High levels of employee churn have been standard in the financial services industry for years. Recently, those already high levels are on the rise. Why are employees leaving their roles at increasing rates? Here is an overview of what attrition looks like in the financial services industry and what companies can do to curb it.
Employee attrition rates tend to soar in January. Why are workers more likely to leave at the beginning of the year? We identify some of the common reasons that cause employees to resign and look at some measures to help reduce rates of attrition.
For decades, the practice of driving revenue has been through bringing in new business – and new customers. If you solely focus and succeed in acquisitions, you won’t account for customers leaving you. Not only does subscriber churn affect your net business, it also carries other hidden costs.
It’s no secret that high rates of employee turnover result in financial losses for a company. Where does the money go? Every step of the employment process has hidden expenses, even steps like listing a job or hiring someone. However, in call centers, one cost stands out from the rest: training and onboarding new employees.
Managers and executives know that call centers can be costly parts of business. Yet, many common call center practices thought to help reduce costs cause problems themselves. If your company wants to reduce costs funneling into contact centers, consider these two techniques and their pros and cons.
Often used interchangeably, data modeling and data analytics evaluate separate components of data. Data modeling requires setting parameters on data to better understand it.On the other hand, data analysis considers the data itself, allowing you to make informed business decisions. Here are some distinguishing qualities between modeling and analytics.
Here are several common reasons that agents leave after the onboarding process, and how AnswerOn’s solution can help call centers know which agents are at-risk and, most importantly, the proactive action that can prevent them from leaving.
With today’s technological capabilities, we have access to numerous sources of qualitative and quantitative data. However, an exhaustive amount of raw data fails to reveal patterns that describe churn rates. Moving from raw data to predictable data requires a data mining process.
Structured interactions are one of the key components in the AnswerOn System for retaining agents.Find high risk agents BEFORE they leave.
Michael Mozer, Scientific Advisor at AnswerOn and Professor at the University of Colorado discusses AnswerOn’s early days of churn prediction.