Reducing Employee Turnover with Predictive HRMS Software

Nolan Gray Blog


Is your company finding it challenging to find and then keep good people? If you had a way of looking deeper into the reasons why candidates often struggle to get past the first few months on a new job, wouldn’t your HR department take action? It’s been said over and over again that “people don’t leave jobs, they leave management.” This is certainly true when it comes to the overall new hire experience that too few supervisors know how to manage well. If a candidate does not go through the process smoothly, due to lack of tracking, this can turn into a negative experience fast.

Why Employees Leave

why employees leave

In a Gallup Poll, 17 percent of employees leave their jobs voluntarily because of conflicts with management. 32 percent leave for lack of career growth opportunities, and 22 percent leave because of a poor compensation structure.

Using Predictive HRMS to Reduce Turnover

The good news is that predictive HR Management Software (HRMS) is available that can help to streamline the recruitment and onboarding process, which can help to improve the candidate experience and improve retention rates. In the above infographic, produced by Enspire Learning, we can see that the number one predictor of success is having a positive experience with the immediate supervisor. Additionally, 70 percent of the workforce in America is disengaged at any one time, mostly due to lack of leadership skills from their team manager.

It’s clear that having an HRMS in place to carefully onboard, train, and sync with employees is the key to successful retention and performance.

History Lesson on HR  Predictive Analysis

HR data analytics

In Josh Bersin’s 2012 report The HR Measurement Framework,(Bersin & Associates)  he talks about the evolution of data tracking in the field of human resources. As early as the 1970s, companies were highly focused on measuring the success of customer marketing and analytics, to drive the expansion of CRM and sales metrics using software. By the millennium, companies are beginning to understand the critical nature of tracking HR data to predict the success or failure of their human capital assets. Today, there are more big data tools than ever before that aid HR teams in candidate and employee tracking.

Some best practices for HR predictive data analysis Bersin advises include:

  1. Starting with the biggest problem first, not the data.  If your company has a high turnover rate, there is a problem that needs to be addressed quickly. Then use the HRMS data to monitor the progress of the team and retention.
  2. Defining the data in terms of employee metrics. Any HRMS needs to be relevant and job related in order to be successful in predicting future behaviors. Consider the employee characteristics, skills, and backgrounds that predict success.
  3. Using tools for clean and organized data management. A system that is intuitive and offers robust reporting is a good investment. Start simple, find out what data you need to watch, and put a process in place to make sure analytics are in line with company objectives.

Human Resource Analytics are Mainstream

human capital analytics

The truth is that the trend towards understanding candidates using predictive assessments and recruitment tools is here to stay. It’s in every aspect of HR process, from candidate selection and interviewing to onboarding and training. There are a wide range of analytical tools and resources out there, as this graphic illustrates. The most important factor, however, is to determine what system best meets the unique needs of the business to predict future performance of candidates, and to engage your current employees to stay on board.


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