Marketing Research的课件- Conjoint_IC_s.ppt

Marketing Research 7th Edition Aaker, Kumar, Day Conjoint Analysis Session: March 22-26; 2010 1. Objectives/Purpose An extremely powerful and useful analysis tool Used to determine the relative importance of various attributes to respondents, based on their making trade-off judgments Useful in Helping to select features on a new product/service Predicting sales Understanding decision processes/consumer judgments 1. Objectives (ctd) E.g. UvT: What drives students’ choice of (and willingness to pay for) a room? How can Albert Heijn compose its assortment of cereals to improve customer appeal? Nike: What are the optimal features for a new type of sneakers? 2. Steps Design Assumptions Model estimation and fit Interpreting results Validation 2.1. Design Method: Select attributes (number, type) Choose model form (additive? dependent variable?) Individual or aggregate estimation? Traditional, Choice-based or Adaptive conjoint? 2.1. Design Stimuli: Factor (= Attribute) selection Criteria: Differentiate Able to communicate Actionable Price ? Could enter as separate attribute, mind correlations or infeasible stimuli Levels: Strive for Balance Range: Feasible, Relevant, Stretch 2.1. Design Stimuli: Utility specification Part worth, Ideal Point or Linear model? Main effects or interactions? Alternative Models 2.1. Design Data collection: Presentation: Trade-off Full profile (Fractional factorial)? Preference Measure: Ranking Rating Choice (no-) Task per respondent (Regular, Adaptive, Hybrid?) Example: Sneakers 3 attributes, 3 levels each: Sole: Rubber, Polyurethane, Plastic Upper: Leather, Canvas, Nylon Price: 30$, 60$, 90$ Fractional Factorial: 9 out of 27 profiles (3 sole x 3 upper x 3 price) evaluated Example: Profiles for Sneakers 2.2. Assumptions Few statistical assumptions Theory-driven design, estimation and interpretation Overfitting? GIGO (Garbage in Garbage out)? 2.3. Model Estimation and Fit E.g. Additive Model, part-worths: where U(X)=utility of alte