brand evaluation and advertising effectswebuser.bus.umich.edu/jagadees/other/brand...†...

TitleIntroduction

Theoretical FrameworkResearch Context and Data

Brand EvaluationConclusion

Brand Evaluation and Advertising Effects- A Study of The Mobile Phone Industry11

Bo Huang

The Ross School of BusinessUniversity of Michigan

Nov. 2, 2007

1This paper has benefited greatly from the guidance of Prof. Francine Lafontaine, from the courses taught by

Prof. Patrik Bajari and Prof. Serena Ng, and from discussion with Ph D candidate Zhixi Wan. Special thanks arealso due to Profs. Katherine Terrell, Jan Svejnar, Jagadeesh Sivadasan, Valerie Suslow, Scott Masten, RejeevBatra, and Puneet Manchanda. All remaining errors are mine.

[email protected] Brand Evaluation and Advertising Effects

TitleIntroduction



Research ObjectivesMotivation/Literature ReviewIntuition

Introduction

• Research Objectives• Motivation/Literature Review• Intuition


TitleIntroduction




Research Objectives

• Develop a quantitative method of estimating firms’ monetary brandvalues under dynamic Bertrand competition, and apply the method tothe mobile phone industry in Italy.

• Extend the BLP’s framework by identifying the economic meaning of thelatent variable ξ in the utility/demand function of the discrete randomcoefficients logit model.

• Study the dynamics among firms’ advertising, brand and market shares.


TitleIntroduction




Motivation/Literature Review

• Estimating the monetary value of brands remains a challenging task of utmostimportance to business and academia. Two current approaches have theirlimitations:

◦ The subjective judgment approach is just subjective. For example, it is based onpeople’s scoring.

◦ The marketing costs approach may not reflect the true marketing effectiveness tothe brand. For example, its adverting may address the wrong segments and donot increase any brand value.

• Tobin’s q is a method reflecting some unmeasured or unrecorded assets of thecompany.

• BLP(1995) first model and estimate the latent variable ξ in the utility/demandfunction.Their major contribution rests on smartly solving endogeneity problemsand enabling more reasonable price elasticity calculation.

• Nevo (2000a, b, 2001) applies their approach to the cereal industry, and writes a"How-to" paper on this method.


TitleIntroduction




Intuition

• Apply the BLP method to estimate the latent variable ξ’s for all products by timeperiod (i.e. market by market).

• Obtain the brand level ξ’s by calculating the average/market-share-weighted ξ foreach brand in each time period.

• Put all competitors’ advertising, ξ’s and market share in a time series setting,and estimate a Structural Vector Autoregressive Model (SVAR) ⇒ 2nd structuralmodel taking into account competition dynamics.

• Recover unobserved marginal cost for each product under differentiatedproducts Bertrand competition.

• Manipulate a firm’s brand effects, recalculate the new scenario market shares forall competitors given all other variables constant, and calculate the firm’s profitchange (loss) ⇒ the brand monetary value for each period ⇒long-run brandvalue.


TitleIntroduction



Utility FunctionDemand FunctionBetrand Competition and Constant Marginal CostStructural Vector Autoregressive Model

Theoretical Framework

• Utility Function• Demand Function• Detrand Competition and Constant Marginal Cost• Structural Vector Autoregressive Model


TitleIntroduction




Utility FunctionAn individual’s utility function is defined as:

ln (uijt) = Xθ(i) + ξjt + εijt ≡ Uijt , (1)where X is a Matrix of product characteristics.

ξjt = ξbt + τ, (2)

where ξjt and ξbt are product and brand level latent variables, respectively. τ is error.The parameters of an individual i ’s utility function are:

θ(i) = θ̄ + ΠD(i) + Σν(i), ν(i) ∼ P0, D(i) ∼ PD , (3)

The utility of outside goods is normalized:

ln(u0) = 0, i.e. u0 = 1. (4)

Therefore, the log-linear utility function can be written as:

Uijt = δjt

(xjt , ξjt ; θ1

)+ µijt

(xjt , ξjt , ν(i), D(i); θ2

)+ εijt , (5)

δjt = xjt θ1 + ξjt ,

µijt = xjt

(ΠD(i) + Σν(i)

),

where δjt is the linear part and µijt the non-linear part of the log utility function.


TitleIntroduction




Demand Function

Conditional on ν(i) and D(i) and after integrating out over ε, which is assumedto have the Type I extremely value distribution, the conditional market share(i.e. the expected probability of individual i choosing product j) is:

sij(x , δ, ν(i), D(i); θ2) =e(δj +µj )

1 +∑Jt

q=1 e(δq+µq), (6)

where µ(.) contains corresponding ν(i) and D(i). The remaining two levels ofintegrals can be computed byMonte Carlo simulation as follows:

s?j (x , δ, P0, P̂D; θ2) =

1ns

∑

i

sij(x , δ, ν(i), D(i); θ2), (7)

where ns is the number of simulated individuals in a market, and ? indicates asimulation value.


TitleIntroduction




Betrand Competition and Constant Marginal CostA firm f maximizes its profit over its product portfolio, i.e. over all of itsproducts in a market, by setting product prices. The profit function of firm f is:

Πf =∑

j∈=f

(pj −mcj) Msj (x , ξ, P0, PD; θ) , (8)

where M is market size. There is a unique equilibrium that can be identifiedby solving the first order conditions for all products in a market:

sj (x , ξ, P0, PD; θ) +∑

r∈=f

(pr −mcr )∂sr (x , ξ, P0, PD; θ)

∂pj= 0. (9)

The marginal cost can be recovered as:

mc ≡ p −∆(p, x , ξ, P0, PD; θ)−1s (p, x , ξ, P0, PD; θ) , (10)

where ∆ is the matrix of ∂s/∂p.


TitleIntroduction




Structural Vector Autoregressive Model (SVAR)

The SVAR is set up to reflect the following points:

• Advertising is a function of its own lags and lagged market shares.

• Consumers’ willingness-to-pay for a brand, ξbt , is a function of its ownone-period lagged value and the contemporaneous advertising, i.e. allthe past advertising is absorbed in ξb,t−1.

• Market share is a function of its own lag, and contemporaneouswillingness-to-pay.


TitleIntroduction




Structural Vector Autoregressive Model (SVAR)Formally, the structural model is:

(Ψ0 −Ψ1L−Ψ2L2 −Ψ3L3)Yt = c + Σvt , (11)

i.e.

I0,11 0 0Ψ0,21 I0,22 0

0 Ψ0,32 I0,33

adb,tξb,tsb,t

=

adbξbsb

+

Ψ1,11 0 Ψ1,13Ψ1,21 Ψ1,22 Ψ1,23

0 Ψ1,32 Ψ1,33

adb,t−1ξb,t−1sb,t−1

+

Ψ2,11 0 0Ψ2,21 0 0

0 0 0


+

Ψ3,11 0 0Ψ3,21 0 0

0 0 0


+

I0,11 0 00 I0,22 00 0 I0,33

vad,tvξ,tvs,t

.

It can also been written as follows with the underlined reduced form model:

Ψ0(IK − Φ1L− Φ2L2 − Φ3L3)Yt = Ψ0ωt + Ψ0et ≡ c + Σvt (12)


TitleIntroduction



Italian Mobile Phone Industry and MarketData DescriptionData Processing

Research Context and Data

• Italian Mobile Phone Industry and Market• Data Description• Data Processing


TitleIntroduction




Italian Mobile Phone Industry and Market

• Italy is the 2nd largest European mobile phone market after Germany.Mobile phone penetration rate has been more than 100% for years.

• Competition is getting intensive all the time in both new productdevelopment and marketing.

• More importantly, mobile phone (handset) purchases are not bundledwith the mobile phone service contract in Italy. Different from bundledmarkets such as USA, its industry structure provide researcher withclear handset price.

• There are dozens of mobile phone brands in the Italian market. Themajor players are Nokia, Samsung, Motorola, Sony-Ericsson, Simensand LG. They are the study subjects in this paper.


TitleIntroduction




Data Description

I use four types of data:

• Monthly price and volume data of mobile phones in Italy from Jan. 2002to Dec. 2006.

• Self-collected product characteristics data from various Internet sourcessuch as the web sites of mobile phone manufacturers,www.gsmarena.com and other public web sites2.

• Self-collected product voting scores for design, feature, andperformance from www.gsmarena.com.

• Self-collected advertising proxy data from www.google.com betweenJan. 2001 and Dec. 2006.

2Special thanks to the Ross School of Business for financing the data collection, and to the excellent research

assistant work by Guiling Wang and Jun Chen.


TitleIntroduction




Data Processing

Price and volume data:

• I removed products whose agency-recorded market shares are lessthan 0.01%.

• Two products are removed because of obvious data input error (the unitprices are greater than Euro 2000).

• Another two products are removed because they are car phones, notbelonging to the mobile phone handset category.

Voting scores:

• If there are less than two missing data points, they are filled with theaverage of the adjacent available data points as the mobile phonemodels adjacent in a product series are very similar in theircharacteristics and design.


TitleIntroduction




Data Processing

Data Summary

• 60 time periods (markets)

• 6 brands

• 479 mobile phone models

• 7798 observations

• 2 voting scores

• 72 time-period Advertising data of all brands

• 9 + 6 phone characteristics incl. price


TitleIntroduction



"Design" and "Feature" as A Function of Phone CharacteristicsEfficient GMMBrand EffectsBrand Monetary Values

Brand Evaluation

• "Design" and "Feature" as A Function of PhoneCharacteristics

• Efficient GMM• Brand Effects• Brand Monetary Values


TitleIntroduction




"Design" and "Feature" as A Function of PhoneCharacteristics

• Design voting scores data enable me to control forintangible/hard-to-measure product characteristics.

• Both voting scores can be written as a function of exogenous productcharacteristics. In other words, their corresponding fitted values are thelinear combinations of those characteristics.

• Therefore those fitted values of design and feature voting scores can beused as independent variables for parsimonious estimation.

• As both design and feature scores are continuous on [1, 10], they alsoimprove the practicality of non-linear search of the GMM algorism.


TitleIntroduction




"Design" and "Feature" as A Function of PhoneCharacteristics

Table: Estimated Parameters of The Design and Feature Functions

Variable Design Coef. SE Feature Coef. SEConstant 11.744 1.094 3.512 1.292Form 0.190 0.042Extra display 0.155 0.055Lnlength −0.768 0.216Lnwidth 0.852 0.337Lnheight −0.378 0.115Email 0.137 0.062Internet 0.346 0.075 0.448 0.085Connectivity 0.157 0.034 0.297 0.044

Note: All p-values are less than 0.03.


TitleIntroduction




Efficient GMM

Table: Estimated Parameters of Utility Function

Variable 1st Stage Coef. 2nd Stage Coef. SE p-valueConstant −7.7884 0.1053 0.0000Price −1.0661 0.0023 0.0000Form 0.1367 0.0023 0.0000Extra display 0.1785 0.0011 0.0000Lnlength −1.2335 0.0098 0.0000Lnwidth 3.9008 0.0159 0.0000Lnheight 0.0602 0.0059 0.0000Email −0.1216 0.0012 0.0000Internet 0.2073 0.0069 0.0000Connectivity 0.4529 0.0069 0.0000v 0.464 0.4723 0.6923 0.2476Price ∗ v 0.2824 0.2857 0.0034 0.0000D̂esign ∗ v 0.6744 0.674 0.0251 0.0000F̂eature ∗ v 0.2691 0.2704 0.0261 0.0000GMM obj. value 781.2140 154.9452num. of simulation 200 200


0 10 20 30 40 50 60-4

-2

0

2

4

6

8

10

12

Time

Will

ingness-T

o-P

ay

Willingness-To-Pay For Different Brands

brand1brand2

brand3

brand4

brand5brand6

0 10 20 30 40 50 60-2

-1.5

-1

-0.5

0

0.5

1

1.5

Time

Will

ingness-T

o-P

ay

Demeaned Willingness-To-Pay For Different Brands

brand1

brand2

brand3

brand4

brand5

brand6

TitleIntroduction




ξ̂bt

• Recall ξ̂jt were calculated in a context in which each time period wasregarded as a market.

• However, we can see from the first figure that ξ̂bt exhibit clearly a timeseries correlation and a clear annual cycle, and that

• the relative positions of each brand remain relatively stable and changegradually.

• After demeaning the industry average of ξ̂jt , we can see a clearer trend,which accurately reflect the reality of the industry.

• Those two figures confirm that ξ̂bt do have a lot of information thatremains to be exploited.


TitleIntroduction




Brand Effects

Run SVAR estimation with three major brands out of six, and obtain thebrand effects after controlling the serial correlation and advertising effects.The brand effects, i.e. ξ’s are:

ξ1 = 1.3769ξ2 = 1.7306ξ3 = 1.8330

Next I lower the brand effects to their lower bound for each of those threefirms separately, and calculate the profit changes for all six firms.


0 10 20 30 40 50 600

2

4

6

8

10

12x 10

7

Time

Pro

fit, E

uro

s

Estimated Profits of Different Brands

0 10 20 30 40 50 60-6

-5

-4

-3

-2

-1

0

1

2

3

4x 10

7

Time

Pro

fit, E

uro

s

Profit Changes When The Effects of Brand 1 Are Lowered

brand1brand2

brand3

brand4

brand5brand6

0 10 20 30 40 50 60-10

-8

-6

-4

-2

0

2

4x 10

7

Time

Pro

fit, E

uro

s

Profit Changes When The Effects Of Brand 2 Are Lowered

brand1

brand2

brand3

brand4

brand5

brand6

0 10 20 30 40 50 60-5

-4

-3

-2

-1

0

1

2x 10

7

Time

Pro

fit, E

uro

s

Profit Changes When The Effects of Brand 3 Are Lowered

brand1

brand2

brand3

brand4

brand5

brand6

TitleIntroduction




Brand Monetary Values

Table: Long-Run Brand Monetary Values At Different Discount Rates,million Euros

Discount rate Brand1 Brand2 Brand35% 5, 024.6 12, 413.0 3, 806.310% 2, 512.3 6, 206.5 1, 903.115% 1, 674.9 4, 137.6 1, 268.820% 1, 256.2 3, 103.2 951.625% 1, 004.9 2, 482.6 761.3


TitleIntroduction



Conclusion

This paper has developed a new quantitative method in evaluating brands’monetary value in a dynamic competition setting, which is close to the realbusiness world. The empirical work confirms the validity and accuracy of themethod.

• It estimates a more "pure" brand effect not only by removing the effectsof products and advertising effects but also by controlling the serialcorrelation and effects of competitors’ advertising.

• It also contributes to the literature in simplifying a complex panel,abstracting the key relationship in a dynamic time series setting, andenabling a deeper analysis on economic and marketing questions.


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