Post on 15-Jan-2016
Perceptual Mapping Techniques
Perceptual Map
Need 2
Need 1
+20
+20
-20
-20
SELF
PrHi
Bu
Si
Ot
SEMI
SONO
SOLD
SULI
SAMA
SUSI
SALT
SIBI
SIRO
Semantic ScalingResearch 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 attributesUnimportant attributes get low ratingsImportant attributes may be overlooked overlooked
• Limited rating scaleConstrained upper & lower ratingsGradients may not adequately differentiateImplicitly 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 notDescribes it describecompletely it at all| | | | | |0 1 2 3 4 5
Conventional MappingSnake Chart
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 notDescribes it describecompletely it at all| | | | | |0 1 2 3 4 5
Conventional MappingSnake Chart
Perceptual Map
LowLowQualityQuality
Low PriceLow Price
High PriceHigh Price
HighHighQualityQuality
G
C
F
E
BD
A
Perceptual Map
LowLowQualityQuality
Low PriceLow Price
High PriceHigh Price
HighHighQualityQuality
G
C
F
E
BD
AVALUE
Perceptual Map
LowLowQualityQuality
Low PriceLow Price
High PriceHigh Price
HighHighQualityQuality
G
C
F
E
BD
A
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
LowLowQualityQuality
Low PriceLow Price
High PriceHigh Price
HighHighQualityQuality
G
C
F
E
BD
A
1
2 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 similarityA is more like B than B is like C
• Statistically ‘reduce’ the data to a 2-dimensional mappingUsually 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•
OldMilwaukee Light
•
Budweiser
• Coors
Popular with MenHeavy
Special Occasions
Dining Out Premium
Popular with
Women
Light
Pale Color
On a Budget
Good ValueBlue Collar
Full Bodied •
Meister Brau
Stroh’s
•
•
•
Beck’s
• Heineken
Old Milwaukee
•
Miller •
Michelob•
Miller Lite
• Coors Light•
OldMilwaukee Light
•
Budweiser
Less Filling
Beer Market Perceptual Mapping
Popular with MenHeavy
Special Occasions
Dining Out Premium
Popular with
Women
Light
Pale Color
On a Budget
Good ValueBlue Collar
Full Bodied
PremiumBudget
Light
Regular
Less Filling
Beer Market Perceptual Mapping
• Coors
Popular with MenHeavy
Special Occasions
Dining Out Premium
Popular with
Women
Light
Pale Color
On a Budget
Good ValueBlue Collar
Full Bodied
PremiumBudget
Light
Regular
•
Meister Brau
Stroh’s
•
•
•
Beck’s
• Heineken
Old Milwaukee
•
Miller •
Michelob•
Miller Lite
• Coors Light•
OldMilwaukee Light
•
Budweiser
Less Filling
Beer Market Perceptual Mapping
• Coors
PremiumBudget
Light
Regular
•
Meister Brau
Stroh’s
•
•
•
Beck’s
• Heineken
Old Milwaukee
•
Miller •
Michelob•
Miller Lite
• Coors Light•
OldMilwaukee Light
•
Budweiser
Beer Market Perceptual Mapping
Multidimensional Scaling
• Smaller samples (than semantic scaling)Very high cost methodology
• Requires extensive interpretationBy definition, results are equivocal
• Conventional wisdom: “more precise”How does anybody know?
• Separate effort to juxtapose preferencesDerived from brand rankings‘Joint space’ maps
Conjoint Measurement
• Pairs of tightly defined alternativesReduced attribute setSpecific attribute values‘Orthogonal arrays’
• Computed ‘utility’ weightsBased on pairwise preferencesIf added, reflect original preferencesBasis for inferences re: attribute importance weights
Conjoint Measurement
• Smaller samples (than semantic scaling)Very high cost methodology
• Requires extensive interpretationHighly complex, hardly intuitive
• Basis for strong insightsPotentially dangerous if used literally