The document discusses various perceptual mapping techniques used to understand consumer preferences and product positioning. It describes semantic scaling surveys that ask consumers to rate products on important attributes to map their perceptions. It also covers multidimensional scaling which statistically maps similarity ratings between products onto a 2D space. Conjoint analysis is mentioned as deriving utility weights for attributes from pairwise preference judgments.
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2. Perceptual Map
Need 2
Need 1
+20
+20
-20
-20
SELF
Pr
Hi
Bu
Si
Ot
SEMI
SONO
SOLD
SUL
I
SAMA
SUSI
SALT
SIBI
SIRO
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. 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
6. 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
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
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
•
Meister Brau
Stroh’s
•
•
•
Beck’s
• Heineken
Old Milwaukee
•
Miller •
Coors
•
Michelob•
Miller
Lite
• Coors
Light•
Old
Milwaukee Light
•
Budweiser
17. •Coors
Popular
with MenHeavy
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
18. Popular
with MenHeavy
Special
Occasions
Dining Out Premium
Popular
with
Women
Light
Pale Color
On a
Budget
Good Value
Blue Collar
Full Bodied
PremiumBudget
Light
Regular
Less Filling
Beer Market
Perceptual Mapping
19. •Coors
Popular
with MenHeavy
Special
Occasions
Dining Out Premium
Popular
with
Women
Light
Pale Color
On a
Budget
Good Value
Blue Collar
Full Bodied
PremiumBudget
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
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
Editor's Notes
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.