Predictive Insights: How Deep Learning Neural Networks Can Deliver Better Call Monitoring Insights

by Natalie Chilton on Jul 19, 2017 8:10:00 AM

 

Dive Deep Into Neural Networks & Machine Learning 

Neural networks have gone in and out of style in neurology and computer science since the 1940s.

VoiceBase EU Set to Launch in April Ensuring the Protection of European Customers’ Data

by Emily Blazensky on Mar 29, 2017 7:36:10 AM


VoiceBase to process calls exclusively in region in support of Data Protection Directive 95/46/EC and the upcoming General Data Protection Regulation (GDPR).

3 Major Benefits of Auto Call Scoring for Cloud Contact Centers

by Emily Blazensky on Apr 5, 2016 1:30:00 AM


Today’s contact center is not lacking for data. There are mountains of calls waiting to be mined for intelligence.

However, the manual process of analyzing voice recordings in search of business opportunities is labor intensive and costly.

Contact.io – The Next Great Show for Call Marketing!

by Emily Blazensky on Jan 26, 2016 8:28:13 AM

Contact.io, is a new conference focusing on the call marketing industry, and has already established itself as THE call marketing show.

VoiceBase Announces New Predictive Analytics Product; Insights

by VoiceBase on Jan 19, 2016 4:02:52 AM

Today VoiceBase is excited to announce our new product; Insights. Insights utilizes predictive analytics to match all future calls to certain call classifiers,

Advanced Transcription and Analytics With VoiceBase and Tropo

by VoiceBase on Jul 15, 2015 2:44:57 AM

VoiceBase and Tropo have integrated to provide a powerful and complete solution for transcription, speech analytics and predictive analytics.

How To Nail Predictive Analytics By Incorporating Big Voice Predictions

by VoiceBase on Oct 2, 2014 4:57:00 AM

Predictive analytics is a branch of data mining that focuses on the prediction of future probabilities, trends and behavior. It has recently become a hot topic amongst big data fanatics and rightly so. The ability to predict future successes or failures hold value in any industry; sales, marketing, fraud detection, customer relationship management, and more. This is traditionally done by exploiting patterns found in historical, transactional and customer data to identify and optimize risks and opportunities through machine learning. The power of using machine learning capabilities to collect and organize this data is even more disruptive to an industry when you can collect different forms of data than your competitors; like spoken data.