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Mapping Techniques for Market Research

Mapping Techniques for Market Research

Discover powerful mapping techniques to explore and understand the underlying preference structure of consumers. Learn how to create and interpret efficient and insightful graphical data summaries.

Course Details

Learning Objectives

Learning Objectives:

Upon completion of this module, participants will be able to:

  • Establish a relationship between product or concept acceptance and their characteristics
  • Optimize product acceptance by focusing on preferred characteristics
  • Analyze hedonic scores at the individual level
  • Identify consumer segments with similar preferences
  • Summarize results in a clear and concise manner

Target Audience

Target Audience:

This training session is intended for people collecting preference data wishing to determine consumer and product segments to identify market opportunities.



Basic knowledge of regression analysis and principal component analysis is desirable. However, a brief review of these techniques will be given during the course.

Course Outline

Course Outline:
  • Introduction to Consumer Preferences
  • Traditional Methods
    • Measuring Preferences
    • Traditional Methods of Analyzing Preference Data
    • Limitations
  • Overview of Preference Mapping
    • Goals & Scope
    • Data Acquisition Considerations
    • MDPREF vs. PREFMAP Philosophies
  • MDPREF or Internal Preference Mapping
    • Methodology
    • Interpretation of Results
    • Extended MDPREF : Incorporating Additional Information
  • PREFMAP or External Preference Mapping
    • Data Preparation
    • Methodology
    • Interpretation of Results
  • Advanced Topics
    • Clustering Methods
  • Limitations, Alternative Methods and Summary

Practical Info

Practical Info:

Recommended Duration: 1 day

Related Sessions

  • Learn about preference mapping techniques to explore and understand consumer preferences. Applications dealing with segmentation and the identification of niche markets are discussed. Focus on pitfalls and good practices.

  • Understanding consumers and their preferences is crucial for competitive businesses. Learn about commonly used methods to analyse consumer test data, the way to interpret results and to communicate them with insightful graphical summaries.

  • Several clustering methods. Learn about their principle, conditions of use, data preparation phases, common pitfalls as well as good practices. Several real life applications are presented.

  • Conceptually similar to PCA, correspondence analysis a method is designed for discovering associstions in categorical rather than continuous data. Discover the informative 2D-plots for efficient data mapping.