1. Perceptual Mapping Techniques

2. 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.

3. 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

4. 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

5. Conventional Mapping Snake Chart
Does not Describes it describe completely it at all
| | | | | |
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

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

8. 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

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

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

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

12. Perceptual Map E A G D C B F High Price Low High Quality Quality2 3 C B
Low
Quality
High
Quality 1 F
Low Price 3

13. 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

14. Targeting Strategies Direct hit … single product ‘right on’
Bracketing multiple products ‘surround’
“Tweeners” single product ‘splitting the difference’ to induce a new segmentation

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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