
Predictive Analytics in Human Resources
Predictive analytics in HR is the application of predictive analytics specifically in the field of Human Resources. In other words, it involves taking the scientific techniques used in predictive analytics and applying them to people-related data within an organization. By analyzing employee data, HR can predict future trends, such as turnover, hiring needs, or performance. This enables HR teams to make proactive decisions, such as identifying employees at risk of leaving or improving recruitment strategies, ultimately moving from reactive to strategic workforce management.
Applying predictive statistical models to HR-related data requires a solid understanding of statistics and the ability to interpret the results meaningfully. Currently, only a small percentage of HR functions utilize the advanced statistical techniques available to them. While HR analytics teams often handle and report on large volumes of people-related data, few take the next step to apply statistical methods that allow for predictive insights.
Use Cases of Predictive Analytics in Human Resource Management
Predictive analytics has numerous use cases in Human Resource Management. Here are the examples:
1. Employee Turnover Prediction:
Identifying employees likely to leave the organization and implementing retention strategies.
2. Performance Management:
Predicting employee performance trends to tailor development programs and address potential gaps.
3. Talent Development:
Identifying skill gaps and recommending training programs to enhance employee capabilities to prepare for the future.
4. Succession Planning:
Identifying high-potential employees for future leadership roles.
5. Workforce Planning:
Forecasting future workforce requirements and allocating the workforce accordingly.
6. Compensation Analysis:
Analyzing industry compensation data to predict the effectiveness of compensation programs and being more competitive in attracting talent.
7. Employee Engagement:
Analyze patterns in engagement data to predict and improve employee satisfaction and productivity.
8. Diversity and Inclusion:
Identifying and mitigating bias in the hiring process to maintain diversity.
9. Absenteeism Management:
Predicting patterns of absenteeism and developing strategies to reduce unscheduled time off.
Why do businesses need predictive analytics in HR?
The HR function spends considerable time and resources in producing and comparing descriptive reports. While such reports are useful in explaining the present state of human capital, they possess a very limited capability of explaining why a situation exists in the business. Conducting exploratory research is very difficult with such reports. At the same time, they do not help us in predicting what might happen in the future. By identifying trends and patterns, predictive analytics can tell businesses what they do not know.
An understanding of trends enables HR professionals to make better strategic decisions to deal with future workforce challenges. For instance, if you can identify the predictors of high performance, better productivity, longer employee retention, etc, then you can identify the correct strategic activities to lever important employee outcomes.
How do you implement Predictive HR analytics in your organization?
The implementation of HR analytics has 4 major requirements:
1. Data:
Predictive HR analytics relies completely on current and historical data. It is important that the data is sufficient and accurate because you cannot find true patterns when the data is limited and sketchy. In modern times, HR professionals aren’t struggling with a lack of data. Instead, they’re often overwhelmed by the sheer volume of information available, making it difficult to know how best to utilize it. Some examples of useful HR data include:
2. The Knowledge of Statistics:
Statistical models are key to identifying patterns and trends in HR data, and using these models effectively requires a solid understanding of statistics. Without this knowledge, HR professionals would struggle to apply techniques like regression or probability analysis, which are essential for making accurate predictions about employee behavior, turnover, or recruitment success. While it’s common for HR professionals not to have a deep knowledge of statistics, they can easily acquire these skills through training.
3. Tools and Software:
A statistical tool or software will simplify the process of building predictive models by offering user-friendly interfaces, pre-built algorithms, and visualization features. Without such software, HR professionals would struggle to manually apply statistical methods. Some examples of statistical software include SPSS, Minitab, Stata, SAS, R, JASP, etc.
What’s Next?
As you plan to implement predictive analytics in your organization, start by defining your objectives. What is it that you want to predict? Next, you need to identify your data sources. Your data source can be the organization’s HR database, employee surveys, customer satisfaction surveys, sales performance data, or operational performance data. Finally, you need to run your analysis using the appropriate statistical tools to come up with predictions to aid your organization’s strategic initiatives.