Brand AnalyticsCase 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 BrandCustomers+ Competitors

o Some form of 80/20 analysiso 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

log𝑞𝐴=𝛽0 𝐴+𝛽𝐴𝐴 log 𝑃 𝐴+𝛽𝐴𝐵 log 𝑃𝐵+𝛽𝐴𝐶 log 𝑃𝐶+𝛽𝐴𝐷 log 𝑃𝐷+¿ 𝜀𝐴¿

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_ServiceRoomy

Uncomfortable

Sporty

Interesting

Poorly_built

Unreliable

Quiet

Attractive

Mercury Capri

BMW 318i

Pontiac Firebird

Saab 900

Honda PreludeEagle Talon

Toyota Supra

Audi 90

Ford T-BirdG20

8

Brand StrategiesPositioning

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: CO2

http://www.youtube.com/watch?v=7sGKvDNdJNA

12

Cola Wars

Cub/Omni Jewel TI7 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

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 in R

Overall

S3

S2

S1Poor_value

Avant_Garde

Successful

Economical

Common

Hi_prestige

Easy_Service

Roomy

UncomfortableSporty

Interesting

Poorly_built

Unreliable

Quiet

Attractive

Mercury Capri

BMW 318i

Pontiac Firebird

Saab 900

Honda PreludeEagle TalonToyota Supra

Audi 90

Ford T-Bird G20

Brand Asset Valuation

D3M

22

Older Techniques for Brand SimilarityPlease rate the following pairs of toothpaste brands on the basis of their similarity (1 = very similar, 9 = very dissimilar).

Very Very

Similar Dissimilar1. Aqua-Fresh vs Crest 1 2 3 4 5 6

72. 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 DentagardAqua-FreshCrest 3Colgate 2 1Aim 4 2 2Gleem 6 5 4 3Macleans 5 5 4 4 3Ultra Brite 6 6 6 5 3 3Close-Up 6 6 6 6 2 3 2Pepsodent 6 6 6 6 2 2 1 2Dentagard 7 6 4 6 4 5 5 4 5

Average of which brand pairs are considered most (dis)similar?

23

Data on Attributes & PreferencePopular

with men

Popular with

womenGood Value Heavy Full

BodiedSpecial Occasio

nOn a

Budget

Bud 4 6 7 2 2 3 7Beck’s 7 3 4 3 5 5 3. . . . . . . .. . . . . . . .. . . . . . . .Stroh’s 3 2 3 6 5 5 2

Respondent 1

Overall RatingBud 6Beck’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'sBudweiser

Coors Ratings of the brands on each attributes

averaged across All RespondentsCoors lightHeineken

Meister BrauMichelob

MillerMiller 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 AttributesHigh 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 SubjectiveFactor 1: “Best Brand”Factor 2: Innovative/VisionaryFactor 3: PrestigiousFactor 4: Fun/FriendlyFactor 5: CaringFactor 6: StylishFactor 7: DifferentFactor 8: EnergeticFactor 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 FactorModel 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 0.76Adj. R2 0.76Num. 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