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Eliciting Consumer Preferences Through Conjoint Analysis in Market Research

Conjoint analysis is a powerful market research technique used to assess consumer preferences for homogeneous product classes by examining attributes and their levels. By simulating real-choice tasks with pairwise comparisons, researchers can identify which product features influence consumer choices. For instance, when analyzing cars based on attributes like maximum speed and gas mileage, respondents' preferences can reveal valuable insights for product development and marketing strategies. This method enables effective optimization of visualization systems and enhances understanding of consumer behavior.

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Eliciting Consumer Preferences Through Conjoint Analysis in Market Research

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  1. Preference Elicitation [Conjoint Analysis]

  2. Conjoint Analysis Market research: assess consumer’s preferences on homogenous class of products Approach: describe products in terms of attributes and levels [conjoint structure]. Example: Cars = (Max.Speed) x(Gas Mileage) Max. Speed = { 100 mph, 120 mph, 150 mph} Gas Mileage = { 20 mpg, 17 mpg, 13 mpg, 10 mpg}

  3. 150 mph 10 mpg 100 mph 20 mpg Pairwise Comparsions Which car are you more likely to buy? • 100 mph • 13 mpg • 120 mph • 17 mpg Tradeoff!

  4. Marketing Approach • Given a set of products X • Elicit consumer’s preferences from pairwise comparisons [simulates real choice tasks] • Only small [constant] number of questions per respondent • For each respondent value function v: X →[0,1]

  5. Optimizing Visualization Systems Which (volume) rendering shows more detail?

  6. Optimizing Visualization Systems Which (volume) rendering do you like better?

  7. Optimizing Visualization Systems Which (volume) rendering shows more detail?

  8. Optimizing Visualization Systems Which (volume) rendering shows more detail?

  9. Netflix Challenge

  10. Netflix Challenge http://www.netflixprize.com/index

  11. Netflix Challenge

  12. Netflix Challenge • Challenge: From given ratings predict rating of unrated movies. • Training data set: >100 million ratings from >480 thousand customers on ~18 thousand movies. • Test data: 2.8 million customer/movie pairs with the ratings withheld. • Compare to Netflix’ predictor ‘Cinematch’ • Quality measure: root mean square error

  13. Surface Reconstruction

  14. Surface reconstruction

  15. Surface Reconstruction

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