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Brand Analytics
Case Study on Brand Positioning
D3M
Understand your Industry
o Who are the key players
 Ownership structure (Illusion of Choice!)
 Market share of brands
 Price/quality tiers
 Generics/store brands
 Marketing Mix (Promotion/AD)
o Information sources
 Depends on the Industry (e.g. Comscore, IRI, Nielsen, IMS Health)
What we want
 Insights into consumer decision making
 Example: Why do we form loyalties?
 How do we decode the black box? Elicit
preferences/decision rules?
 Simply ask people (Stated preferences)
 Observe what people do and reverse engineer to derive
underlying preferences or mechanisms (Revealed
preference)
 Experimentation
Understand your Brand
Customers+ Competitors
o Some form of 80/20 analysis
o Who are the top customers?
 Demographics
 Location
 Behavior/Life style (what else do they buy, what
Magazine/Sports/TV shows)
o Competition
o Elasticity (Own & Cross)
o Brand perceptions
Approach 1: Competitive Analysis
using Revealed Preference Data
log 𝑞 𝐴 = 𝛽0𝐴 + 𝛽𝐴𝐴 log 𝑃𝐴 + 𝛽𝐴𝐵 log 𝑃𝐵 + 𝛽𝐴𝐶 log 𝑃𝐶 + 𝛽𝐴𝐷 log 𝑃 𝐷 + 𝜀 𝐴
Own price elasticity Cross price elasticities
Approach 2:
Elicit Brand Perceptions by asking questions
Stated Preference Data
Example: Brand Perceptions
Overall
S3
S2
S1
Poor_value
Avant_Garde
Successful
Economical
Common
Hi_prestige
Easy_Service
Roomy
Uncomfortable
Sporty
Interesting
Poorly_built
Unreliable
Quiet
Attractive
Mercury Capri
BMW 318i
Pontiac Firebird
Saab 900
Honda Prelude
Eagle Talon
Toyota Supra
Audi 90
Ford T-Bird
G20
8
Brand Strategies
Positioning
9
.
.
.
What is a Product?
10
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Engineering versus Perceptual Attributes
MPG Fuel Efficiency
Air Bags Safety
Conjoint MDS
11
Differentiation and Positioning
• Differentiation: “The creation of tangible or
intangible differences on one or two key
dimensions between a focal product and its
main competitors”
– How do retailers differentiate?
– How do airlines differentiate?
• Positioning: “The set of strategies that
firms develop and implement to ensure
that the differences occupy a distinct and
important position in the minds of
consumers”
– Example: Auto Rentals
– Positioning an issue:
CO2http://www.youtube.com/watch?v=7sGKvDNdJNA
12
Cola Wars
Cub/Omni Jewel TI
7 UP 10.9% 11.8% 9.1%
COKE 30.5% 35.3% 51.2%
PEPSI 28.9% 25.9% 15.4%
R C 10.9% 4.3% 2.2%
CRUSH 1.2% 2.7% 1.8%
DR PEPPER 3.5% 4.0% 3.5%
MOUNTAIN DEW 1.5% 2.0% 1.1%
DIET RITE 6.1% 5.6% 10.0%
SLICE 1.1% 1.6% 1.3%
SPRITE 2.6% 3.2% 3.7%
SUNKIST 2.7% 3.8% 0.7%
Market Shares Vary across geographies (even within US)
& across type of supermarkets within a small geographical locations
Market Shares in Chains in Chicago
“Point of Origin”
Good Academic work on this
(see Bronnenberg et al. 2012)
Positioning Product Line
17
18
Other Issues in Positioning
Me Too Positioning
Strong Positioning Activity
Managing Your Image
19
A good positioning strategy requires …
An understanding of the
dimensions along which the
consumer perceives the
product
Knowing how
competitors’ products are
perceived along these
dimensions
Identifying the gaps that
your product can fill
20
Creating Perceptual Maps
Overall
S3
S2
S1
Poor_value
Avant_Garde
Successful
Economical
Common
Hi_prestige
Easy_Service
Roomy
Uncomfortable
Sporty
Interesting
Poorly_built
Unreliable
Quiet
Attractive
Mercury Capri
BMW 318i
Pontiac Firebird
Saab 900
Honda Prelude
Eagle Talon
Toyota Supra
Audi 90
Ford T-Bird G20
Brand Asset Valuation
D3M
22
Brand Similarity
(Think about how data is being collected)
Please rate the following pairs of toothpaste brands on the basis
of their similarity (1 = very similar, 9 = very dissimilar).
Very Very
Similar Dissimilar
1. Aqua-Fresh vs Crest 1 2 3 4 5 6 7
2. Aqua-Fresh vs Colgate 1 2 3 4 5 6 7
…
45. Pepsodent vs Dentagard 1 2 3 4 5 6 7
Aqua-Fresh Crest Colgate Aim Gleem Macleans Ultra Brite Close-Up Pepsodent Dentagard
Aqua-Fresh
Crest 3
Colgate 2 1
Aim 4 2 2
Gleem 6 5 4 3
Macleans 5 5 4 4 3
Ultra Brite 6 6 6 5 3 3
Close-Up 6 6 6 6 2 3 2
Pepsodent 6 6 6 6 2 2 1 2
Dentagard 7 6 4 6 4 5 5 4 5
Average of which brand pairs are considered most (dis)similar?
23
Data on Attributes & Preference
Popular
with men
Popular
with
women
Good
Value
Heavy
Full
Bodied
Special
Occasion
On a
Budget
Bud 4 6 7 2 2 3 7
Beck’s 7 3 4 3 5 5 3
. . . . . . . .
. . . . . . . .
. . . . . . . .
Stroh’s 3 2 3 6 5 5 2
Respondent 1
Overall Rating
Bud 6
Beck’s 9
.
.
.
Stroh’s 3
Your overall rating for
each Beer:
1 2 3 4 5 6 7 8 9
Rating of
Brands on
different
attributes
24
Input to Factor Analysis
Vectors of attributes can be plotted based on factor loadings.
Individual brand’s location on the perceptual map is based on
factor scores.
Heavy Pop/Men Pop/Women Full Bodied Blue Collar Good Value Spec Occ
Beck's
Budweiser
Coors
Ratings of the brands on each attributes averaged
across All Respondents
Coors light
Heineken
Meister Brau
Michelob
Miller
Miller Lite
Stroh's
BAV Database
Brand “Personality”
(Click on Article to see the paper)
Comprehensive Data on Top 700 Brands in the US
(Click on Article to see the paper & download data)
Perceptual Attributes
High Correlation but Difficult to see/analyze
Conduct Factor analysis
V1 V2 V3 V4 V5 V20…..
Cluster
Analysis
(Group Subjects)
Factor
Analysis
(Group Variables)
Data
Interpreting the Output
We are not
capturing
several
attributes
well. These
are
somewhat
unique, not
correlated
with other
If we use 9
Factors
rather than
52 attributes
we capture
about 72%
of total
information
Factors are arranged in terms of proportion of variance explained
Factor Analyze the Data to Understand the Correlation Structure
Notice that some of the
variables that had high
“uniqueness” are not correlated
with the Factors. If these were
important in our context, we
will keep them as individual
variables.
Labels of Factors is Subjective
Factor 1: “Best Brand”
Factor 2: Innovative/Visionary
Factor 3: Prestigious
Factor 4: Fun/Friendly
Factor 5: Caring
Factor 6: Stylish
Factor 7: Different
Factor 8: Energetic
Factor 9: ??
Interpret The factors
 It is our job to interpret what these underlying
“factors/themes” are
 Go down each column and look for large positive or
negative numbers
 These are correlations between original variables and the
“Factors”
 Large numbers help us interpret what these underlying
Factors are
 Note that R has created 9 new variables “Scores”
The new Variables (Scores) are
(1) Standardized: They have mean of 0 and std. deviation of 1
(2) Uncorrelated with each other
Using New Variables
• Run a regression of “Brand Asset” on the 9 Factor
Model 1
(Intercept) 51.13 (0.24)***
Factor1 22.37 (0.24)***
Factor2 8.16 (0.25)***
Factor3 -1.82 (0.25)***
Factor4 5.38 (0.25)***
Factor5 2.87 (0.26)***
Factor6 -0.86 (0.25)***
Factor7 -4.90 (0.26)***
Factor8 1.98 (0.26)***
Factor9 -4.52 (0.27)***
R2 0.76
Adj. R2 0.76
Num. obs. 3669
***p < 0.001, **p < 0.01, *p < 0.05
Clustering Brands on Factor Scores
Segments 3 & 1 are composed of Best Brands
Perceptual Maps are Usually Made on Factor 1 & 2
Segmentation of Brands in BAV (2012Q1) data

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Brand Analytics

  • 1. Brand Analytics Case Study on Brand Positioning D3M
  • 2. Understand your Industry o Who are the key players  Ownership structure (Illusion of Choice!)  Market share of brands  Price/quality tiers  Generics/store brands  Marketing Mix (Promotion/AD) o Information sources  Depends on the Industry (e.g. Comscore, IRI, Nielsen, IMS Health)
  • 3. What we want  Insights into consumer decision making  Example: Why do we form loyalties?  How do we decode the black box? Elicit preferences/decision rules?  Simply ask people (Stated preferences)  Observe what people do and reverse engineer to derive underlying preferences or mechanisms (Revealed preference)  Experimentation
  • 4. Understand your Brand Customers+ Competitors o Some form of 80/20 analysis o Who are the top customers?  Demographics  Location  Behavior/Life style (what else do they buy, what Magazine/Sports/TV shows) o Competition o Elasticity (Own & Cross) o Brand perceptions
  • 5. Approach 1: Competitive Analysis using Revealed Preference Data log 𝑞 𝐴 = 𝛽0𝐴 + 𝛽𝐴𝐴 log 𝑃𝐴 + 𝛽𝐴𝐵 log 𝑃𝐵 + 𝛽𝐴𝐶 log 𝑃𝐶 + 𝛽𝐴𝐷 log 𝑃 𝐷 + 𝜀 𝐴 Own price elasticity Cross price elasticities
  • 6. Approach 2: Elicit Brand Perceptions by asking questions Stated Preference Data
  • 9. 9 . . . What is a Product?
  • 10. 10 . . . . . . . . . . . . . . . . . . . . Engineering versus Perceptual Attributes MPG Fuel Efficiency Air Bags Safety Conjoint MDS
  • 11. 11 Differentiation and Positioning • Differentiation: “The creation of tangible or intangible differences on one or two key dimensions between a focal product and its main competitors” – How do retailers differentiate? – How do airlines differentiate? • Positioning: “The set of strategies that firms develop and implement to ensure that the differences occupy a distinct and important position in the minds of consumers” – Example: Auto Rentals – Positioning an issue: CO2http://www.youtube.com/watch?v=7sGKvDNdJNA
  • 12. 12
  • 13. Cola Wars Cub/Omni Jewel TI 7 UP 10.9% 11.8% 9.1% COKE 30.5% 35.3% 51.2% PEPSI 28.9% 25.9% 15.4% R C 10.9% 4.3% 2.2% CRUSH 1.2% 2.7% 1.8% DR PEPPER 3.5% 4.0% 3.5% MOUNTAIN DEW 1.5% 2.0% 1.1% DIET RITE 6.1% 5.6% 10.0% SLICE 1.1% 1.6% 1.3% SPRITE 2.6% 3.2% 3.7% SUNKIST 2.7% 3.8% 0.7% Market Shares Vary across geographies (even within US) & across type of supermarkets within a small geographical locations Market Shares in Chains in Chicago
  • 14.
  • 15.
  • 16. “Point of Origin” Good Academic work on this (see Bronnenberg et al. 2012)
  • 18. 18 Other Issues in Positioning Me Too Positioning Strong Positioning Activity Managing Your Image
  • 19. 19 A good positioning strategy requires … An understanding of the dimensions along which the consumer perceives the product Knowing how competitors’ products are perceived along these dimensions Identifying the gaps that your product can fill
  • 22. 22 Brand Similarity (Think about how data is being collected) Please rate the following pairs of toothpaste brands on the basis of their similarity (1 = very similar, 9 = very dissimilar). Very Very Similar Dissimilar 1. Aqua-Fresh vs Crest 1 2 3 4 5 6 7 2. Aqua-Fresh vs Colgate 1 2 3 4 5 6 7 … 45. Pepsodent vs Dentagard 1 2 3 4 5 6 7 Aqua-Fresh Crest Colgate Aim Gleem Macleans Ultra Brite Close-Up Pepsodent Dentagard Aqua-Fresh Crest 3 Colgate 2 1 Aim 4 2 2 Gleem 6 5 4 3 Macleans 5 5 4 4 3 Ultra Brite 6 6 6 5 3 3 Close-Up 6 6 6 6 2 3 2 Pepsodent 6 6 6 6 2 2 1 2 Dentagard 7 6 4 6 4 5 5 4 5 Average of which brand pairs are considered most (dis)similar?
  • 23. 23 Data on Attributes & Preference Popular with men Popular with women Good Value Heavy Full Bodied Special Occasion On a Budget Bud 4 6 7 2 2 3 7 Beck’s 7 3 4 3 5 5 3 . . . . . . . . . . . . . . . . . . . . . . . . Stroh’s 3 2 3 6 5 5 2 Respondent 1 Overall Rating Bud 6 Beck’s 9 . . . Stroh’s 3 Your overall rating for each Beer: 1 2 3 4 5 6 7 8 9 Rating of Brands on different attributes
  • 24. 24 Input to Factor Analysis Vectors of attributes can be plotted based on factor loadings. Individual brand’s location on the perceptual map is based on factor scores. Heavy Pop/Men Pop/Women Full Bodied Blue Collar Good Value Spec Occ Beck's Budweiser Coors Ratings of the brands on each attributes averaged across All Respondents Coors light Heineken Meister Brau Michelob Miller Miller Lite Stroh's
  • 26. Brand “Personality” (Click on Article to see the paper)
  • 27. Comprehensive Data on Top 700 Brands in the US (Click on Article to see the paper & download data)
  • 28. Perceptual Attributes High Correlation but Difficult to see/analyze
  • 29. Conduct Factor analysis V1 V2 V3 V4 V5 V20….. Cluster Analysis (Group Subjects) Factor Analysis (Group Variables) Data
  • 30. Interpreting the Output We are not capturing several attributes well. These are somewhat unique, not correlated with other If we use 9 Factors rather than 52 attributes we capture about 72% of total information Factors are arranged in terms of proportion of variance explained
  • 31. Factor Analyze the Data to Understand the Correlation Structure Notice that some of the variables that had high “uniqueness” are not correlated with the Factors. If these were important in our context, we will keep them as individual variables. Labels of Factors is Subjective Factor 1: “Best Brand” Factor 2: Innovative/Visionary Factor 3: Prestigious Factor 4: Fun/Friendly Factor 5: Caring Factor 6: Stylish Factor 7: Different Factor 8: Energetic Factor 9: ??
  • 32. Interpret The factors  It is our job to interpret what these underlying “factors/themes” are  Go down each column and look for large positive or negative numbers  These are correlations between original variables and the “Factors”  Large numbers help us interpret what these underlying Factors are  Note that R has created 9 new variables “Scores”
  • 33.
  • 34. The new Variables (Scores) are (1) Standardized: They have mean of 0 and std. deviation of 1 (2) Uncorrelated with each other
  • 35. Using New Variables • Run a regression of “Brand Asset” on the 9 Factor Model 1 (Intercept) 51.13 (0.24)*** Factor1 22.37 (0.24)*** Factor2 8.16 (0.25)*** Factor3 -1.82 (0.25)*** Factor4 5.38 (0.25)*** Factor5 2.87 (0.26)*** Factor6 -0.86 (0.25)*** Factor7 -4.90 (0.26)*** Factor8 1.98 (0.26)*** Factor9 -4.52 (0.27)*** R2 0.76 Adj. R2 0.76 Num. obs. 3669 ***p < 0.001, **p < 0.01, *p < 0.05
  • 36. Clustering Brands on Factor Scores
  • 37. Segments 3 & 1 are composed of Best Brands
  • 38. Perceptual Maps are Usually Made on Factor 1 & 2
  • 39. Segmentation of Brands in BAV (2012Q1) data