Fighting attrition is like swatting mosquitoes. Be careful not to focus so much on the mosquito and miss the stinger.
Why is Managing Call Center Attrition like “The Witcher”?
Have you had a chance to see “The Witcher” season 1 or 2? It is a medieval fantasy series on Netflix. It has proven to be a top-rated series pitting good against evil or, more often, evil vs. evil. In binge-watching season 2, I started thinking about what lessons could be gleaned from this show relating to agent retention…
“What do you do for a living?”
Whenever I go to some social interaction (back when I could go to a social interaction), people I know or just met inevitably ask what I do and what AnswerOn does.
Boomerangs come back; will agent attrition?
Higher success at curbing churn hinges on being able to proactively keep agents from leaving. Trying to change an agent’s mind once they have already decided to leave is not only painfully frustrating, but also statistically unsuccessful. You need the tools to step in and address agent concerns or issues BEFORE they have even reached the thought of leaving your organization.
Agent Pain Points: How to Benefit from Proactive Performance Management
Call centers have long battled with the costs associated with agent turnover. Depending on the type of contact center, you might be looking at the training cost of an individual agent around $5k or even $10k. At AnswerOn, we have found that taking preventative measures is the best way to ensure your company doesn’t incur these unnecessary expenses.
Predictive Analytics: Using Data to Save Money and Employees
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.
Data Modeling and Data Analytics: What’s the Difference?
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.
Data Mining Churn Solutions: From Raw Data to Predictable Data
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.
Implementing Predictive Analytics
Predictive Analytics is a hot topic, but many consulting firms don’t tell their clients what to do with the data to improve their business. AnswerOn does.
The AnswerOn Development Environment and Process
To provide the most value for our services, AnswerOn must respond quickly and accurately to the evolving requirements of our customers. The behavior of the end user (our customer’s customer), is not static. Predictive models and prescriptive interventions must be constantly monitored and enhanced to align with behavioral changes.