Organizations that have many employees who are in high turnover positions, such as call centers, sales teams, or temporary agencies. All of these roles could benefit from modeling to determine why employees leave.

Predicting employee turnover through data mining and analytics can help reduce and retain top talent. The impact of turnover can be in both time and money. It’s time to train new hires and get them up to speed on your systems and processes. Monetary cost associated with posting new roles, paying third-party agencies, paying overtime to remaining staff, and investing in employees just to have them leave within six months to a year.

According to Quality Assurance & Training Connection, the standard turnover rate for the call center industry is 30-45%. In the article, Exploring Call Center Turnover Numbers, they indicate that the average cost to replace a frontline employee is $10 to $15,000 per employee. To calculate the impact using these numbers. A call center that has 100 full-time workers with a 30% attrition rate would cost approximately $300,000 per year in replacement costs alone. Using the high end of the example, 45% churn at $15,000 per employee would cost $675,000.

By collecting data on employees and then building a predictive model using employees who have left the organization. A predictive analytics model can be created that will give you new insights into the characteristics of employees at high risk of leaving. In addition, employees with a low risk of leaving would have different characteristics. The output of the model creates a score for each employee that indicates their probability of leaving or staying. By having this score, you can compare employee performance to determine options to keep your best talent and prevent them from leaving.

Some of the factors that could be used in the model include:

1. Environment Satisfaction
2. Previous experience
3. Working time under the same manager
4. normal working hours
5. Work satisfaction
6. overtime pay
7. relationship satisfaction
8. storage options

Understanding why some employees succeed and others fail can give you the competitive edge needed to increase revenue and market share. Programs can be created to help screen candidates who are likely to drop out and reduce the cost associated with hiring new employees. Additionally, operational changes can be made to reward top talent. Other employees you want to grow to peak performance can be targeted based on this information. Specific actions can be taken to make these employees even more productive.

Leveraging predictive analytics will lower your overall cost of keeping traditionally high-risk positions filled. Since the cost of employee turnover can be very high. Companies should start a pilot project to understand exactly how data mining can affect their business and customer experience.