perceptual mapping techniques.ppt

8/9/2019 Perceptual Mapping Techniques.ppt

1/23

Perceptual Mapping Techniques


2/23

Perceptual Map

Need 2

Need 1

+20

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-20

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SELF

Pr

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Bu

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SEMI

SONO

SOLD

SULI

SAMA

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3/23

Semantic ScalingResearch Illustration

• How sweet is your ideal cola ?

• How important is it to you that a colahave the proper sweetness ?

• How closely does brand X match toyour ideal sweetness ?

Very! Somewhat" #ot much$ #ot at all%


4/23

Semantic Scaling

• &arge samples 'typically(Survey-based methodology

• ) priori selection o* attributesUnimportant attributes get low ratings

Important attributes may be overlooked overlooked

• &imited rating scaleConstrained upper & lower ratingsGradients may not adequately dierentiateImplicitly assumes linear relationships

• '+elatively( easy understand , apply


5/23

%- .ompany provides adequate insurance coverage *or my car-

$- .ompany will not cancel policy because o* age/ accidente0perience/ or health problems-

"- 1riendly and considerate-

!- Settles claims *airly-2- 3ne**icient/ hard to deal with-

4- Provides good advice about types and amounts o* coverage tobuy-

5- Too big to care about individual customers-

6- 70plains things clearly-

8- Premium rates are lower than most companies-

%9- Has personnel available *or questions all over the country-

%%- :ill raise premiums because o* age-

%$- Ta;es a long t ime to settle a claim-

%"- Very pro*essionalgood citien@ in community-

%5- Has complete line o* insurance products available-

%6- 3s widely ;nown >name company@-

%8- 3s very aggressive/ rapidly growing company-

$9- Provides advice on how to avoid accidents-

Does notDescribes it describecompletely it at all

| | | | | |

0 1 2 3 4 5

.onventional MappingSnake Chart


6/23

%- .ompany provides adequate insurance coverage *or my car-

$- .ompany will not cancel policy because o* age/ accident e0perience/ orhealth problems-

"- 1riendly and considerate-

!- Settles claims *airly-

2- 3ne**icient/ hard to deal with-

4- Provides good advice about types and amounts o* coverage to buy-5- Too big to care about individual customers-

6- 70plains things clearly-

8- Premium rates are lower than most companies-

%9- Has personnel available *or questions all over the country-

%%- :ill raise premiums because o* age-

%$- Ta;es a long time to settle a claim-

%"- Very pro*essionalgood citi?en@ in community-

%5- Has complete line o* insurance products available-

%6- 3s widely ;nown >name company@-

%8- 3s very aggressive/ rapidly growing company-

$9- Provides advice on how to avoid accidents-

Does notDescribes it describecompletely it at all

| | | | | |

0 1 2 3 4 5

.onventional MappingSnake Chart


7/23

Perceptual Map

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8/23

Perceptual Map

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9/23

Perceptual Map

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10/23

3deal Points

• .ustomer perceptions

• )ggregation o* individualsB

Cistributions around points• Ci**erent shapes

BDptimal points/ vectors

• Segment variations

• 7volutionary progressionB#ice to have E Must have


11/23

Pre*erence Models

• 3deal points !individuals"

• .lusters !segments"

• Pro0imity !preerence"


12/23

Perceptual Map

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13/23

3n general ---

• Most o* a brandFs sales will come *rom the

segments with the closest ideal points

• Most o* a segmentFs sales 'share( will go

to the brands closest to its ideal point


14/23


15/23

Multidimensional Scaling

'MCS(

• +an; pairs o* products 'brands(by degree o* similarity

% is more like than is like C

• Statistically reduceF the data to a$Idimensional mappingUsually a #black bo'$ application

• Judgmentally interpret the a0es Multi-dimensionally

(i' o art and science


16/23

Geer Mar;et)erceptual (apping

•

Meister Brau

Stroh’s

•

•

•

Beck’s

• Heineken

Old Milwaukee

•

Miller •

Coors•

Michelob

• Miller

Lite

• CoorsLight•

Old

Milwaukee Light

•

Budweiser


17/23

• Coors

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with MenHeavy

SpecialOccasions

Dining Out Preiu

Popular

with

!oen

Light

Pale Color

On a

Budget

"ood #alueBlue Collar

$ull Bodied •

Meister Brau

Stroh’s

•

•

•

Beck’s

• Heineken

Old Milwaukee

•

Miller •

Michelob

• Miller

Lite

• CoorsLight•

Old

Milwaukee Light

•

Budweiser

Less $illing



18/23

Popular

with MenHeavy

SpecialOccasions

Dining Out Preiu

Popular

with

!oen

Light

Pale Color

On a

Budget


$ull Bodied

PreiuBudget

Light

%egular

Less $illing



19/23

• Coors

Popular

with MenHeavy

SpecialOccasions

Dining Out Preiu

Popular

with

!oen

Light

Pale Color

On a

Budget


$ull Bodied

PreiuBudget

Light

%egular

•

Meister Brau

Stroh’s

•

•

•

Beck’s

• Heineken

Old Milwaukee

•

Miller •

Michelob

• Miller

Lite

• CoorsLight•

Old

Milwaukee Light

•

Budweiser

Less $illing



20/23

• Coors

PreiuBudget

Light

%egular

•

Meister Brau

Stroh’s

•

•

•

Beck’s

• Heineken

Old Milwaukee

•

Miller •

Michelob

• Miller

Lite

• CoorsLight•

Old

Milwaukee Light

•

Budweiser



21/23


22/23

.onLoint Measurement

• Pairs o* tightly de*ined alternativesReduced attribute set

Speciic attribute values

#0rthogonal arrays$

• .omputed utilityF weightsased on pairwise preerences

I added+ relect original preerencesasis or inerences re1 attribute importance weights


23/23

.onLoint Measurement

• Smaller samples 'than semantic scaling(*ery high cost methodology

• +equires e0tensive interpretation,ighly comple'+ hardly intuitive

• Gasis *or strong insights)otentially dangerous i used literally

perceptual mapping techniques.ppt

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