Perceptual Mapping Techniques

Perceptual Map Need 2 +20 -20 Pr Hi Bu Si Ot Need 1 SONO SELF SEMI SOLD SULI SAMA SUSI SALT SIBI SIRO Need 1 Multidimensional scaling of brands similarities and preferences One basis for analysing the positioning of each competitive brand is the perceptual mapping of similarities and preferences based on the Multidimensional scaling study. The data are obtained through interviews with a sample of 200 individuals. This is a two-dimensional map whose axes are arbitrarily scaled from -20 to +20 and represent composite dimensions. Axis one represents the first most important need of the consumers and axis 2 the second most important need for that product category. The study will provide the best interpretation of the composite dimensions for each axis. The circles 'Bu', 'Si', 'Pr', 'Hi', and 'Ot' on the graph represent the ideal points of each of the five segments. Each circle only represents the 'center of gravity' of the whole segment. Each consumer has a different preference, however, the preferences within a segment are sufficiently similar so that the ideal point represents well the overall global preference of the segment. The various geometric shapes (square, triangle, star...) correspond to the positioning of the brands as they are perceived by the market at the time of the study. Each brand name is clearly labeled. One specific color and shape is attached to each firm (for example, all brands marketed by firm A are represented by red stars). This study differs from the semantic scales study in that the respondent is not provided with criteria to evaluate the brands. Instead, these criteria are deduced by the approach which is based on global assessment of similarities of pairs of brands. This is a complex task which necessitates a number of brands to be able to derive a solution. This study is therefore not available for the Vodite market until sufficient competing brands are marketed.

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

Conventional Mapping Snake Chart Does not Describes it describe completely it at all | | | | | | 0 1 2 3 4 5 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. 6

Perceptual Map E A G D C B F High Price Low High Quality Quality Low Price 3

Perceptual Map E A G D C B F High Price Low High Quality Quality VALUE E A G D C B Low Quality High Quality F Low Price 3

Perceptual Map E A G D C B F High Price Low High Quality Quality Low Price 3

Ideal Points Customer perceptions Aggregation of individuals Distributions around points Different shapes Optimal points, vectors Segment variations Evolutionary progression Nice to have => Must have

Preference Models Ideal points (individuals) Clusters (segments) Proximity (preference)

Perceptual Map E A G D C B F High Price Low High Quality Quality 2 3 C B Low Quality High Quality 1 F Low Price 3

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 Old Milwaukee Budweiser Beck’s Meister Brau Heineken Miller Coors Stroh’s Michelob Coors Light Miller Lite Old Milwaukee Light 12 12 15

Beer Market Perceptual Mapping Popular with Men Heavy Full Bodied Old Milwaukee Budweiser Beck’s Meister Brau Special Occasions Heineken Miller Blue Collar Dining Out Premium Good Value Coors Stroh’s Michelob Coors Light Popular with Women Miller Lite Pale Color On a Budget Old Milwaukee Light Light Less Filling 17

Beer Market Perceptual Mapping Regular Popular with Men Heavy Full Bodied Special Occasions Blue Collar Dining Out Premium Good Value Budget Premium Popular with Women Pale Color On a Budget Light Light Less Filling 13 16 13

Beer Market Perceptual Mapping Regular Popular with Men Heavy Full Bodied Old Milwaukee Budweiser Beck’s Meister Brau Special Occasions Heineken Miller Blue Collar Dining Out Premium Good Value Coors Budget Stroh’s Premium Michelob Coors Light Popular with Women Miller Lite Pale Color On a Budget Old Milwaukee Light Light Light Less Filling 17

Beer Market Perceptual Mapping Regular Old Milwaukee Budweiser Beck’s Meister Brau Heineken Miller Coors Budget Stroh’s Premium Michelob Coors Light Miller Lite Old Milwaukee Light Light 17

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