International Customer Management Institute
Applying Predictive Analytics in Contact Centers
Advances in data mining and predictive analytics are enabling companies to gain a deeper understanding of their customers. By leveraging these techniques companies are able to create focused marketing campaigns, understand likelihood of a customer to churn or create better customer segmentation. Companies can also use these same techniques to better understand their workforce. The effective application of data mining techniques in a contact center can decrease agent turnover and improve working conditions, or insights to find the best candidates. Applied data mining and predictive analytics can highlight what traits make a high performing employee as well as the traits that lead to employee turnover. Initiating a predictive analytics project to predict agent turnover starts with reviewing available data.
For the complete article - http://www.icmi.com/Resources/Metrics/2017/06/Applying-Predictive-Analytics-in-Contact-Centers
CIO Review Article
Leveraging Big Data For Data Analytics
One of the main benefits that you can achieve using big data technology is the ability to perform advanced analytics. The ability to store vase amounts of data at a nominal cost in any format provides you quantities and types of data you could not leverage just 10 years ago. Companies that invest and leverage their data are outperforming their peers in the ability to improve service, decrease expenses or increase revenue....
For the complete article - http://bigdata.cioreview.com/cxoinsight/leveraging-big-data-for-data-analytics-nid-24225-cid-15.html
CIOReview.com - Leveraging Big Data for Data Analytics
CIOReview.com - Leveraging JMP to Improve Business Decisions
CIO Application - Moving from Business Intelligence to Predictive Analytics
SQL Server Analysis Service (SSAS) has been widely used across multiple businesses to build smart online analytical reporting solutions. It includes two different types of modeling for analysis services: Tabular and Multi Dimensional. This book covers Tabular modeling, which uses the concepts of tables and relationships with a fast in-memory engine that provides state of art compression algorithms and query performance.
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