mva_2008 india school rick loyd

8/8/2019 Mva_2008 India School Rick Loyd

1/86

Confidential & Proprietary Copyright 2007 The Nielsen Company

Introduction to Multivariate

Analysis

CRS Quantitative School, Mumbai India

Rick Loyd, 11.00 -13.00 May 27th 2008


2/86

[Topic of Presentation] Page 2December 6, 2010 Confidential & ProprietaryCopyright 2007 The Nielsen Company

Purpose and Desired Outcomes for MVA Training

Purpose:

To enable you to understand how the key Multivariate Analysis (MVA)techniques are used to analyse research, and in particular their use in theWinning Brands (WBs) model

This course is not intended to be a how to do course

Desired outcomes for the group: Everyone should...

Have a sound grasp of the concepts underlying regression, factor andcorrespondence analyses

Know which research questions/issues these MVA techniques help answerBe confident about using them in future on Winning Brands, or recommend

their use on ad hoc projects


3/86


Agenda

What is Multivariate analysis? Techniques reviewed

Correlation

Regression, simple linear and multiple linear regression (MLR)

Factor analysis

Correspondence analysis and mapping Winning Brands Model (using the factor and regression analysis)


4/86


What is Multivariate Analysis (MVA)?

Uni Variate

analysis

Bi Variate

analysis

PersuasionPersuasion

Multi Variate

analysis

Looks at variables (questions)

one at a time. Frequencies and

averages are examples

Looks at two variables

(questions) simultaneously.

Cross tabulations and

correlations are examples

Analysis of the

relationship between

two, three or more

variables

simultaneously.

Factor andRegression Analyses

are examples

WhatisMVA?

WhatisMVA?


5/86


COVERE

DINTH

ISSES

SION

What is MVA? Summary of main techniquesTechnique Purpose in Research

Regression Used to: identify key drivers of performance (eQ); isolate factorsinfluencing brand equity (WBs); some forms of regression predictshare movements from price increases (PriceItRight, PIR)

Factor analysis Used to: examine inter-relationships between variables, with the aimof data reduction, or to identify underlying themes (eQ and WBs);build Key performance indicators from survey data (eQ and WBs)

CorrespondenceAnalysis/Biplots andMapping

Provide graphical summary of brands positioning in relative orabsolute terms across a range of perceptions/images (Used inWBs and ad hoc studies)

Clusteranalysis/Consumersegmentation

Group respondents in terms of their similarity and/or dissimilarity toestablish previously undiscovered attitudinal and/or behavioralsegments. Segmentation is key part ofWB Foresight, and a part ofmany U&A studies.

Conjoint and discretechoice modelling

Identifies the relative worth or value of each level of several attributesfrom rank-ordered preferences of attribute combinations


6/86


What is MVA? More advanced MVAtechniques used in customised research

Logistic regression

Latent class modelling

Structural Equation Modeling (SEM)

Discriminant Analysis

CHAID / CART

Bayesian Networks

Genetic Algorithms/Optimisation

WhatisMVA?

WhatisMVA?

None of these will be covered today


7/86


Software ACNielsen uses for MVA

SPSS for general univariate statistics and most MVA

Amos (SPSS Add-in module) - SEM Answer Tree (SPSS Add-in module) CHAID, CART

Latent GOLD for Latent Class Modeling/Segmentation BrandMap (Excel add-in) for Correspondence Analysis,Biplots & MDS

Sawtooth for Conjoint Analysis, Choice Modeling GeneHunter for Genetic Algorithms in Brand3

Netica Bayesian Networks

WhatisMVA?

WhatisMVA?


8/86


Information and Support sources

ACNielsen sources

Your Measurement Science Analyst ACNielsen texts Watchbuilder Measurement Science Standards Vol 2

(April 2004) Colleagues in your local company, region or globally

Software Software training schools (eg SPSS courses / SAS courses) The software packages themselves

Textbooks on market research and statistics Hair Joseph F, Anderson Rolph E, Tatham Ronald L, Black William C:

Multivariate Data Analysis Prentice Hal

The internet

General statistics websites

WhatisMVA?

WhatisMVA?


9/86


Why do MVA or Value Added Analysis?

Consumers are complex: Consumers rarely make a purchase decision based upon a single

variable

They tend to unconsciously relate their decisions with multipleparameters simultaneously

Value added analysis illuminates the data: it makes the data more actionable for the client

it shows them things that they would not otherwise easily see

is often the correct way to do it

Nielsen BPP rely heavily on MVA

WhatisM

VA?

WhatisM

VA?


10/86


CorrelationMeasure of linear association between two variables.

Must always be between -1 and +1


11/86


Correlation

Correlation is a measure of linear association between two variables

(bivariate analysis) and the building block for other multivariatetechniques such as factor and regression analyses

?How much are measures related or associated? What measures really matter?

What should I concentrate on improving?

Do they impact on overall ratings?

Reasons for purchase/satisfaction When asked directly, often told everything is important so correlation

enables regression to measure overall the strength of association

between measures Which attitudes are similar and which independent (uncorrelated)

Correlation

Correlation


12/86


-1

Correlation: the -1 to 1 scale

Correlation is a number between -1 and 1 that measures the linearassociation betweentwo variables (questions often attitudinal statements in MR)

Correlation does not imply causation Zero or low correlation does not imply that there is no association at all, just no linear

association

10 Perfect positive correlation

Total cost=fixed + variable costs

Market Research measures

tend to have smaller correlations

Negative correlation

Product price & market share

Positive correlation

-0.7 0.7

Correlation

Correlation


13/86


0

2

4

6

8

10

0 2 4 6 8 10

CommitmenttoC

ompanyX

Suppliers Frequency of Visit

Correlation measures Linearrelationships

Correlation of 0.17 is low, but there is visibleassociation between visitation and commitment

Correlation

Correlation


14/86


Associations (Correlations) andRelationships (Regression)

Perfect linear relationship, y = 2x + 1 all points lie on the straight line

gradient=2, intercept=1 Not seen in Market Research eg electricity bill. Total costs

=fixed costs + variable costs Y is the independent variable and X the independent (or

explanatory) variable

Approximate linear relationship y = 3.5x - 3.3 all points lie close to the line

gradient=3.5, intercept=-3.3 Line is a good fit (97%)

Approximate non linear relationship y = ln(x) or y=sqrt(x) all points lie on the curve gradient=variable, intercept=0 Imperfect non linear relationships Examples price and

volume

0

2

4

6

8

10

12

14

16

18

0 1 2 3 4 5 6 7 8 9

x start (Independent)

y

end

(Dependent)

0

5

10

15

20

25

30

0 1 2 3 4 5 6 7 8 9

x independent

yindependent

Volume & Price

0

5000

10000

15000

2000025000

30000

35000

40000

45000

1 1.1 1.2 1.3 1.4 1.5

Correlation

Correlation


15/86


Factor AnalysisAnalysis of Interdependence:

for data reduction and the discovery of underlying themes inthe data


16/86


Factor Analysis (FA)

Factor analysis tries to simplify attitudinal data by

providing an alternative way of looking at it? What are the main underlying themes in the data?? Which perceptions are related?

FA is based on analysing correlation matrix of attributesand aims to identify questions that measure, what

respondents see as, similar or related concepts

Uses Use FA to reduce number of questions asked in future research

waves

Use factors with other techniques (eg regression and clusteranalyses) to analyse data more successfully with uncorrelateddata

FAFA


17/86


Factor Analysis: Example 1

Customers asked to rate bus travel on a number of attributes on a 10 pointscale: 1 = Doesnt describe bus travel at all 10 = Totally describes bustravel

Relaxed Friendly Nervous Tolerate it Easy Interesting Uncertain Waste of time

Which statements did they rate similarly? ie which statements are correlated? common themes in the data

FAFA


18/86


Factor Analysis: Example 1 Statements

Correlations Grouped by

Factors

Q1 - Relaxed Q2 - Friendly Q3 - Nervous Q4 - Tolerate itQ5 Easy Q6 - Interesting Q7 - Uncertain Q8 - Waste of time

Q1 - Relaxed 1 0.59 -0.16 0.24 0.55 0.49 -0.13 -0.19

Q2 - Friendly 0.59 1 -0.14 0.24 0.52 0.54 -0.06 -0.15

Q3 - Nervous -0.16 -0.14 1 0.02 -0.18 -0.06 0.33 0.29

Q4 - Tolerate it 0.24 0.24 0.02 1 0.23 0.11 0.10 0.03

Q5 - Easy 0.55 0.52 -0.18 0.23 1 0.39 -0.16 -0.25

Q6 - Interesting 0.49 0.54 -0.06 0.11 0.39 1 0.02 -0.11

Q7 - Uncertain -0.13 -0.06 0.33 0.10 -0.16 0.02 1 0.32

Q8 - Waste of time -0.19 -0.15 0.29 0.03 -0.25 -0.11 0.32 1

FAFA


19/86


Factor Analysis: Example 1 Component Matrix

Component

1 2

Q2 Friendly 0.823

Q1 Relaxed 0.803 -0.186

Q6 Interesting 0.732

Q5 Easy 0.725 -0.265

Q4 - Tolerate it 0.456 0.253

Q7 Uncertain 0.767

Q3 Nervous 0.697

Q8 - Waste of time -0.144 0.691

Correlation between statementsand factor

First four statement load mainlyon first factor Positive bus travel

Other 4 load on second factor Negative about bus travel

Tolerate it loads on both

FAFA


20/86


Factor Analysis: Example 1 Component Matrix

Component

1 2

Q2 Friendly 0.82

Q1 Relaxed 0.80

Q6 Interesting 0.73

Q5 Easy 0.72

Q4 - Tolerate it 0.45

Q7 Uncertain 0.77

Q3 Nervous 0.70

Q8 - Waste of time 0.70

Correlation between statementsand factor

First four statement load mainlyon first factor Positive bus travel

Other 4 load on second factor Negative about bus travel

Tolerate it loads on both

FAFA


21/86


Factor Analysis: Example 1 Statements(reordered)

CorrelationsGrouped by Factors

Q1 - Relaxed Q2 - Friendly Q5 - Easy Q6 - Interesting Q4 - Tolerate itQ3 - Nervous Q7 - Uncertain Q8 - Waste of time

Q1 - Relaxed 1 0.59 0.55 0.49 0.24 -0.16 -0.13 -0.19

Q2 - Friendly 0.59 1 0.52 0.54 0.24 -0.14 -0.06 -0.15

Q5 - Easy 0.55 0.52 1 0.39 0.23 -0.18 -0.16 -0.25

Q6 - Interesting 0.49 0.54 0.39 1 0.11 -0.06 0.02 -0.11

Q4 - Tolerate it 0.24 0.24 0.23 0.11 1 0.02 0.10 0.03

Q3 - Nervous -0.16 -0.14 -0.18 -0.06 0.02 1 0.33 0.29

Q7 - Uncertain -0.13 -0.06 -0.16 0.02 0.10 0.33 1 0.32

Q8 - Waste of time -0.19 -0.15 -0.25 -0.11 0.03 0.29 0.32 1

FAFA


22/86


FA: Example 2, Rate 5 Insurance providers on 11Attributes

Brand A Brand B Brand C Brand D Brand E

A reputable insurance provider

Offers wide range of products and services to suitdifferent needs

Progressive and provides innovative insurancesolutions

Offers value-for-money products and services

Has strong working relationships with its

distributors/intermediaries

Global insurance provider

Established local insurance provider

One of the insurance providers that I would firstrecommend to my

customers

Has expertise in providing insurance solutions

An insurance provider with financial strength

An insurance provider I can trust

FAFA


23/86


FA: Example 2, How much variance do the factors explain?

Total Variance Explained

Initial EigenvaluesExtraction Sums ofSquared Loadings

Rotation Sums of SquaredLoadings

Component

Total% of

VarianceCumulative

% Total% of

VarianceCumulative

% Total% of

VarianceCumulative

%

1 5.459 49.628 49.628 5.459 49.628 49.628 2.894 26.312 26.312

2 1.249 11.359 60.986 1.249 11.359 60.986 2.634 23.948 50.260

3 .900 8.179 69.165 .900 8.179 69.165 2.080 18.905 69.165

4 .830 7.546 76.711

5 .631 5.736 82.448

6 .478 4.348 86.795

7 .431 3.917 90.713

8 .353 3.208 93.921

9 .295 2.682 96.603

10 .204 1.850 98.453

11 .170 1.547 100.000

Extraction Method: Principal Component Analysis.

Run FA and examine how much of the totalvariation in the data is explained by the factors The factors should explain at least 2/3 of thevariance. In these data, the first three factorsexplain 69% of the variable.

FAFA


24/86


FA: Example 2: Identifying factors from the Factorloadings

Rotated Component Matrix(a)

Component

1 2 3

Offers value-for-money products and services .865 .257 -.006

Offers wide range of products and services to suit different needs .836 .101 .192

Progressive and provides innovative insurance solutions .741 .197 .432

Has expertise in providing insurance solutions .657 .326 .267

A reputable insurance provider/company .251 .849 .086

An insurance company I can trust .187 .809 .208

Global insurance company .425 .593 .283

An insurance company with financial strength .074 .575 .458One of the insurance companies that I would first recommend to my customers .172 .086 .821

Has strong working relationships with its distributors/intermediaries .200 .342 .689

Established local insurance company .334 .481 .543

Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization.

Review factor loadings to decipher thefactors. The factor loadings are thecorrelations between the factor and theattribute.

Each attribute belongs tothe factor it is most highly

correlated with

FAFA


25/86


Example (Identifying factors)

Rotated Component Matrix(a)

Component

1 2 3

Offers value-for-money products and services .865 .257 -.006

Offers wide range of products and services to suit different needs .836 .101 .192

Progressive and provides innovative insurance solutions .741 .197 .432

Has expertise in providing insurance solutions .657 .326 .267

A reputable insurance provider/company .251 .849 .086

An insurance company I can trust .187 .809 .208

Global insurance company .425 .593 .283

An insurance company with financial strength .074 .575 .458One of the insurance companies that I would first recommend to my customers .172 .086 .821

Has strong working relationships with its distributors/intermediaries .200 .342 .689

Established local insurance company .334 .481 .543

Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization.

Factor 1:

Practical

solutions

Factor 3:

Distribution/

established

Factor 2:

Reputation

A three factor solution is selected for these data:1. Practical solutions2. Reputation3. Distribution/how well established

FAFA


26/86


Factor analysis considerations

Choosing the number of factors is an art, as much as a science Usual practice is to run several alternative analyses

Researcher and analysts collaborative judgment are important, to generate asolution that provides a plausible explanation and interpretation of the factors

Must achieve a balance between, one the one hand, having enoughfactors to explain the variation in the original data satisfactorily and, onthe other, not having so many factors that little or no data reduction hadbeen achieved.

Look for at least 65-70%+ with scale data, but 50+% with binary How big a sample is needed?

The larger the sample size, the more accurately we can estimate thecorrelations between questions and the more repeatable the analysis will be

A sample of 400 or more should provide a stable factor analysis

Minimum sample size of c200?

What types of scales work best? Preferably interval data (5 or 7 point Likert Agree/disagree scale is actually

ordinal data but is treated as interval) as the correlations estimated better

Binary (yes/no) variables often have a lower correlation

FAFA


27/86

[Topic of Presentation] December 6, 2010 Confidential & ProprietaryCopyright 2007 The Nielsen Company

Factor Analysis - Summary

Summarises large amounts of

data Identifies patterns easily that can

be hard to find

By basing factors on data

patterns, analysis based on

actual results, notpreconceptions or questionnaire

issues

Used in conjunction with MLR

But....

All variability in data not usually

accounted for in factor analysis Factors can be hard to interpret

- represent many measures

Factors depend on data, and

can differ for different sets of

data

FAFA


28/86


RegressionQuantifies the of the relationship between a dependent

variables and some explanatory independent variablesAnalyst specifies the nature of the relationship, ie which are

the dependent and independent variables

MM


29/86


Regression

Simple (bivariate) Regression The starting point for multiple regression

Bivariate regression is the same analyses as finding correlationbetween independent and dependent variable

Multiple Linear Regression

Several Independent variable, but still only one dependent

Many other non-linear forms not covered today Logistic, Generalised Linear Models etc

These types of regression are for different types of data, eg

choice

MLRMLR

MM


30/86


Sales by Advertising costs

05

10

15

20

25

30

0 25 50 75 100 125

Advertising Spend

Sales

Value

Simple Linear Regression, Example 1

Line of best fit: Y = 1.8 + 2.15*X

Sales value = constant + multiple of advertising expenditure

Simple linear regression hasonly one independent variable

Model fit from R2 = 0.975

R2 indicates the proportionof the total variation in thedependent variableexplained by theindependent variable

MLRMLR

Y

X

MM


31/86


Simple Linear Regression, Example 2

Brand Equity - Brand Share Relationship

y = 0.118x + 0.485

R2

= 0.800

1

2

34

5

6

7

8

0 10 20 30 40 50

Brand Share (val)

Brand

Eq

uityIndex

MLRMLR

MM


32/86


Multiple Linear Regression (MLR): MultipleIndependent variables (Xs)

We are interested in the causes of variation in the response toa dependent variable (eg what causes an increase/decrease insales/ratings)

There will be many variables in a survey which can beregarded as possible causes/predictors of a dependent

variable (eg Money spent on advertising, value for money etc) In statistics speak these are called IndependentorExplanatory

variables Multiple Linear Regression uses correlation as its building bock

to establish the association between Y and Xs

MLRMLR

MM


33/86


ML Regression: Dependent variable (Y)

The dependent variable Y in a regression will be a KeyPerformance Indicator (KPI)

? What are the key drivers of customer satisfaction? Or what

are the biggest influencers of brand equity in the market?

From a questionnaire we maybe interested in one variable inparticular eg purchase intention, likelihood to recommend,

overall satisfaction, the amount of sales of a product, an overall

rating of service

When this type of variable represents the key interest within a

survey, Regression refers to this as the Dependent variable

MLRMLR

Working through an example: drivers of overall


34/86


Working through an example: drivers of overallsatisfaction with insurance provider Brand A

We want to know what drives customers overall satisfaction

towards Brand A (insurance provider)

Having grouped the list of 11 attributes into factors (see section onFactor analysis), we can then use the factors as independentvariables for the regression analysis

We then build a regression model with the factors as drivers, andoverall satisfaction as the dependent variable Now work through the main steps involved, identifying the key

elements to review

MLR: Example 1: What is the relative importance of

MM


35/86


MLR: Example 1: What is the relative importance ofthese three factors in driving customer satisfaction?

Q58 ASK ALL XXX CHANNEL (Q1 CODED 1/2/3)Read list

Overall how satisfied are you with XXX as a life insurance company as a whole? Pleaserate on a 5 point scale, where "1" is "Very Dissatisfied" and "5" is Very Satisfied", areyou ...... (READ LIST) [SA]

Code(3364)

Route

1 Very dissatisfied 1

2 2

3 3

4 4

5 Very Satisfied 5DK/Can't say (Do not read out) 6

Factor 1: Practical solutions

Factor 2: Reputation

Factor 3: Distribution/ established

Satisfaction = thedependent variable

The 3 independentvariables

MLRMLR

Example: check how well the regression model fits theMM


36/86


Example: check how well the regression model fits thedata, using R2

R-square (R2) is an overall measure of how well the model (the regressionequation) explains the variance in the data

R2 is always between 0 and 1: An R2 value of 0.222 means it explains 22% of the variance in the data

The bigger, the R2 value, the better

An acceptable level forR2depends on the research setting, but low ones areaccepted in the market research industry. But preferably at least 0.3 andhigher

Use the Adjusted R2 which takes account of the sample size and the no. ofindependent variables. Often there is not a large difference between this and

the R2

MLRMLR

M R E l 1 SPSS O f M R MM


37/86


MLR: Example 1, SPSS Output from MLR

Unstandardized Coefficients Standardized Coefficients

ModelB Std. Error

Beta t Sig.

(Constant) 3.640 .085 42.664 .000

REGR factor score 1 .235 .086 .355 2.727 .009

REGR factor score 2 .062 .086 .093 .716 .4781

REGR factor score 3 .196 .086 .296 2.276 .028

a Dependent Variable: Q58. Overall how satisfied are you with XXX as a life insurance company as a w

Look at the table ofstandardised coefficients (beta scores). These are

the weights ( i) of the model

The Beta scores show the extent to which the independent variable

fluctuates with the dependent variable: The bigger the Beta scores, the greater their impact (ie. The more they

fluctuate with satisfaction) The implication is that these are more important attributes, because they are

the ones that are moving when satisfaction levels change

MLRMLR

MM


38/86


MLR Example 1: Model for Insurance provider A

Factor 2: Reputation*12%

Factor 2: Reputation*12%

Factor 3: Distribution/Established40%

Factor 3: Distribution/Established40%SatisfactionSatisfaction

Factor 1: Practical solutions48%

Factor 1: Practical solutions48%

* This driver is not a significant

contributor to the model

Key Drivers and % Impact on

Satisfaction

R2 = 0.22, which is low

for this type of

customer analysis

MLRMLR

MLR: Example 2 Drivers of Customer retention for an MM


39/86


Customer

retention

MLR: Example 2, Drivers of Customer retention for aninsurance company

Prompt personal service Resolve complaints quickly

Friendly and helpful Processing claims with empathy

Follow-up after complaint Easy to contact

Customer service

0.17

Global networkSafe and financially secure

Company image0.22

Setting ongoing expectations Range of options Knowledgeable

Acting in your best interests Friendly and helpful

Advisor performance0.28

Competitive rates of return Flexible products

Medical and life better value Fees and charges clear

Written documents

Product features0.18

More interested in profit All companies are the same

Industry image0.06

Awareness0.09

MLRMLR

R2

=0.58

MLR: Example 3 Critical Improvement Plot using MLR forMM


40/86


MLR: Example 3, Critical Improvement Plot using MLR forimportance, mean scores from performance

I

M

P

O

RT

A

N

C

E

P E R F O R M A N C E

HIGHLOW

H

I

G

H

L

O

W

* Product hard to use

* Customer Focus

* Overall Quality

* Emergency

orderingResponsive Rep *

* Delivery time

MLRMLR

MML


41/86


Multiple Lineear Regression Summary Linear Regression

eg Key Driver analysis

usually based on attitudinal data The relationship is linear(ie a

straight line can describe therelationship) and is additive innature

Based on correlation

Use model fit R2(adjusted) Provides Importance Scores

Used in eQ and Winning Brands

Not suitable for all data types,categorical or choice data

Can get multiple-collinearity(overlap) between theindependent variables which maydiscredit the analysis.

0

5

10

15

20

25

30

0 1 2 3 4 5 6 7 8 9

x independent

yindependent

MLRMLR

Multiple Regression Model:

Y = c + b1x1 + b2x2 + b3x3 + ..+ e


42/86


Correspondence Analysis and Perceptual

MappingCorrespondence analysis provides a visual summary ofbrand and attribute survey data

What is Correspondence Analysis(CA)?MaMa


43/86


What is Correspondence Analysis(CA)? Correspondence analysis is a technique for summarizing large tables

of data in terms of a visual map

CA analyses respondents perceptions of the similarity or dissimilarityof certain brands, products and services across a range of attributes

Maps present simple graphical summaries of a market: for example: Brand positioning: the relationship between brands and attributes

The relationship between current brand positioning and the idealpositioning

Image ratings by brand users, segments, etc

Maps are generated via BrandMap, an excel add-on

Research Questions Answered? What attributes do consumers associate my brand with

? What are my brands / competitors strengths and weaknesses maps present results of cross-tabs or count data visually need to consider the absolute scores and relative scores in explaining the

research findings

Mapping

Mapping

MM


44/86


Data Table: Cereal Brands Image Data

COCO POPS FRUITY BIX KELLOGGS CORNFLAKES

KELLOGGS RICEBUBBLES

NUTRI-GRAIN VITA BRITS WEET-BIX WEETBIXCRUNCH

MILO

High in fibre 3% 11% 11% 4% 19% 41% 73% 2% 1%

Good source of energy 11% 12% 26% 12% 46% 34% 63% 2% 6%

Most nutritious breakfast 2% 8% 17% 5% 19% 29% 65% 1% 1%

Meets my familys needs 14% 8% 36% 17% 25% 21% 54% 1% 3%

Australian owned & made 6% 5% 19% 10% 11% 18% 53% 2% 2%

Children like the taste 69% 12% 23% 34% 32% 8% 22% 1% 8%

Good for kids 10% 12% 31% 19% 22% 33% 66% 2% 3%

Good value for money 8% 4% 35% 16% 12% 23% 60% 1% 1%

Like the taste 37% 12% 43% 26% 38% 19% 47% 2% 5%

Meets my needs 11% 7% 31% 13% 22% 20% 53% 1% 3%

Low in sugar 2% 4% 24% 13% 8% 37% 70% 1% 0%

Convenient 33% 17% 49% 33% 36% 29% 61% 2% 6%

My kids want it 45% 5% 14% 21% 23% 4% 17% 1% 5%

Everyone eats it 18% 3% 48% 19% 20% 11% 46% 1% 3%

A brand I trust 22% 10% 53% 31% 29% 26% 64% 2% 4%

Number 1 cereal brand 6% 1% 33% 7% 9% 4% 28% 1% 1%

Mapping

Mapping

MaMa


45/86


Input for Correspondence Maps

Attitudinal data are most common Brand association grids are a typical type of input

Anything with absent / present type scores is appropriate (eg. Yes

associate that brand with that attribute, or no dont associate it) Tables of either percentages or raw numbers are acceptable Means can be used

Whether based on means, or percentages, correspondence maps

usually provide similar results. Often maps are just based on

percentage data Important to note that Correspondence Analysis is based on

aggregated, not individual level, data unlike FA and MLR

Mapping

Mapping


46/86


Points to consider withCorrespondence analysis

What is the minimum number of attributes? This is subjective, but a map of data with fewer than four brands

(columns) or 8 attributes (rows) may be relatively uninformative

Sample size issues are less critical than in segmentationstudies, as analysis has a qualitative feel about it But a sample size of between 200-400 would be a minimum

threshold

Care is needed with interpretation Overplaying weak relationships

Underplaying strong relationships

Using overly precise language in describing the map

MM


47/86


High in fibre

Good source of energy

Data Table: Cereal Brands Image DataMapping

Mapping

Correspondence Map: Example 1 MM


48/86


Correspondence Map: Example 1 Mapping

Mapping

MM


49/86


Interpreting the Map

Brands that are close to each other are seen to have similar

profiles in the eyes of the consumer

Brands are located next to attributes which are theirgreatest

relative strength(ie consumers feel that most characterizes the

brand)

Attributes that differentiate the brands are close to the edges.

Attributes that do not discriminate (i.e. could be considered are

generic to the category) are located near the centre of the map

The axes also have meaning the horizontal is more important

than the vertical. Thus, the position of a brand relative to the

horizontal axis is more important than its location vertically

Mapping

Mapping

I t ti C d MMM


50/86


Interpreting Correspondence Maps

Angle in d Correlation Level of Correlation/Assoc

0 1 Perfect +ve

15 0.97 +ve Correlation



60 0.5 Some +ve

75 0.26 Small +ve

90 0 No association

105 -0.26 Small oppostite -ve

120 -0.5 Some oppostite -ve

135 -0.71 -ve Correlation



180 -1 Perfect opposite v

Distance from the origin to the brand or attribute: Brandsfurthest from the origin, particularly horizontally (east or

west), are more distinct than brands nearer the middle ofthe map. Similarly for attributes.

Relationships between brands and attributes: The smallerthe angle between a brand and an attribute the more thatattribute applies to that brand. Brands that are 180degrees apart have the opposite positioning to each other.Brands at right angles are simply different or uncorrelated.

Attributes that are at right angles to a brand have noassociation with that brand.

Measuring the association between points on a map: It ishelpful to think of the visual measure of associationbetween brands or attributes (ie the angle between thepair in question) in quantitative terms as the correlation

between the pair.

Mapping

Mapping

C d M UK ST D t E l 2MapMap


51/86


Correspondence Map: UK ST Data Example 2Mapping

Mapping

C o n v e n ie n t to g e t to

S ta f f p ro v id e g o o d s e

F o o d a n d G ro c e r ie s a Correspondence Map: UK ST Data Example 2

MM


52/86


Correspondence Map: UK ST Data Example 2Mapping

Mapping

D t T bl C l B d I D tMM


53/86


Data Table: Cereal Brands Image Data

COCO POPS FRUITY BIX KELLOGGS CORNFLAKES

KELLOGGS RICEBUBBLES

NUTRI-GRAIN VITA BRITS WEET-BIX WEETBIXCRUNCH

MILO

High in fibre 3% 11% 11% 4% 19% 41% 73% 2% 1%

Good source of energy 11% 12% 26% 12% 46% 34% 63% 2% 6%

Most nutritious breakfast 2% 8% 17% 5% 19% 29% 65% 1% 1%

Meets my familys needs 14% 8% 36% 17% 25% 21% 54% 1% 3%

Australian owned & made 6% 5% 19% 10% 11% 18% 53% 2% 2%

Children like the taste 69% 12% 23% 34% 32% 8% 22% 1% 8%

Good for kids 10% 12% 31% 19% 22% 33% 66% 2% 3%

Good value for money 8% 4% 35% 16% 12% 23% 60% 1% 1%

Like the taste 37% 12% 43% 26% 38% 19% 47% 2% 5%

Meets my needs 11% 7% 31% 13% 22% 20% 53% 1% 3%

Low in sugar 2% 4% 24% 13% 8% 37% 70% 1% 0%

Convenient 33% 17% 49% 33% 36% 29% 61% 2% 6%

My kids want it 45% 5% 14% 21% 23% 4% 17% 1% 5%

Everyone eats it 18% 3% 48% 19% 20% 11% 46% 1% 3%

A brand I trust 22% 10% 53% 31% 29% 26% 64% 2% 4%

Number 1 cereal brand 6% 1% 33% 7% 9% 4% 28% 1% 1%

Mapping

Mapping

Biplot: Example Cereals MM


54/86


Biplot: Example Cereals

Biplots use

absolutedata values

Mapping

Mapping

C d A l i S


55/86


Correspondence Analysis - Summary

CA.... Summarises large amount of

information from tables

succinctly and visually

Identifies relationships between

statements, between brands &between statements and brands

Removes halo effects of brands

as it is a relative analysis

Probably need to show absolute

scores as well

But....

CA can... Be misinterpreted - map

presented visually, highlights

relative strengths of brands

mean numbers from analysis

difficult to interpret Be hard to compare different

different maps - how different

they are?

Should be described in

qualitative, or passivelanguage...eg brands tends to

be or near to


56/86


Winning Brands ModellingThe Brand Equity Index (BEI)

The Brand Equity Model (BEM)

Wi i B d M d lli L i


57/86


Winning Brands Modelling: LearningObjectives & Agenda

ObjectivesReview Winning Brands outputs from MSCiReinforce understanding of Winning Brands and itsbenefits for clients and revisit factor, regression and

correspondence analysis in the WB context

Agenda

Review BEI Calculation & InterpretationReview BEM Image Analyses


58/86


What is Brand Equity?

The BEI Calculation Explained

Professor Kevin Keller defines brandequity as the differential effect that

knowledge about the brand has on the

consumer response to the marketing ofthat brand.

BEI explained: BEI measures emotional commitment


59/86


BEI explained: BEI measures emotional commitmentto brands but it is correlated with share

Brand Equity - Brand Share Relationship

y = 0.118x + 0.485

R2

= 0.800

1

2

3

4

5

6

7

8

0 10 20 30 40 50

Brand Share (val)

Brand

Equ

ityIndex

BEIBEI


60/86


Measuring BEI (1)

These key outcomes are each respondents

relationship with each brand for Favourite/2nd Favourite (for markets with fewer than five

brands) (Variable has different values for 1st favourite, 2nd favourite, and neither

favourite) Recommended

(Variable has two values, recommended or not recommended) Price Premium

(Six point scale)

BEIBEI

Measuring BEI (2)


61/86


Measuring BEI (2)

Run FA on the BEI outcome variables Results in weights for favrite, recmnd & premium

Favrite, recmnd & premium are correlated

eg more likely to recommend a brand that is 1st favourite andmore likely to pay price premium for favourite brand

Factor analysis creates one factor or main theme from

the correlated data EQUITY

BEIBEI


62/86


Measuring BEI (3)

Convert the Equity to BEI on the scale 0 to 10 Scale of 0-10 allows comparisons within and across

categories and over time Score of 0 corresponds to (Not Favourite, Not Recommended, Wouldnt buy it

at all)

Score of 10 corresponds to (1st

Favourite, Recommended, Pay whatever itcosts)

BEI scores are then averaged across brands and other

classificatory variables

BEIBEI


63/86


BEI Outputs by Brand & Subgroup

Step 1: Understand the Nature of the Task

Brand A Brand B Brand C

Age Count BEI Std Dev Count BEI Std Dev Count BEI Std Dev

1.00 16-19 years 165 6.5 3.407 165 3.7 3.159 165 1.8 1.828

2.00 20-24 years 155 6.0 3.452 155 3.3 3.002 155 1.4 1.653

3.00 25-29 years 122 4.2 3.499 122 3.9 3.559 122 1.3 1.535

4.00 30-39 years 130 5.5 3.501 130 3.6 3.235 130 1.2 1.354

BEIBEI

Interpreting Brand Equity


64/86


Interpreting Brand Equity

Normative Database

Interpreting BEI: What Does a Brands BEI Score mean? B


65/86


Only about 15% of brandscommand a brand equityscore of more than 3.0

About 35% are in therange 1.0 - 3.0

Majority of brands have

an equity score of lessthan 1.0

Source : ACNielsens Winning Brands normative database of over 2,000 cases

Strongbrands

Maximum score is 10,

Minimum Score 0.

Brand Equity Index

50%

35%

10%

5%

0% 10% 20% 30% 40% 50% 60%

Less than

1.0

1.0 - 3.0

3.1 - 5.0

5.0 andabove

Interpreting BEI: What Does a Brand s BEI Score mean?Normative Database

BEIBEI

Interpreting BEI:Category Brand BEI

Carbonated Beverages Coca-Cola (Regular) 4.0


66/86


Interpreting BEI:High Scoring Brands

The distribution of BEI scoresis skewed to 0.

Brand averages are close to 0,but even the strongest brandswould not score more than 7

g ( g )

Cigarettes Winfield 2.6

Cigarettes Benson & Hedges 2.4

Fresh White Milk Pura Fresh 2.5

Fresh White Milk Dairy Farmers Fresh 2.6

Packaged Bread Helgas 3.3

Instant Coffee Nescafe Blend 43 4.0

Instant Coffee Moccona Classic 4.7

Toilet Tissue Kleenex 3.8Toilet Tissue Sorbent 3.8

Chocolate Cadbury 6.7

Pet Food Whiskas Cat Food 2.8

Snacks (Chips) Smith's Crisps 3.6

Snacks (Chips) Kettle Chips 3.9

Toothpaste Colgate 6.9

Toothpaste Macleans 3.2

Canned Fish John West 5.0

Canned Fish Greenseas 4.6

Yo hurt Ski 4.0

BEIBEI

Use of Norm, for ... BEBE


67/86


,

1) Benchmarking against the best in the industry/category

against the best in the country against the best in the region

2) Key PerformanceIndicator BEI

Brand Leverage

4) Marketing Management Performance set KPIs for performance management

3) Monitor BrandPerformance on key indicators

Ultimate Objective:Ensure Success of Brand and Company Profitability

BEIBEI

BB


68/86


Interpreting Brand EquitySignificance Testing:

(1) Between Brands and(2) Over Time

BEIBEI

Significance Testing between Brands BB


69/86


Significance Testing between Brands

3.6

0.9

1.8

0.9

0

2

4

6

8

10

Brand A Brand B Brand C Brand D

Brand

EquityI

ndexS

core

Aheadof all other

brands

Significantlylower

than BrandA,

aheadof BrandsB

&D

Brand A

Brand C

Brand B

Brand D

BEIBEI

Significance Testing Across Subgroups or


70/86


Over Time

Significance Testing:Changes in BEI year on year by State Capital City


71/86


Changes in BEI year-on-year by State Capital City

Brand Sydney Melbourne Brisbane

Brand 1 Significant Change No Change No Change

Brand 2 Significant Change Significant Change Significant Change

Brand 3 No Change Significant Change No Change

Brand 4 No Change No Change No Change










BB


72/86


Understanding

What is Important to Consumers

Creating the Brand Equity Model

BEMBEM

Overview: Winning Brands Model BB


73/86


Overview: Winning Brands Model

Consideration

Attributes

Benefits

Attitudes

Awareness

BrandEquityIndex

Consumer Loyalty

PricePremium

What consumersdoor feelWhat consumersknow

BEMBEM

Overview: Two Steps to the BEM BB


74/86


Overview: Two Steps to the BEM

Factor Analysis to identify underlying themes Factor analysis identifies correlated questions (images)

Creates main factors (or themes) from individual questions

Multiple Regression to find the drivers of BEI Awareness, consideration & category-related themes versus BEI Regression coefficients identifies how much these measures are related to BEI

BEMBEM

Drivers: Example of BEM Drivers BB


75/86


Drivers: Example of BEM Drivers

Nutrition/Health

(14%)

Awareness

(16%)

BrandEquity Index

Consideration(18%)

TOTAL = 100%

Known Brand/Image

(53%)

R2 =55%

BEMBEM

BEM E l O t t D i & I BB


76/86


BEM Example Output Drivers & Images

Image Factor A brand for me

Tastes good

A brand that makes me feel good,etc

Health Factor

Made from whole soy beans No cholesterol

No lactose, etc

0 20 40 60

Awareness

Consider

Image

Health

% Contribution to BEI o

Attribute

BEMBEM

II


77/86


Brand Perceptions

Correlations with BEI

Perceptual MapsDistinctiveness Scores

ImageData

ImageData

Perceptions: Image Correlations with BEI IIm


78/86


p g

Sorted by

size

Sorted within

factors

Reported bybrand

A brand for me 0.82

A brand that makes me feel good 0.72

A brand I trust 0.71

A brand for everyday use 0.66

Tastes good 0.65

A leading brand 0.65

A brand I know is good for me 0.64

A brand that fits with my healthy lifestyle 0.64

Good value for money 0.61

All round good health 0.58

High in calcium 0.56

Natural 0.55

Good for your bones 0.52

No cholesterol 0.50

Not genetically modified 0.49

Made from whole soy beans 0.49

Australian Brand 0.48

No animal fat 0.47

No Lactose 0.47

Good for your heart 0.46

Good source of phytoestrogens 0.42

Contains antioxidants 0.37

ImageData

ImageData

Correspondence Map: UK ST Data Example 2Mappi

Mappi


79/86


p p p ppingpping

Perceptions: Distinctiveness Scores UK ST 07


80/86


Green better than average

Red worse than average

D is t ic t iv e n e s s S c o r e sC o n v e n ie n t t o g e t t o

S t a f f p r o v id e g o o d s e

F o o d a n d G r o c e r ie s a

E v e r t h in I n e e d in t h


81/86


MVA SummaryConclusions and final obervations

Summary of techniques covered today


82/86


Summary of techniques covered today

Provide graphical summary of brands positioning in relative or

absolute terms across a range of perceptions/images (Used in

WBs and ad hoc studies)

Correspondence

Analysis/Biplots and

Mapping

Used to: examine inter-relationships between variables, with the

aim of data reduction, or to identify underlying themes (eQ and

WBs); build Key performance indicators from survey data (eQ and

WBs)

Factor analysis

Used to: identify key drivers of performance (eQ); isolate factorsinfluencing bran equity (WBs); some forms of regression predict

share movements from price increases (PriceItRight, PIR)

Regression

Purpose in ResearchTechnique

MVA Summary: Classifying MVA techniques byrelationship e amined


83/86


relationship examined

Type of relationship being examined

r, Anderson Tatham, Black: Multivariate Data Analysis Prentice Hall

Interdependence

Identify structure of

interrelationships

How many

variables are being

predicted or

explained?

Dependence

Prediction of Dependent

variables by Other

independent variables

Is the structure ofrelationships

among.?

One dep.

variable in a

single

relationship

Several

dep.

Variables in

single

relationship

Multiple

relationship

s of dep.

and indep.

variables

Variable

s

Cases/

Respondents

Objects

MVA Summary:Interdependence Relationships


84/86


y p p

Interdependence

Identify structure of interrelationships

Is the structure of relationships among.?

Variables ObjectsCases/

Respondent

s

How are the

attributes

measured?

Metric

Factor

analysis

Nonmetric

Cluster

analysis

Multidimensional

scaling Correspondence

analysisr, Anderson Tatham, Black: Multivariate Data Analysis Prentice Hall

MVA Summary:Dependence Relationships


85/86


y p p

Dependence

Prediction of Dependent variables by Other

independent variables

How many variables are being predicted or

explained?

Several dep.

Variables in single

relationship

One dep. variable in

a single relationship

Multiple

relationships of

dep. and indep.

variablesWhat is the

measurement

scale of the dep.

Variables?

Metric Nonmetric

Multiple

regressionConjoint

analysis

Multiple

discriminant

analysis

Linear

probability

models

Structural

equation

modelling

Canonical

correlation

analysis withdummy variables

Multiple discriminant

analysis

MetricNonmetric

What is the

measurement

scale of the

predictor

variables?Nonmetri

cMetric

What is the

measurementscale of the

variables?

Canonical correlation

analysis

r, Anderson Tatham, Black: Multivariate Data Analysis Prentice Hall


86/86

Thank You &Any Questions Please?

mva_2008 india school rick loyd

Documents