market intelligence session 10 perceptual maps. perceptual mapping visual representation of customer...

Market Intelligence Session 10

Perceptual Maps

Perceptual Mapping • Visual representation of customer perceptions

– Shows how target customers view competing alternatives in a Euclidean space representing the market

– Pair-wise distances between alternatives indicate how close or far apart the products are in the minds of customers

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Some examples…

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Clothing retailers

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Chips

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Sports apparel

Perceptual Mapping • Uses of maps

– Identify your closest competitors– Suggest repositioning strategies – Suggest advertising themes supporting

repositioning– Identify new product opportunities where some

segment not well served by current brands

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Perceptual Mapping • 2 types of maps, based on different ways of

measuring similarity between brands:– 1. Similarity-Based Map

• Based on ratings of overall similarity b/w brands• Multidimensional scaling (MDS) to analyze

– 2. Attribute-Based Map• Based on ratings of brands on various perceptual

attributes• Brands that are highly correlated on attributes are similar• Factor Analysis/Principal Components Analysis to analyze

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When to use similarity vs. attribute based?

• Advantages to similarity based maps:– Allows you to map products without specifying list

of attributes– Better for “softer” attributes which we do not

verbalize well (feel, aesthetics, smell)• Disadvantages to similarity based maps:

– Impractical when number of products/brands is large

– Interpretation of axes is more difficult

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When to use similarity vs. attribute based?

• Advantages to attribute-based map– Works well for hard or functional attributes (product

features)– Fewer questions required of respondents (vs. similarity),

especially with large number of considered products• Disadvantages to attribute-based maps

– Researchers needs to clearly conceptualize attributes– Misleading if attributes are not ones most important to

consumers– Implicit equal weighting of attributes

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Similarity Based Map

• Generate relevant set of objects brand, products– Relevance: set of products chosen must be the set of

competitive products that are relevant for managerial decision making

• Have respondents rate similarity (e.g. 1-10 pt scale) between every possible brand pairing

• Can perfectly represent 3 brands in 2 dimensions, but if more than 3, there will be information loss– MDS is a mathematical technique used to analyze

similarity perceptions with minimum information loss

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Similarity based map: Soap example

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Similarity based map: Soap example

Aggregate across respondents so these are averages

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SPSS Commands – similarity based

• Analyze – Scale – Multidimensional scaling (Proxscal) – Select Define– Select variables (brands to include)– Model

• Proximity transformations: Interval• Shape: Upper triangular matrix• Proximities: Similarities• Dimension: min = 2, max = 2

– Plots• Check “common space”

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SPSS Output – similarity based

• Check fit of model (2 dimensions)• Goodness of fit “S-Stress”. Want it less than

0.10

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X, Y coordinates can be plotted

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Labeling dimensions

• Not always obvious• 3 ways to generate labels

– Your own judgment– Have respondents look at dimensions – Run 2 regression with various attributes as

predictors: once with X coordinates as DV, then with Y coordinates as DV

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Applications

• Where are we and competition on key dimensions?

• Who are Dove’s biggest competitors?• Which brand is seen as most different from

Dial?• Are there clusters of brands (substitution) or are

they spread out?• Are there gaps in the market?

– What would you want to know first?

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Next step: Plotting ideal points

• Ask respondents to rate similarity between each brand and their “ideal” on same scale as before

• Their ideal becomes another “brand” in analysis

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Similarity based map with ideal point

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Mapping ideal points

• Run analysis separately for each respondent to get individual x,y coordinates for “ideal”

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Similarity map with 1 person’s ideal point

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Final step• Create scatterplot with:

– Original coordinates (from aggregate data) for each brand– Each respondent’s ideal point coordinates (gotten from

separate MDS for each person)

+ For each person…

Baesd on averages

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Brands

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

Y-Value 1

Y-Value 1

Caress

Dial

DoveIrish Spring

Safeguard

Lever 2000

Ivory

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With ideal points

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Y-Value 1

Y-Value 1

Caress

Dial

DoveIrish Spring

Safeguard

Lever 2000

Ivory

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Applications

• Are there unmet needs in the market? (any ideal points with no brand close by?)

• Segments of consumers who want different things?

• Competitor analysis• Repositioning strategy?• Brand/line extension opportunities?• What should I communicate to customers?

Perceptual Mapping: Type 2: Attribute-based

• Based on ratings of brands on different attributes

• Steps– Generate list of relevant brands– Generate list of key attributes

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Car example

Cars• Ford• Infiniti• Cadillac• Camero• Mercedes• Mazda• Buick• Porsche• Kia• Audi

Attributes• unreliable• roomy • Prestige• Highquality• Lowprofiletires• Sporty• Powerfulengine• Smoothride• Tighthandling• Poorvalue• Attractive• Quiet• Poorlybuilt• Uncomfortable• Premiumsound- system

Perceptual Mapping: Attribute-based


• Steps– Generate list of relevant brands– Generate list of key attributes – Consumers rate each brand on each attribute

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For each brand, ask consumers to rate to what extent each attribute describes the brand

Car X

Strongly StronglyDisagree Agree

1 2 3 4 5 6 7 8 9 10Attribute A ____ ____ ____ ____ ____ ____ ____ ____ ____ ____Attribute B ____ ____ ____ ____ ____ ____ ____ ____ ____ ____

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SPSS DATA – attribute based map

Perceptual Mapping: Attribute-based


• Steps– Generate list of relevant brands– Generate list of key attributes – Consumers rate each brand on each attribute – Factor analyze matrix of attribute ratings (use a

separate row for each brand for each respondent) 34

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Factor Analysis – Attribute based

• Data reduction technique that is useful in mapping. – Identifies a (hopefully) small number of factors or

dimensions that represent the relationships in the larger set of attributes.

– For perceptual map: do 2 factors capture a high percentage of the variance in the data?

• Observed correlations in the data are assumed to be the result of sharing the latent (unobserved) factors.

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SPSS Commands – attribute basedNote: Lots of alternatives here, a basic example

• Analyze – Dimension Reduction – Factor– Select variables (attributes to include, do not include the brands here)– Descriptives

• Initial Solution• (Correlation) Coefficients

– Extraction• Method: principle components• Correlation Matrix• Unrotated Factor Solution• Extract – Fixed Number of Factors – 2

– Rotation • varimax • Loading Plots• rotated solution

– Scores• Save as variables (regression method)• Display Factor Score Coefficient Matrix

– Options• Sorted by size

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Output - Correlations

• Provides a descriptive pairwise correlation matrix. You can get a feel for the data, e.g., “unreliable” and “high quality” should be negatively correlated.

unreliable roomy prestige highquality lowprofiletires

Correlation unreliable roomy prestige highquality lowprofiletires Sporty powerfulengine smoothride tighthandling poorvalue attractive quiet poorlybuilt uncomfortable premiumsoundsystem

1.000.792

-.871-.955-.639-.248-.570.360

-.166.889

-.679-.055.931.177

-.670

.7921.000-.515-.867-.704-.628-.755.532

-.308.831

-.422.086.744

-.151-.537

-.871-.5151.000

.845

.365-.091.214.028

-.057-.756.463.252

-.836-.185.426

-9.55-.867.845

1.000.605.392.639

-.407.291

-.956.603.164

-.898-.033.485

-.639-.704.365.605

1.000.541.542

-.485.360

-.516.454

-.645-.516-.131.788

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Output - Correlations

• Provides a descriptive pairwise correlation matrix. You can get a feel for the data, e.g., “unreliable” and “high quality” should be negatively correlated.

unreliable roomy prestige highquality lowprofiletires

Correlation unreliable roomy prestige highquality lowprofiletires Sporty powerfulengine smoothride tighthandling poorvalue attractive quiet poorlybuilt uncomfortable premiumsoundsystem

1.000.792

-.871-.955-.639-.248-.570.360

-.166.889

-.679-.055.931.177

-.670

.7921.000-.515-.867-.704-.628-.755.532

-.308.831

-.422.086.744

-.151-.537

-.871-.5151.000

.845

.365-.091.214.028

-.057-.756.463.252

-.836-.185.426

-9.55-.867.845

1.000.605.392.639

-.407.291

-.956.603.164

-.898-.033.485

-.639-.704.365.605

1.000.541.542

-.485.360

-.516.454

-.645-.516-.131.788

Component Total % of Variance Cumulative % Total % of var. Cum. %123456789101112131415

7.7422.8002.0601.286

.430

.385

.196

.080

.0218.487E-165.793E-166.083E-18

-4.952E-17-1.462E-16-1.901E-16

51.61618.66713.733

8.5742.8652.5681.304

.530

.1425.658E-153.862E-154.055E-17

-3.301E-16-9.749E-16-1.267E-15

51.61670.28384.01692.59195.45698.02499.32899.858

100.000100.000100.000100.000100.000100.000100.000

6.9793.563

46.528 46.52823.755 70.283

Initial Eigenvalues Rotation Sums of Loadings

Tota

l Var

ianc

e E

xpla

ined

• The Eigenvalues represent the amount of variance explained by a factor and are scaled such that the sum of the Eigenvalues is equal to the total number of factors. Typically factors with Eigenvalues >1.0 are considered significant. The first 4 factors below meet this cut-off and would capture 92.6% of the total variance. We will keep 2 factors, which explain 70.3% of the variance.

Variance Explained

Component1 2

unreliableroomy prestigehighquality lowprofiletiresSporty powerfulengine smoothridetighthandlingpoorvalueattractivequietpoorlybuiltuncomfortablepremiumsound- system

-.995-.803.864.955.668.250.594

-.383.193

-.892.679.033

-.936-.192

.685

.019-.367-.361.116.342.887.707

-.853.861.266.322

-.178-.044.442

-.085

Rotated Component Matrix

• Resulting Factor Loadings (“f’s”)– This is the two factor solution (each component is a factor)– “f’s” represent correlations between the attributes (rows) and factors

(columns).– These are the coordinates for where the attributes plot in the factor space

Output - Loadings

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Output - Communalities• The reported “Extraction” is the

proportion of variance in each attribute accounted for by the 2-factor solution

• This is the sum of the squared loadings for each attribute across the 2-factors

– e.g., unreliable communality of .991 = unreliable Loadings on F1 and F2 squared = (-0.995)^2 + (0.019^2)

– Information on “quiet” is not very well captured by the two factor solution. We would need a third or fourth factor to capture the variance in the quiet variable.

Initial Extraction

unreliable roomy prestige highquality lowprofiletires Sporty powerfulengine smoothride tighthandling poorvalue attractive quiet poorlybuilt uncomfortable premiumsoundsystem

1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

1.000

.991

.780

.876

.925

.562

.850

.852

.875

.779

.866

.565

.033

.878

.232

.477

communalities

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Back to Loadings

• SPSS plots loadings as dots on a perceptual map. You can envision vectors that start at the origin and radiate in the direction of the attribute. – A vector on the map indicates both magnitude and

direction in the Euclidean space. Vectors are used to geometrically denote attributes of the brands

– The axes of the map are a special set of vectors suggesting the underlying dimensions that best characterize how customers differentiate between alternatives

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Output - SPSS Loading Plot: without rotation

-1.5 -1 -0.5 0 0.5 1 1.5

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1 sporty

tight handling

powerful engine

low profile tires

attractive

high quality

prestige

premium sound system

roomy

quiet

smooth ride

uncomfortable

unreliable

poor value

poorly built

Output - SPSS Loading Plot: with rotation

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Label Factors Now

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Now how to plot brands in this space?

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Brands

• SPSS calculates the factor score for each brand (Component scores x standardized attribute scores for each brand). These are the brand relationships that you can plot.

The F1 and F2 are generated

in SPSS as new variables

F1 F2ford -1.01336 -0.00729infiniti 1.13945 -0.05706cadillac 0.12308 -1.86319camero -1.03736 1.77516mercedes 1.09697 0.04509mazda -0.62771 0.44884buick -0.70077 -1.17192porsche 1.15774 0.77549kia -1.10467 -0.28417audi 0.96664 0.33905

-1.5 -1 -0.5 0 0.5 1 1.5

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2camero

mazda

ford

kia

buick

porsche

audi

cadillac

infiniti

mercedes

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For next time

• You will do your own attribute based map using SPSS

• We will talk more about applications of perceptual maps

• Guest speaker: Caroline Klompmaker from Burt’s Bees

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Optional slides

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SPSS Factor Analysis Process (the “math slide” – optional)

• Will evaluate as many factors as there are attributes (n).• Choose factors such that starting with the first factor (F1), it

explains as much of the total variance as possible.• Choose the second factor (F2) to be orthogonal (uncorrelated)

to the first and explain as much of the remaining variance as possible. Continue to the third, fourth, to the nth factor.

• Process can be Principle Components Analysis or some other method like Maximum Likelihood.– The process will choose the “a” weights in such a way that the factors,

the “F’s”, are optimal – where optimality is described above. The x’s are the attribute ratings.

njnjjj xaxaxaF ...2211

Component1 2


-.908-.883.652.924.748.579.824

-.688.516

-.925.751

-.040-.878-.002

.597

.408-.022-.671-.269.052.718.417

-.634.716.106.029

-.177.328.482

-.348

Component MatrixComponent

1 2


-.995-.803.864.955.668.250.594

-.383.193

-.892.679.033

-.936-.192

.685

.019-.367-.361.116.342.887.707

-.853.861.266.322

-.178-.044.442

-.085

Rotated Component Matrix

• Resulting Factor Loadings (“f’s”)– This is the two factor solution (each component is a factor)– “f’s” represent correlations between the attributes (rows) and factors

(columns).– These are the coordinates for where the attributes plot in the factor space

Output - Loadings

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Output - Factor Scores• Values in the original data can

be approximated by linear combinations of other factors – the “z’s” are the factor scores.

rjkrjkjkkj fzfzfzx ...2211

Component1 2


-.165-.102.172.147.082

-.032.039.007

-.039-.125.085.020

-.150-.068

.120

.088-.052-.187-.041.055.265.179

-.243.261

-.012.048

-.060.063.158

-.084

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Brands• SPSS calculates the factor score for each brand (Component scores x standardized

attribute scores for each brand). These are the brand relationships that you can plot.

X

=The F1 and F2 are generated

in SPSS as new variables

Component

1 2


-.165-.102.172.147.082

-.032.039.007

-.039-.125.085.020

-.150-.068

.120

.088-.052-.187-.041.055.265.179

-.243.261

-.012.048

-.060.063.158

-.084

unreliable roomy prestigehighquality

lowprofiletires sporty

powerfulengine

smoothride

tighthandling poorvalue attractive quiet

poorlybuilt

uncomfortable

premiumsoundsystem

1.055 1.70732 -0.65364 -1.33872 -0.59824 -0.95479 -0.47626 0.07383 0.25823 1.29036 0.06705 -0.62938 0.54444 0.27993 -0.3386

-1.00364 -0.73171 0.98046 0.9973 1.39589 0.09156 0.67831 -0.29532 0.85414 -0.93705 0.20115 -0.48301 -0.88147 -1.22739 1.1851

-0.23161 0.71364 0.35196 -0.44025 -0.02849 -1.34718 -1.63083 1.42738 -1.92677 0.6759 -0.06705 -0.62938 0.15555 -1.22739 1.1851

1.0551 -0.1897 -1.78494 -0.88948 0.39883 1.79187 0.24535 -0.78752 0.85414 0.44548 -0.73753 -1.21485 0.8037 1.57192 -0.1693

-1.00364 -0.82204 1.10616 1.08715 -0.17093 0.09156 0.67831 -0.66447 0.25823 -1.47469 1.13982 1.2734 -1.52962 0.49526 0

0.54042 0.89431 -0.27654 -0.26056 -0.88311 0.35314 -0.33194 -0.29532 1.05277 -0.01536 0.60344 1.41977 0.93333 -0.5814 -1.693

0.66909 0.3523 0.35196 -0.26056 -0.88311 -0.69321 -1.4865 2.04263 -1.33086 0.52229 -2.0785 0.39519 0.54444 1.14126 -1.1851

-1.13231 -1.45438 0.60336 1.17699 1.96564 1.13791 1.11127 -1.03362 0.45686 -0.70662 1.13982 -1.36122 -0.62222 -0.15073 1.1851

1.18378 0.53297 -1.40784 -1.15903 -1.02555 -0.95479 -0.0433 0.19688 -0.73495 1.29036 -0.87163 0.39519 1.32221 -1.01206 -0.5079

-1.13231 -1.00271 0.72906 1.08715 -0.17093 0.48394 1.25559 -0.66447 0.25823 -1.09066 0.60344 0.8343 -1.27036 0.7106 0.3386

F1 F2ford -1.01336 -0.00729infiniti 1.13945 -0.05706cadillac 0.12308 -1.86319camero -1.03736 1.77516mercedes 1.09697 0.04509mazda -0.62771 0.44884buick -0.70077 -1.17192porsche 1.15774 0.77549kia -1.10467 -0.28417audi 0.96664 0.33905

Standardized attribute scores

(gotten from descriptives)

market intelligence session 10 perceptual maps. perceptual mapping visual representation of customer...

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