Predictive analytics for better human resource management

Predictive analytics for better human resource management

What is our employee's involuntary turnover rate? What is the revenue per employee? What is our Absenteeism rate? What is our time to hire? What is the human capital risk?

These are questions that any practicing Human Resource professional can easily answer based on the employee’s data. These and other commonly used metrics mainly focus on reporting of employee’s data. Though they may provide useful information to the department, in a data-driven economy, this may be insufficient since large chunks of data with greater insights remain unutilized in their data warehouses. Recent developments in areas of Artificial Intelligence, Machine Learning and Big data analytics have brought about various algorithms which if well applied in the Human resource departments can mine more useful insights. Through this HR Analytics has developed and is been used by organizations to effectively understand and manage their employees.

What is HR analytics?

HR analytics can be simply defined as the application of statistics, modeling, and analysis of employee-related factors to improve business outcomes. This enables the department to attract, manage and retain employees thus significantly improving the return on investment.  Predictive analytics is a branch of advanced analytics that deals with extracting information from data in order to determine patterns and forecast future outcomes and trends with an acceptable level of reliability.

Some applications in HR analytics.

Predictive analytics in HR can answer the following questions based on the current employee data.

  1. What is the probability of an employee leaving the company?
  2. What drives internal innovation?
  3. Which type of employees are at a higher risk of turnover in the future?
  4. Which new hires are likely to be a success?
  5. Which onboarding techniques have a higher retention and engagement rate

Here are some exciting examples of companies that have benefited from HR analytics.

IBM: Defining successful salespeople

Typically, an outstanding personality is considered as a key trait in defining a successful salesperson. Through a comparison of worker surveys and manager assessments, IBM found out that the most salient trait for sales success was emotional courage.

Xerox: Increasing employee retention

The company carried out an analysis of how to retain its customer service employees. It found out that employees who lived nearby and had reliable transportation tended to stick to their jobs. Through the pilot program, the company was able to reduce its attrition rate by 20%.

Royal Dutch Shell: Identifying good idea-generators

The company analyzed a database of ideas generated by its 14,000 employees over several years. The idea generators were later asked to play a video game that was designed by data scientists, neuroscientists and psychologists as a way of testing their human potential.  Shell compared the results of the video game against the real-world results of the ideas generated. This showed the characteristics of employees whose ideas would succeed in the company.

What are the common data sources for the human resource department?

  • Employee surveys
  • Telemetric Data
  • Attendance records
  • Multi-rater reviews
  • Salary and promotion history
  • Employee's work history.
  • Demographic data
  • Personality/temperament data
  • Recruitment process
  • Employee databases

Conclusion

The recent developments in data management have revolutionized the general business environment, with a lot of focus has been put into sales, marketing and finance departments. The Human resource department can equally benefit from predictive analysis. Though algorithms will not exactly show what will happen, they will provide probabilities of events occurring whether bad or good. This will allow decision-makers to make informed decisions.