perceptual mapping techniques

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