7 Top People Analytics use cases
Every kind of industry in this world requires data and analytics to make better decisions for the future. If you disagree with it, check my previous article to understand how even a Baseball team could use the power of data and analytics to win the whole season.
Moreover, with data analytics, industries can take proper data-driven steps to achieve their long-term strategies.
And the reason is apparent; we live in the age of disruption. The world is being driven by data, and you can notice the enormous demand for solid support of analytics in every organization to survive and continue.
But it’s just a decade that companies have started to recognize the impact of analyzing people’s data on their success. They have noticed that they would lose their main assets shortly without investing in analyzing that data.
So People Analytics came to the world and empowered the decision-makers and HR managers to enjoy the benefits of applying effective analytics methods to the people’s data and promote a better employee experience through it.
People Analytics showed that data science is not limited to specific domains and industries, and it can even revolutionize the Human Resource department and its traditional processes.
In my three years of experience working with HR directors, I have recognized that only a small group of HR leaders know what they exactly need from analytics to prove or solve. The majority of others follow a general trend instead of tailoring to their business needs.
That’s why in this series of articles, I have collected the seven of the top People Analytics use cases which worth considering in future organizations if your HR team has not still identified the actual demand in this area:
- Turnover Analytics
2. Recruitment and Selection
3. Engagement Evaluation
4. Top Performers Analysis
5. Organizational Network Analysis
6. Workforce planning and Analytics
7. Customized training and Performance Management
Why Employee Turnover Analytics?
Employee turnover is always costly. Just imagine that your recruiter team has spent more than six months (the average duration of finding a new employee) and more than 50 thousand euro to hire an employee (consider the cost of attraction, interviewing, on-boarding, training and …)and suddenly she decides to leave the company. It gets worse when you find out losing an employee can cost 1.5–2 times the employee’s salary.
It is essential to keep in mind that turnover is a part of the business. That’s why one of the typical activities of the HR department is reporting the turnover statistics regularly. However, being prepared for it and trying to reduce the cost of the voluntary turnover is a feasible step to take.
Descriptive Turnover Analytics
Most HR departments report turnover by the percentage of people in a team, function, or segment who have left the company over a predefined period. Yet, HR performs very little analysis to discover the reasons behind it.
The reports mostly show the differences of turnover rate among various criteria of the employees, for instance, among the branches, departments or positions and based on it. The causes would be guessed and predicted. For example:
‘The employees in Berlin branch have higher attrition rate. So it might be something related to the management in that branch’.
‘The employees of the IT department left more than others in the previous quarter, so that seems they are not happy with their salary, we should increase their income.’
These scenarios are the most common ones I have noticed in organizations. HR managers believe that since they have used descriptive analytics to show the difference in turnover rate among two segments of employees, they can now predict the reason behind it by their heart. Although their proposed solution might decrease the turnover rate for a short time, it would not address the root cause. Because HR made it based on assumption and inference, not any analytics or accurate methodology based on data and evidence, this potentially leads to generalization and misdiagnosis of company obstacles instead of finding the right solution for them.
How to Make it more data-driven?
How people analytics can help managers in this area is by assisting them to discover the natural causes of the turn over instead of using assumptions.
Inferential statistics is one the most favorite methods here. By applying this kind of statistics across the data and evaluating various possible causes of turnover simultaneously, HR managers can find the real roots of the problem more accurately and independently of gut feelings.
As mentioned, deceptive analytics can summarize and explain the characteristics of the data very well. But if you want to make conclusions and inferences through that data, inferential statistics will support you better. Inferential statistics have various use cases, but one of the most important ones is testing hypotheses to draw conclusions.
The purpose of hypothesis testing is to evaluate relationships between variables using sample data and calculate how likely a pattern or relationship between variables could have arisen by chance.
Most of the significant inferential statistics come from a general family of statistical models known as the General Linear Model.
This model includes the T-test, Analysis of Variance (ANOVA), Analysis of Covariance (ANCOVA), regression analysis, in addition to multivariate methods.
By using these methods, People Analytics experts can determine causes of turnover and more importantly identify factors that can help reduce turnover costs. When HR managers have information about the real drivers of turnover, they can reduce the individual effect that each factor might play in turnover rate and translate this into a potential cost saving for the company. This information can be used to build a business case for a turnover reduction program that could make a real difference to the organization’s bottom line.
What seems essential in the current everchanging world of business is proactivity. Data-driven decisions could help HR and managers to foresee the problems and conquer them. Organizations need to pursue their goals without interruption, and the side effects of employee turnover could be an obstacle on their path. So they need a solid and reliable solution to persuade employees to remain with them as long as possible or find the causes for their turn over to improve the situation.
Data analytics seems to be the most proper solution assisting managers in building their strategies based on that.
In the future articles, I will go through the next common People Analytics use cases and describe which kind of algorithms and analytics will help more to define them.