Bill Kornfeld

Sponsored by

Think Big

Event Analytics in Hadoop: Analyzing Cross-Channel Customer Behavior

Date: This event took place live on November 04 2015

Presented by: Bill Kornfeld

Duration: Approximately 60 minutes.

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Description:

Want to improve your marketing, sales and service outcomes? You'll need the right infrastructure, data management techniques, and applications to extract meaningful signals from cross channel interaction data.

Today there are adequate tools for analyzing customer behavior within individual channels. However, analyzing customer behavior across multiple channels usually requires custom engineering work within a big data environment and a standardized approach to holistically understand cross-channel customer activity.

Join Dr. Bill Kornfeld, director of R&D for Think Big, a Teradata company, to explore how to:

  • Build a cross channel event repository in Hadoop that sorts data from multiple channels by user, with both batch and real-time versions, each designed for different use cases
  • Collect multiple kinds of cookies and user ids; resolving identities and updating an event repository as identities are learned
  • Use an event repository for cross-channel reporting, path analysis, channel attribution, customer journey visualization and other descriptive analytics applications
  • Build prescriptive analytics applications such as product recommendations and next best offer using data in the event repository

If you are seeking a flexible framework to capture and analyze event data from multiple channels with a goal of becoming a more customer focused organization, then don't miss this O'Reilly webinar.

About Bill Kornfeld, Director of R&D for Think Big

Bill Kornfeld has more than three decades of experience designing, architecting, developing and managing the development of high-performance server architectures. He has worked with Hadoop since 2008, and is currently Director of R&D at Think Big, a Teradata Company. In recent years Bill has worked on a wide-range of applications in the event analytics space spanning both engineering and data science.