Brand Asset, BAV analysis, Clustering Brands

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

7. 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. 8
Brand Strategies
Positioning

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What is a Product?

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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%

17. Product Line
17

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

20. 20
Creating Perceptual Maps in R
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

21. Brand Asset Valuation
D3M

22. 22
Older Techniques for Brand Similarity
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
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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

25. BAV Database

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”

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