Analyzing Complex Behavior Graphs in Hadoop at Scale (webcast & podcast)
Traditional approaches to analyzing customer behavior graphs and event sequences require 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, Apigee’s Joy Thomas and Sanjeev Srivastav explore why new methods of behavior graph analysis are superior to the simplifications required for traditional data storage and classical predictive algorithms.
Joy and Sanjeev also discuss:
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