Marketing Research的课件- MDS_s.ppt

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Marketing Research的课件- MDS_s

Marketing Research 7th Edition Aaker, Kumar, Day Multidimensional Scaling Session: April 19-23; 2010 1. Objectives MDS/MDU used to Identify dimensions by which objects are perceived or evaluated Position the objects with respect to those dimensions Position the objects vis-à-vis one another Perceptual Map 1. Objectives (ctd) E.g. Are there opportunities for the introduction of a new Abbey beer brand, and how should it be positioned? How is ‘light butter’ perceived compared to margarine and butter products? Which are the closest competitors of a new brand of Lingerie? MDS Approaches 2. Steps Design Assumptions Model Estimation and Fit Interpretation of Results Validation 2.1. Design Attribute-based or Non-attribute-based? Individual-level or aggregate? Object selection Data collection Attribute-based MDS +: Diagnostic and operational value Often predict better -: Accurate: do consumers use these attributes? Complete? Often needs more dimensions Data Collection Data on what? Object (dis)similarities Object preferences Object possession of attributes Collected how? Ranking Rating Picking Data collection: Typical approaches Similarities data: paired comparisons rating, ranking, rotating anchor point ranking Preference data: rating by object, picking by object pair, ranking or constant sum for all objects Attribute evaluations: rating by object, ranking or constant sum for all objects Data Collection: Example Objects = 4 brands of beer: Tuborg (T), Heineken (H), Amstel (A), Stella (S) ? 6 brand pairs Example Beer brands: Similarities? Paired comparisons rating: give separate similarity score for each of the 6 brand pairs TH, TA, TS, HA, HS, AS Rank pairs from most to least similar, e.g. AS, TH, TS, HS, TA, HA Rotating Anchor point Ranking: Example Beer brands: Preferences? Rating by Object: give separate preference score for each of the 4 brands T,H,S,A Picking by object pair: for each of the 6 brand pairs, select preferred one, e.g in TH: H picked Rank brands f

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