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

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

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

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 abandonment 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 the employee’s 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. Satisfaction with the environment

2. Previous experience

3. Amount of time working 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 advantage you need to increase revenue and market share. Programs can be created to help filter 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 become high performers can be selected based on this information. Specific actions can be taken to make these employees even more productive.

Taking advantage of predictive analytics will lower your total 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 the customer experience.