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8/8/2019 Mva_2008 India School Rick Loyd
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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
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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
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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)
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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?
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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
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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
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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?
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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?
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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?
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CorrelationMeasure of linear association between two variables.
Must always be between -1 and +1
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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
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-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
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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
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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
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Factor AnalysisAnalysis of Interdependence:
for data reduction and the discovery of underlying themes inthe data
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Correspondence Analysis and Perceptual
MappingCorrespondence analysis provides a visual summary ofbrand and attribute survey data
What is Correspondence Analysis(CA)?MaMa
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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
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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
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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
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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
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High in fibre
Good source of energy
Data Table: Cereal Brands Image DataMapping
Mapping
Correspondence Map: Example 1 MM
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Correspondence Map: Example 1 Mapping
Mapping
MM
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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
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Interpreting Correspondence Maps
Angle in d Correlation Level of Correlation/Assoc
0 1 Perfect +ve
15 0.97 +ve Correlation
30 0.87 +ve Correlation
45 0.71 +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
150 -0.87 -ve Correlation
165 -0.97 -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
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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
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Correspondence Map: UK ST Data Example 2Mapping
Mapping
D t T bl C l B d I D tMM
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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
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Biplot: Example Cereals
Biplots use
absolutedata values
Mapping
Mapping
C d A l i S
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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
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Winning Brands ModellingThe Brand Equity Index (BEI)
The Brand Equity Model (BEM)
Wi i B d M d lli L i
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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
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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
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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
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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)
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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
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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
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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
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Interpreting Brand Equity
Normative Database
Interpreting BEI: What Does a Brands BEI Score mean? B
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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
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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
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,
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
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Interpreting Brand EquitySignificance Testing:
(1) Between Brands and(2) Over Time
BEIBEI
Significance Testing between Brands BB
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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
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Over Time
Significance Testing:Changes in BEI year on year by State Capital City
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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
Brand 5 No Change No Change No Change
Brand 6 No Change No Change No Change
Brand 7 No Change No Change No Change
Brand 8 Significant Change No Change No Change
Brand 9 No Change No Change No Change
Brand 10 Significant Change No Change No Change
Brand 11 Significant Change No Change No Change
Brand 12 No Change No Change No Change
Brand 13 No Change No Change No Change
BB
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Understanding
What is Important to Consumers
Creating the Brand Equity Model
BEMBEM
Overview: Winning Brands Model BB
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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
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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
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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
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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
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Brand Perceptions
Correlations with BEI
Perceptual MapsDistinctiveness Scores
ImageData
ImageData
Perceptions: Image Correlations with BEI IIm
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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
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p p p ppingpping
Perceptions: Distinctiveness Scores UK ST 07
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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
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MVA SummaryConclusions and final obervations
Summary of techniques covered today
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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
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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
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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
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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
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Thank You &Any Questions Please?