Webinar: Using two popular data mining techniques to identify customer segments in Excel with XLSTAT, Feb 24

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Public: people working in any field in marketing and aiming at gaining their first steps in segmenting customers using data.

Length: 50 minutes (40 minutes of presentation and demo and 10 minutes of Q&A)

For more info: info@xlstat.com

Do not hesitate to spread the word! The Webinar is open to everybody, including non-users of XLSTAT.

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Webinar : Using two popular data mining techniques to identify customer segments

Customer segmentation is essential to organize personalized marketing campaigns. Two common data mining techniques can be used on customer data to investigate segmentation: Principal Component Analysis and Agglomerative Hierarchical Clustering. This Webinar features an intuitive introduction to these techniques and an application using the user-friendly XLSTAT statistical software for Excel.

Public

People working in any field in marketing and aiming at gaining their first steps in using data mining techniques to segment customers using data.

Program

  • A few words on customer segmentation
  • Segmenting clients using a simple dataset: scatter plot!
  • Tackling customer segmentation in more complex datasets
    • Data mining with Principal Component Analysis
    • Data mining with Agglomerative Hierarchical Clustering

 

Price

Dates

Start at:

End at:

Language

English

Online


Trainers' profiles


Jean-Paul Maalouf

Senior statistics consultant

Jean-Paul Maalouf is a senior statistics consultant working at Addinsoft since 2014. He holds a PhD in biology and has a substantial experience in teaching statistics, an activity he has been intensively practicing since 2012. His training beneficiaries include major French research institutes (INRA, CNRS, INSERM, CIRAD, several universities) as well as private companies around the world. His teaching methods rely on explaining statistical tools conceptually rather than mathematically. Statistics become very easy to understand for the users who do not necessarily have experience in mathematics and need to become operational very quickly.

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