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Customer propensity with Azure Machine Learning Studio

· 37 min watch
Ed Freeman By Ed Freeman Software Engineer III

Explore a customer propensity experiment in Azure Machine Learning Studio, prioritizing e-commerce clients likely to purchase a new service.

About this talk

Tutorial

In this video, endjineer Ed Freeman performs a "worked example" customer propensity experiment using Azure Machine Learning Studio.

"Our hypothetical situation is based on an e-commerce business that is interested in changing its business processes to prioritise customers that are more likely to purchase their new service, based on the information they provide at various stages throughout the purchasing workflow."

The worked example covers:

  • 00:20 What is "Customer Propensity"?
  • 00:35 Classification Problems
  • 01:02 Purpose of Walkthrough
  • 01:41 General Method
  • 02:45 Our example
  • 03:05 Hypothesis
  • 03:32 Interpretation of results
  • 04:02 Desired Metric - AUC
  • 04:26 Worked Example in Azure Machine Learning Studio
  • 36:35 Conclusion
About the presenter

Ed Freeman

Software Engineer III

Ed Freeman

Ed is a Data Engineer helping to deliver projects for clients of all shapes and sizes, providing best of breed technology solutions to industry specific challenges. He focusses primarily on cloud technologies, data analytics and business intelligence, though his Mathematical background has also led to a distinct interest in Data Science, Artificial Intelligence, and other related fields.

He also curates a weekly newsletter, Power BI Weekly, where you can receive all the latest Power BI news, for free.

Ed won the Cloud Apprentice of the Year at the Computing Rising Star Awards 2019.

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