Retail Webinar Replay:
Generating Big Data Insights for Next Generation Retail and CPG Businesses


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Retail Webinar

Machine learning on Hadoop provides the ability to streamline customer sentiment analysis, optimize pricing, and increase recommendation accuracy.

Join Hortonworks' GM of Consumer Products and Retail, Eric Thorsen, and Skytree's CTO, Dr. Alex Gray,  to learn how Machine Learning combined with Hadoop infrastructure allows you to harness the power of your data center. This webinar will help you understand Apache Hadoop and machine learning on big data as they relate to your business-critical goals:

  • Build intimacy with the millennial shopper
  • Accurately build and present relevant promotions to shoppers for cross-sell and up-sell
  • Collect social media and brand sentiment to accurately manage portfolios, assortments, and product extensions


Eric Thorsen, General Manager, Retail and Consumer Packaged Goods at Hortonworks
  • Modern Data Architecture, what are the challenges:
    • Single View of Customer
    • Pricing
    • Recommendations
Alexander Gray, Ph.D., CTO and co-founder at Skytree
  • What is Machine Learning?
  • How leading Retailers are applying Machine Learning:
    • Customer Sentiment
    • Dynamic Pricing
    • Lead Scoring
    • Recommendations


Eric Thorsen

Eric Thorsen General Manager, Retail and Consumer Packaged Goods at Hortonworks

Eric is the GM of Retail and Consumer Packaged Goods at Hortonworks focused on one of the fastest growing technology segments; big data, which brings a myriad of associated technology opportunities. Eric specializes in the value proposition for big data in the retail industry, ensuring customer success through innovative application of big data solutions, and applying the blended benefits from the partner ecosystem to each unique customer situation.

Alexander Gray

Alexander Gray, Ph.D., CTO and co-founder at Skytree

Dr. Gray is CTO at Skytree and Associate Professor in the College of Computing at Georgia Tech.  His work has focused on algorithmic techniques for making machine learning tractable on massive datasets.  He began working with large-scale scientific data in 1993 at NASA’s Jet Propulsion Laboratory in its Machine Learning Systems Group. He recently served on the National Academy of Sciences Committee on the Analysis of Massive Data, as a Kavli Scholar, and a Berkeley Simons Fellow, and is a frequent advisor and speaker on the topic of machine learning on big data in academia, science, and industry.

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