6 use cases for improving employee retention using predictive analytics

Employee retention continues to be a top concern for CHROs and their subordinates, and predictive analytics can carry out use cases such as determining the probability that someone is going to leave an organization and making recommendations for how to improve employee retention.

A company’s CHRO and other company leaders must reassure employees about the organization’s use of predictive analytics. The technology’s use of Personally Identifiable Information, the possibility of bias and the implementation of safeguards must all be openly discussed with employees.

In addition, not all companies experience success with predictive analytics. Predictions’ reliability depends on the company size and market, the quality of HR data and the tools that the company is using. For example, a large multinational company that’s using advanced HR systems and analytics will likely receive more accurate predictions than an SMB that possesses limited data and is using more basic technology.

CHROs and other leaders should remember that predictive analytics’ results are only estimates and may not reflect actual outcomes. Monitoring projections can help leaders better understand how accurate they are so the model can be refined if needed.

Here are some use cases where predictive analytics can potentially help improve employee retention.

1.     Training

Employees may not find generic training engaging. Some HR systems can develop a customized learning plan and learning opportunities for employees, which can improve the overall learning experience for workers and potentially improve retention because they will be happier with their training.

AI can use past employee data, past course completions and data from other employees in similar roles to predict the types of courses that would most benefit and be interesting to employees.

2.     Promotion

Predictive analytics can use a combination of HRIS, performance and succession planning data to identify employees who are good candidates for promotions.

The employee’s manager can then work with HR to coach the employee, either through formal coaching, informal coaching or an external program.

Preparing employees for promotions and giving them promotions can positively impact employee retention because workers will be happy about their new titles. Conversely, bypassing qualified employees or hiring externally when a leadership position becomes vacant can negatively impact retention.

3.     Compensation planning

Predictive analytics can provide insight into whether an employee should receive an increase in compensation and what type of compensation would be best. The experience of the employee’s manager is of course vital here as well.

Giving a raise and options in the company to an employee who is a flight risk could provide incentive for them to stay with the company.

4.     Employee experience

Employees interact with various company systems and processes every day, which affects employee experience. For example, a negative experience with onboarding tools could permanently affect a worker’s perception of the organization.

A CHRO and their subordinates can use predictive analytics and employee sentiment data to evaluate the positive or negative effect of the company’s systems on employees and the likelihood of turnover caused by poor employee experience.

5.     Turnover root cause analysis

Predictive analytics can help identify common issues that lead to turnover. Once the technology has identified possible root causes, the CHRO and leadership team can investigate and address the issues.

SMBs may find carrying out this analysis more challenging because of the smaller amount of turnover in their organization and smaller amount of data to feed the model. Also, CHROs should keep in mind that predictive analytics’ findings may not be completely accurate, as employees are not always candid about the reasons for their departure during exit interviews.

6.     Recruitment

Retention starts with hiring the right people. AI capabilities in recruiting platforms can identify candidates that the technology has found to be the best match for the job requirements.

However, human employees remain an important part of the hiring process and should evaluate whether job candidates show growth potential and possess the personal characteristics needed for the position. In addition, recruiters must be aware of the potential for bias in AI recruiting tools.

Eric St-Jean is an independent consultant with a particular focus on HR technology, project management and Microsoft Excel training and automation. He writes about numerous business and technology areas.