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How Well Does Consumer-Based Brand
Equity Align with Sales-Based Brand
Equity and Marketing Mix Response?
Hannes Datta
Kusum L. Ailawadi
Harald J. van Heerde
12 December 2016
• Product preference and response to marketing effort a product enjoys
because of its brand versus if that same product did not have the brand (Keller 1993)
• Intangible asset → needs to be measured
What is Brand Equity?
vs.
Price: 3.50€Size: 500gFlavour: Raisins
Price: 3.50€Size: 500gFlavour: Raisins
Two Measures of Brand Equity
Sales-Based Brand Equity (SBBE):
Brand’s core attraction
e.g., Brand Equity Ten (Aaker 1991);CBBE Pyramid (Keller 2001);Brand Asset Valuator (Young & Rubicam)
Market share after removing contribution of marketing mix and attributes
Consumer-Based Brand Equity (CBBE):
Perceptual Measures
• Companies..
• spend millions of dollars each year to build brand equity
• reap its benefits in product-market and financial-market outcomes
• pay consulting companies substantial amounts to track, analyze, and value it
• Academics…• develop frameworks to define and measure brand equity
• study its origins, consequences
• …but have not yet assessed
• how well the different measurement approaches (CBBE vs SBBE) align
• whether strong consumer perceptions translate in
• equity in the market place and
• stronger marketing mix effectiveness
Brand Equity: Central theme in marketing
1. What is the association between the major dimensions of CBBE and SBBE?
2. Are there differences between categories in the CBBE-SBBE association?
3. What is the association between the major dimensions of CBBE and marketing mix effectiveness?
Research Questions
Outline of the approach
CBBE
Role of Brand in Category
IRI Marketing ScienceConsumer perceptions
(Brand Asset Valuator)
SBBE
Data: Consumer-Based Brand Equity
Source: Young & Rubicam’s Brand Asset Valuator (BAV)
KNOWLEDGE
ESTEEM
How appropriate is the brand to you?
An intimate understanding of the brand
How do you regard the brand?
A brand’s uniqueness and ability to adapt to future consumer needs
Data: Sales-Based Brand Equity (I)
Source: Symphony/IRI data provided by Bronnenberg, Kruger and Mela (2008)
Category No. of Brands
Beer 59
Carbonated Soft Drinks 27
Cigarettes 25
Coffee 30
Cold (RTE) Cereal 23
Deodorants 19
Disposable Diapers 6
Household Cleaners 15
Ketchup 5
Laundry Detergents 20
Margarine & Spreads 13
Mayonnaise 7
Category No. of Brands
Milk 19
Mustard 12
Peanut Butter 11
Frozen Pizza & Dinners 26
Razors & Blades 5
Salty Snacks 17
Shampoo 28
Soup 8
Pasta Sauce 15
Sugar Substitutes 10
Toilet Tissue 10
Toothpaste 15
Yogurt 16
TOTAL 441
• 25 FMCG categories• 441 brands• 2002-2011; 290 with CBBE measures
Sales-Based Brand Equity
Data: Sales-Based Brand Equity (II)
Total Market Share(based on
scanner data)
SBBE = market share after removing contribution of marketing mix and attributes
Physical attributes
Regular Price
Price promotions
Advertising
Distribution
Feature/Display
Brand A Brand B
Sales-Based Brand Equity
Physical attributes
Regular Price
Price promotions
Advertising
Distribution
Feature/Display
• Brand Intercept in Market Share Model
Estimating SBBE
Model requirements:• Logical consistency• Substitution effects• SBBE = Time-varying brand
intercept• Heterogeneous response
parameters• Dynamic advertising effects• Control for attributes• Control for seasonality• Endogeneity correction with
Gaussian Copulas (Park and Gupta 2012)
• Autocorrelation
Multinomial Logit Attraction Model:
MSbt =Abt
σj=1m Ajt
Abt
= exp ቆ
ቇ
y=1
Y
αby · DumYearty + βb1RegPricebt
+ βb2PriceIndexbt + βb3FDbt + βb4Distrbt + βb5AdStockbt
+ a,l
γalAttrbal + q
κbqQuarterqt + βb1RegPricebt∗
+ βb2PriceIndexbt∗ + βb3FDbt
∗ + βb4Distrbt∗ + βb5Advbt
∗ + εbt
Estimating SBBE
Model requirements:• Logical consistency• Substitution effects• SBBE = Time-varying brand
intercept• Heterogeneous response
parameters• Dynamic advertising effects• Control for attributes• Control for seasonality• Endogeneity correction with
Gaussian Copulas (Park and Gupta 2012)
• Autocorrelation
Multinomial Logit Attraction Model:
MSbt =Abt
σj=1m Ajt
Abt
= exp ቆ
ቇ
y=1
Y
αby · DumYearty + βb1RegPricebt
+ βb2PriceIndexbt + βb3FDbt + βb4Distrbt + βb5AdStockbt
+ a,l
γalAttrbal + q
κbqQuarterqt + βb1RegPricebt∗
+ βb2PriceIndexbt∗ + βb3FDbt
∗ + βb4Distrbt∗ + βb5Advbt
∗ + εbt
Estimating SBBE
Model requirements:• Logical consistency• Substitution effects• SBBE = Time-varying brand
intercept• Heterogeneous response
parameters• Dynamic advertising effects• Control for attributes• Control for seasonality• Endogeneity correction with
Gaussian Copulas (Park and Gupta 2012)
• Autocorrelation
Multinomial Logit Attraction Model:
MSbt =Abt
σj=1m Ajt
Abt
= exp ቆ
ቇ
y=1
Y
αby · DumYearty +βb1RegPricebt
+ βb2PriceIndexbt + βb3FDbt + βb4Distrbt + βb5AdStockbt
+ a,l
γalAttrbal + q
κbqQuarterqt + βb1RegPricebt∗
+ βb2PriceIndexbt∗ + βb3FDbt
∗ + βb4Distrbt∗ + βb5Advbt
∗ + εbt
Estimating SBBE
Model requirements:• Logical consistency• Substitution effects• SBBE = Time-varying brand
intercept• Heterogeneous response
parameters• Dynamic advertising effects• Control for attributes• Control for seasonality• Endogeneity correction with
Gaussian Copulas (Park and Gupta 2012)
• Autocorrelation
Multinomial Logit Attraction Model:
MSbt =Abt
σj=1m Ajt
Abt
= exp ቆ
ቇ
y=1
Y
αby · DumYearty +βb1RegPricebt
+ βb2PriceIndexbt + βb3FDbt + βb4Distrbt + βb5AdStockbt
+ a,l
γalAttrbal + q
κbqQuarterqt + βb1RegPricebt∗
+ βb2PriceIndexbt∗ + βb3FDbt
∗ + βb4Distrbt∗ + βb5Advbt
∗ + εbt
AdStockbt = λ AdStockb,t-1 + (1-λ) Advertisingbt
Estimating SBBE
Model requirements:• Logical consistency• Substitution effects• SBBE = Time-varying brand
intercept• Heterogeneous response
parameters• Dynamic advertising effects• Control for attributes• Control for seasonality• Endogeneity correction with
Gaussian Copulas (Park and Gupta 2012)
• Autocorrelation
Multinomial Logit Attraction Model:
MSbt =Abt
σj=1m Ajt
Abt
= exp ቆ
ቇ
y=1
Y
αby · DumYearty +βb1RegPricebt
+ βb2PriceIndexbt + βb3FDbt + βb4Distrbt + βb5AdStockbt
+ a,l
γalAttrbal + q
κbqQuarterqt + βb1RegPricebt∗
+ βb2PriceIndexbt∗ + βb3FDbt
∗ + βb4Distrbt∗ + βb5Advbt
∗ + εbt
Estimating SBBE
Model requirements:• Logical consistency• Substitution effects• SBBE = Time-varying brand
intercept• Heterogeneous response
parameters• Dynamic advertising effects• Control for attributes• Control for seasonality• Endogeneity correction with
Gaussian Copulas (Park and Gupta 2012)
• Autocorrelation
Multinomial Logit Attraction Model:
MSbt =Abt
σj=1m Ajt
Abt
= exp ቆ
ቇ
y=1
Y
αby · DumYearty +βb1RegPricebt
+ βb2PriceIndexbt + βb3FDbt + βb4Distrbt + βb5AdStockbt
+ a,l
γalAttrbal + q
κbqQuarterqt + βb1RegPricebt∗
+ βb2PriceIndexbt∗ + βb3FDbt
∗ + βb4Distrbt∗ + βb5Advbt
∗ + εbt
Xbt∗ = Φ−1 H Xbt , where Φ-1 = inverse CDF of standard normal,
H(·) = empirical cumulative distribution function of Xb.
Estimating SBBE
Model requirements:• Logical consistency• Substitution effects• SBBE = Time-varying brand
intercept• Heterogeneous response
parameters• Dynamic advertising effects• Control for attributes• Control for seasonality• Endogeneity correction with
Gaussian Copulas (Park and Gupta 2012)
• Autocorrelation
Multinomial Logit Attraction Model:
MSbt =Abt
σj=1m Ajt
Abt
= exp ቆ
ቇ
y=1
Y
αby · DumYearty +βb1RegPricebt
+ βb2PriceIndexbt + βb3FDbt + βb4Distrbt + βb5AdStockbt
+ a,l
γalAttrbal + q
κbqQuarterqt + βb1RegPricebt∗
+ βb2PriceIndexbt∗ + βb3FDbt
∗ + βb4Distrbt∗ + βb5Advbt
∗ + εbt
εbt = ρbεbt−1 + ubt
Results
DSMS 201617
• Relevance
RQ1: Association between CBBE and SBBE?
=.39 (p < .01)
• Esteem
RQ1: Association between CBBE and SBBE?
=.35 (p < .01)
• Knowledge
RQ1: Association between CBBE and SBBE?
=.53 (p < .01)
• Energized Differentiation
RQ1: Association between CBBE and SBBE?
=-.14 (p < .01)
• Measures a brand’s uniqueness and ability to
stand out from the competition, as well as its ability to adapt to and meet consumer needs in the future.
• Only CBBE pillar which correlates negatively (-0.14) with SBBE.
• High energized differentiation does not necessarily appeal to the masses.
• Upside: these brands charge higher prices
RQ1: The role of Energized Differentiation
RQ2: Category differences in CBBE-SBBE Association?
Pillars of CBBE
• PCA (89% variance explained)
• EnDif• Energized
Differentiation• RelStat (relevant stature)
• Relevance• Esteem• Knowledge
Relevance of Brand in Category
• Social Value• Hedonic Value• Functional Risk Reduction
• Information Cost Reduction
SBBE
RQ2: Category moderators (1/4)
• Social value
(Steenkamp and Geyskens 2014; measured
on Mturk with N = 752)
• “You can tell a lot about a person from the
brand of category X he or she buys”
• “The brand of category X a person buys says
something about who they are.”
Highest Social Demonstrance
1Beer 3.4
2Cigarettes 3.1
3Coffee 3.1
4Shampoo 2.8
5Carbonated Soft Drinks 2.7
…… …
21Margarine & Spreads 2.2
22Soup 2.2
23Mayonnaise 2.2
24Mustard 2.1
25Ketchup 2.1
Lowest Social Demonstrance
RQ2: Category moderators (2/4)
• Hedonic value
(Voss, Spannenberg, and Grohmann 2003;
measured on Mturk with N = 752)
• “Please rate category X on how not fun/fun it
is”
• “Please rate category X on how
unenjoyable/enjoyable it is”
Most Hedonic
1 Beer 6.0
2 Coffee 5.5
3 Carbonated Soft Drinks 5.3
4 Salty Snacks 5.2
5 Cold (RTE) Cereals 4.7
… …
21 Razors & Blades 2.6
22 Toilet Tissue 2.4
23 Lundary Detergents 2.4
24 Household Cleaners 2.2
25 Diapers 2.1
Least Hedonic
RQ2: Category moderators (3/4)
• Functional Risk
(Steenkamp & Geyskens 2014; measured on
Mturk with N = 752)
• “There is much to lose if you make the wrong
choice in category X.”
• ”In category X, there are large differences in
quality between the various products.”
Highest Functional Risk
1Diapers 3.7
2Razors & Blades 3.7
3Coffee 3.6
4Toilet Tissue 3.6
5Shampoo 3.6
…… …
21Milk 2.8
22Sugar Substitutes 2.8
23Margarine & Spreads 2.7
24Ketchup 2.6
25Mustard 2.5
Lowest Functional Risk
RQ2: Category moderators (4/4)
• Concentration (cf. information cost)
(calculated from the data)
• C4 = market share of four largest brands in the
category
Highest Concentration
1Ketchup 1.00
2Diapers 0.99
3Razors & Blades 0.99
4Soup 0.98
5Mayonnaise 0.93
…… …
21Carbonated Soft Drinks 0.56
22Deodorants 0.51
23Cold (RTE) Cereals 0.48
24Beer 0.47
25Frozen Pizza & Dinners 0.47
Lowest Concentration
RQ2: Differences between Categories in the CBBE-SBBE Association?
Independent Variable Effect on SBBE
Energized Differentiation -0.08*
Energized Diff. x Category Social Value -0.05
Energized Diff. x Category Hedonic 0.13**
Energized Diff. x Category Functional Risk -0.08
Energized Diff. x C4 0.72***
Relevant Stature 0.52***
Relevant Stature x Category Social Value 0.29**
Relevant Stature x Category Hedonic -0.09**
Relevant Stature x Category Functional Risk -0.01
Relevant Stature x C4 -0.50*
Secondary market -0.60***
Category Social Value -0.21
Hedonic 0.03
Category Functional Risk -0.08
C4 0.27
Constant 0.64
R-squared 0.47
Number of brands 290
Number of observations 2423*** p< 0.01; ** p< 0.05; * p<0.10
Relevant stature effect is weaker for more hedonic categories
Energized Differentiation effect is stronger for more hedonic categories
Energized Differentiation effect is stronger for more concentrated categories
Relevant stature effect is weaker for more concentrated categories
Relevant Stature effect is stronger for stronger social value categories
• Elasticity estimates
RQ3: Association between CBBE and marketing mix effectiveness?
Marketing Mix ParameterElasticity
estimate
90%-interval of
estimated
elasticities
Mean
Regular price elasticity-.79* [-2.74,.53]
Promotional price elasticity-2.59* [-5.64,-.45]
Feature/Display elasticity.02* [-.04,.19]
Distribution elasticity.40* [-.10,1.03]
Advertising elasticity.001* [-.02,.04]
* p<.1
RQ3: Association between CBBE and marketing mix effectiveness?
0.02
0.140.16
-0.19
0.07
-0.08 -0.09
0.08
0.03
0.08
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
|Regular PriceElasticity|
|Promotional PriceElasticity|
Feature/DisplayElasticity Distribution Elasticity Advertising Elasticity
*
******
***
* *
*** p< .01; ** p< .05; * p<.10
Overachieving versus underachieving brands
SBBE
CBBE
Overachievers
Underachievers
Brands which have higher SBBE than expected.
Brands which have lower SBBE than expected.
Top Brands: High CBBE and High SBBE
Overachievers: Higher SBBE than expectedbased on CBBE
Underachievers: Lower SBBE than expected based on CBBE
Strugglers: Low CBBE,low SBBE
Overachieving versus underachieving brands: Cereals
Overachievers
Higher on SBBE than expected based on
CBBE
The high SBBE is not mirrored by a high CBBE.
These brands experience an unusually low brand perception.
What is causing this unexpectedly low CBBE?
Sales-Based Brand Equity (SBBE)
Consumer-Based Brand Equity (CBBE)
Underachievers
Lower on SBBE than expected based on
CBBE
The medium CBBE is not mirrored by a medium SBBE.
Those who do not buy these brands have an unusually high
opinion of them.
What is stopping those with a medium opinion of these brands from making the
decision to purchase them?
Sales-Based Brand Equity (SBBE)
Consumer-Based Brand Equity (CBBE)
1. Overall, moderate association between CBBE and SBBE
2. Energized Differentiation is different
3. Association differs across categories in line with brand role
• Social Value, Hedonic Value, Information Cost Reduction
4. Nuanced association between CBBE and marketing response
• Price promotion and Feature/Display more effective for brands
high on Relevant Stature, not Energized Differentiation
• Distribution elasticities are smaller for brands high on Relevant Stature
• Advertising response is stronger for both dimensions
Key Takeaways
Do investments in CBBE pay of in terms of SBBE?
• High Relevant Stature → SBBE but far from perfect alignment
• Energized Differentiation no direct payoffs, even slightly negative
• Social demonstrance categories
• Utilitarian categories
• More competitive categories
• Hedonic categories
• Concentrated categories
Is CBBE a Marketing mix booster?
• Relevant Stature brands: focus on advertising & (non-) price promotions; less need to focus on distribution
• Energized Differentiation brands: focus on advertising; less on price promotions
Managerial Implications
Invest in Relevant Stature
Invest in Energized Differentiation
Managerial Implications
• Get in touch with us? h.datta@uvt.nl
Thanks!
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