Perceptual Mapping Techniques |authorSTREAM

Semantic Scaling Research Illustration How sweet is your ideal cola ? How important is it to you that a cola have the proper sweetness ? How closely does brand X match to your ideal sweetness ? Very=4 Somewhat=3 Not much=2 Not at all=1 Semantic Scaling Large samples (typically) Survey-based methodology A priori selection of attributes Unimportant attributes get low ratings Important attributes may be overlooked overlooked Limited rating scale Constrained upper & lower ratings Gradients may not adequately differentiate Implicitly assumes linear relationships (Relatively) easy understand & apply 1. Company provides adequate insurance coverage for my car. 2. Company will not cancel policy because of age, accident experience, or health problems. 3. Friendly and considerate. 4. Settles claims fairly. 5. Inefficient, hard to deal with. 6. Provides good advice about types and amounts of coverage to buy. 7. Too big to care about individual customers. 8. Explains things clearly. 9. Premium rates are lower than most companies. 10. Has personnel available for questions all over the country. 11. Will raise premiums because of age. 12. Takes a long time to settle a claim. 13. Very professional/modern. 14. Specialists in serving my local area. 15. Quick, reliable service, easily accessible. 16. A “good citizen” in community. 17. Has complete line of insurance products available. 18. Is widely known “name company”. 19. Is very aggressive, rapidly growing company. 20. Provides advice on how to avoid accidents. Does not Describes it describe completely it at all | | | | | | 0 1 2 3 4 5 Conventional Mapping Snake Chart Ideal Points Customer perceptions Aggregation of individuals Distributions around points Different shapes Optimal points, vectors Segment variations Evolutionary progression Nice to have =>, Must have In general ... Most of a brand’s sales will come from the segments with the closest ideal points Most of a segment’s sales (share) will go to the brands closest to its ideal point Targeting Strategies Direct hit … single product ‘right on’ Bracketing multiple products ‘surround’ “Tweeners” single product ‘splitting the difference’ to induce a new segmentation Multidimensional Scaling (MDS) Rank pairs of products (brands) by degree of similarity A is more like B than B is like C Statistically ‘reduce’ the data to a 2-dimensional mapping Usually a ‘black box’ application Judgmentally interpret the axes Multi-dimensionally Mix of art and science Beer Market Perceptual Mapping Meister Brau Stroh’s Beck’s Heineken Old Milwaukee Miller Coors Michelob Miller Lite Coors Light Old Milwaukee Light Budweiser Coors Popular with Men Heavy Special Occasions Dining Out Premium Popular with Women Light Pale Color On a Budget Good Value Blue Collar Full Bodied Meister Brau Stroh’s Beck’s Heineken Old Milwaukee Miller Michelob Miller Lite Coors Light Old Milwaukee Light Budweiser Less Filling Beer Market Perceptual Mapping Popular with Men Heavy Special Occasions Dining Out Premium Popular with Women Light Pale Color On a Budget Good Value Blue Collar Full Bodied Premium Budget Light Regular Less Filling Beer Market Perceptual Mapping Coors Popular with Men Heavy Special Occasions Dining Out Premium Popular with Women Light Pale Color On a Budget Good Value Blue Collar Full Bodied Premium Budget Light Regular Meister Brau Stroh’s Beck’s Heineken Old Milwaukee Miller Michelob Miller Lite Coors Light Old Milwaukee Light Budweiser Less Filling Beer Market Perceptual Mapping Coors Premium Budget Light Regular Meister Brau Stroh’s Beck’s Heineken Old Milwaukee Miller Michelob Miller Lite Coors Light Old Milwaukee Light Budweiser Beer Market Perceptual Mapping Multidimensional Scaling Smaller samples (than semantic scaling) Very high cost methodology Requires extensive interpretation By definition, results are equivocal Conventional wisdom: “more precise” How does anybody know? Separate effort to juxtapose preferences Derived from brand rankings ‘Joint space’ maps Conjoint Measurement Pairs of tightly defined alternatives Reduced attribute set Specific attribute values ‘Orthogonal arrays’ Computed ‘utility’ weights Based on pairwise preferences If added, reflect original preferences Basis for inferences re: attribute importance weights Conjoint Measurement Smaller samples (than semantic scaling) Very high cost methodology Requires extensive interpretation Highly complex, hardly intuitive Basis for strong insights Potentially dangerous if used literally You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation. HTTPS (Hypertext Transfer Protocol Secure) is a protocol used by Web servers to transfer and display Web content securely. Most web browsers block content or generate a “mixed content” warning when users access web pages via HTTPS that contain embedded content loaded via HTTP. To prevent users from facing this, Use HTTPS option. Source.


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Last Modified: April 18, 2016 @ 9:12 pm