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.

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.

The Machine Learning Bubble?

Articles appear daily describing the accomplishments of machine learning, companies market their prowess in machine learning, and machine learning has become nearly synonymous with artificial intelligence. Michael Mozer, Scientific Advisor at AnswerOn and Professor at the University of Colorado discusses the astronomical rise of Machine Learning.

Is Big Data All It’s Cracked Up To Be?

Michael Mozer, Scientific Advisor at AnswerOn and Professor at the University of Colorado discusses issues facing big data and machine learning. What does “big data” mean and how can it help us with analysis?