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.
“One-Size fits all” won’t make it right for all
“One-Size fits all” solutions for improving call center moral might make happy agents happier, but could push at-risk agents out the door.
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.
