Conjoint Analysis with XLSTAT-Marketing, Paris Feb. 2016 (in French)

Conjoint analysis is a method combining several techniques to build a model that allows simulating market shares. This training session considers all the things you need to know around this method, spanning from generating experimental designs to utilities computation and simulation of market shares. Both the full profile conjoint analysis and choice-based conjoint (CBC) will be tackled. Subscribe to this training session that will let you understand precisely the principles of this method and learn how to use it in XLSTAT-Marketing.

The session will take place in Paris, France.

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Conjoint analysis with XLSTAT-Marketing, 2 days training

This training will teach you how to setup a conjoint analysis to simulate market shares and thus to evaluate the impact of introducing new products. Conjoint analysis techniques are also known as trade-off methods. The program covers both classical conjoint analysis methods and choice-based coinjoint (CBC) and is illustrated using XLSTAT-Marketing. The training is followed by practical application exercises and tests to be submitted online for correction.




  • Understand the principles of conjoint analysis
  • Learn how to set up a conjoint analysis study
  • Learn how to use the XLSTAT-Marketing solution to run a conjoint analysis
  • Learn how to read and interpret conjoint analysis outputs

Training program

  • Conjoint analysis or the trade-off method: description
  • Differences between choice-based and profile-based methods
  • Design of conjoint analysis experiments
  • Utilities computation
  • Market simulation
  • Introduction to MaxDiff and TURF



per participant


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Paris France

Trainers' profiles

Emmanuel Jakobowicz

Founder of Stat4decision

With more than 15 years of experience in data-oriented projects, Emmanuel Jakobowicz is passionate about data science and entrepreneurship. He founded Stat4decision to propose a new way of assisting people in exploring their data. He owns a PhD in applied statistics and computer science. He also is an engineer in mathematics specialized in machine learning. His work experience includes research and development at Electricité de France and software development at Addinsoft XLSTAT where he was a partner, CTO, Chief Scientist, consultant and trainer for large companies, research institutes and universities.