Is a favorite saying in many business circles, referring to awakening a naïve concealed danger. McGraw-Hill defines it as a proverb-  “Do not instigate trouble; Leave something alone if it might cause trouble.” We have observed a number of call center operators express this idiom when asked to speak to their at-risk employees. Read More
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. Read More
AnswerOn’s CEO Eric Johnson compares AnswerOn’s solutions to prevent call center agent attrition with five strategies of Fly Fishing. Read More
CEO Eric Johnson discusses the unique challenges the security industry faces in fighting attrition; including identifying two different types of churn. One of the challenges in attacking attrition in the Security industry is agreeing on a definition for attrition.  There are basically two types of Churn commonly mentioned: 1) Gross Attrition and 2) Net Attrition.  Read More
Managers think call center agents leave because they are unhappy with their income. AnswerOn has found that money doesn’t matter as much as you might think. CEO Eric Johnson outlines the leading causes of agent attrition. Read More
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When building predictive models, the information provided to the model and the manner in which it is encoded or formatted is known as the input representation. The choice of representation can determine what can be learned and how readily it will be learned. Read More
answeron team at habitat for humanity st vrain colorado
Pictures of AnswerOn’s staff volunteering with Habitat for Humanity of St. Vrain Valley! Read More
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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? Read More
Although neural networks have been around for sixty years, a seminal research paper appeared thirty years ago that revolutionized the field. The paper was called Learning representations by back-propagating errors (Rumelhart, Hinton, & Williams, 1986). In this post, I’ll explain what “learning representations” means and why this idea is so central to neural networks. Read More