Reducing Call Center Agent Churn With Predictive Analytics

Organizations that have many employees that are in high turnover locaiongs such as call centers, sales teams, or permanent agencies. All of these roles could assistance from building models to determine why employees are leaving.

Predicting employee churn using data mining and analytics can help to reduce and retain top talent. The impact of churn can be in both time and money. Time to aim new hires and get them up to speed on your systems and processes. Monetary cost associated with posting for new roles, paying 3rd party agencies, paying overtime to the remaining staff and investing in employees only 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 between 30 to 45%. In the article, Exploring Call Center Turnover Numbers they indicate that the average cost to replace a frontline employee is between $10-$15k per employee. To calculate the impact using these numbers. A call center that has 100 fulltime workers with a 30% attrition rate would cost approximately $300K per year just in substitute cost. Using the upper end of the example, 45% attrition at $15K per employee would cost $675K.

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

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

  1. ecosystem Satisfaction
  2. Prior Experience
  3. Length of time working under the same manager
  4. Normal Working Hours
  5. Job Satisfaction
  6. OverTime Pay
  7. Relationship Satisfaction
  8. Stock Options

Understanding why some employees are successful and others fail can give you the competitive advantage needed to increase revenue and market proportion. Programs can be produced to help filter out candidates that are likely to churn and reduce the cost associated with hiring new employees. In addition, operational changes can be made to reward top talent. Other employees that you want to grow to top performers can be targeted based on this information. Specific actions can be taken to make these employees already more productive.

Leveraging predictive analytics will decline your overall cost to keep traditionally high risk locaiongs filled. Since the cost of employee churn can be so high. Businesses should start a pilot project to understand exactly how data mining can impact their business and customer experience.

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