Employing traditional approaches to analyzing customer behavior graphs and event sequences requires data simplifications, generalizations, and segmentations that severely degrade prediction accuracy and can lead to the loss of valuable information.
But there’s an alternative approach that retains the fidelity of customer profile data, the full sequence of events data, and enables powerful business intelligence and predictive analytics. In this webcast, join Apigee’s Joy Thomas, Chief Scientist, and Sanjeev Srivastav, VP Data Strategy, as they explore the superiority of new methods of behavior graph analysis over the simplifications required for traditional data storage and classical predictive algorithms.
Join to learn:
• How shortcomings of traditional approaches make it difficult to build and analyze complex behavior graphs
• Why GRASP—graph and sequence processing technology on Hadoop presents a different and more effective approach to behavior graph analysis
• How descriptive analytics using GRASP uncover hidden patterns in historical customer journey data
• How Predictive analytics that use behavior graphs and Bayesian algorithms, along with machine learning, ensure model performance over time
Download Video: http://youtu.be/CwxvlgW9aZY
Download Podcast: https://soundcloud.com/apigee/analyzing-complex-behavior-graphs-in-hadoop-at-scale