Competitive Price Targeting...Mobile marketing and price targeting aTrgeting competitive locations...

57

Transcript of Competitive Price Targeting...Mobile marketing and price targeting aTrgeting competitive locations...

Page 1: Competitive Price Targeting...Mobile marketing and price targeting aTrgeting competitive locations to drive coupon redemption Dunkin': 3.6% Department store: 2% A source of incremental

Competitive Price TargetingStrategic Interactions in Mobile Marketing

Jean-Pierre Dubé1

Zheng Fang2

Nathan Fong3

Xueming Luo3

1University of Chicago, Booth School of Business and NBER2Sichuan University, Business School

3Temple University, Fox School of Business

November 2015

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Mobile marketing and price targeting

Targeting competitive locations to drivecoupon redemption

Dunkin': 3.6%

Department store: 2%

A source of incremental sales

Not accounting for competitive response

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Mobile marketing and price targeting

Targeting competitive locations to drivecoupon redemption

Dunkin': 3.6%

Department store: 2%

A source of incremental sales

Not accounting for competitive response

2/23

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Mobile marketing and price targeting

Targeting competitive locations to drivecoupon redemption

Dunkin': 3.6%

Department store: 2%

A source of incremental sales

Not accounting for competitive response

2/23

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Competitive price targeting

Monopoly: targeting weakly dominates uniform pricing

Firms may optimize based on unilateral evaluations

Oligopoly: targeting can result in lower prices and prots in every segment

Asymmetric best response a necessary condition for ambiguity (Corts, 1998)

Cannot necessarily commit to no targeting (Thisse and Vives, 1988; Shaer and

Zhang, 1995)

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Competitive price targeting

Monopoly: targeting weakly dominates uniform pricing

Firms may optimize based on unilateral evaluations

Oligopoly: targeting can result in lower prices and prots in every segment

Asymmetric best response a necessary condition for ambiguity (Corts, 1998)

Cannot necessarily commit to no targeting (Thisse and Vives, 1988; Shaer and

Zhang, 1995)

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Research objectives

Estimate the eect of price targeting on prots in a competitive market

Evaluate the adequacy of unilateral optimization

Challenge: rms (and researchers) lack information on own price responseunder varying competitive prices

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Research objectives

Estimate the eect of price targeting on prots in a competitive market

Evaluate the adequacy of unilateral optimization

Challenge: rms (and researchers) lack information on own price responseunder varying competitive prices

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Research objectives

Estimate the eect of price targeting on prots in a competitive market

Evaluate the adequacy of unilateral optimization

Challenge: rms (and researchers) lack information on own price responseunder varying competitive prices

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Agenda

1 Introduction

2 Field Experiment

3 Model

4 Results

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Experimental design

Randomly assigned prices

3 levels for oense (holdout, medium, high)

3 levels for defense (holdout, low, medium)

Observed segments

2 locations (symmetric design)

2 behavioral types (high and low based on recency)

N = 500 per cell, 18, 000 total, mid-day on a Saturday

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Experimental design

Randomly assigned prices

3 levels for oense (holdout, medium, high)

3 levels for defense (holdout, low, medium)

Observed segments

2 locations (symmetric design)

2 behavioral types (high and low based on recency)

N = 500 per cell, 18, 000 total, mid-day on a Saturday

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Experimental design

Randomly assigned prices

3 levels for oense (holdout, medium, high)

3 levels for defense (holdout, low, medium)

Observed segments

2 locations (symmetric design)

2 behavioral types (high and low based on recency)

N = 500 per cell, 18, 000 total, mid-day on a Saturday

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Aggregate response

Asymmetric cross-promotional eectsDefense is eective, but all rms still discount

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Aggregate response

Asymmetric cross-promotional eectsDefense is eective, but all rms still discount

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Aggregate response

Asymmetric cross-promotional eects

Defense is eective, but all rms still discount

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Aggregate response

Asymmetric cross-promotional eects

Defense is eective, but all rms still discount

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Aggregate response

Asymmetric cross-promotional eectsDefense is eective, but all rms still discount

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Observations

Similar pattern across 4 segments

In equlibrium everyone chooses maximum discount

Discrete pricing treatments limit observed strategy sets

Limited range and resolution

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Observations

Similar pattern across 4 segments

In equlibrium everyone chooses maximum discount

Discrete pricing treatments limit observed strategy sets

Limited range and resolution

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Agenda

1 Introduction

2 Field Experiment

3 Model

4 Results

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Estimating the impact on prots

Estimate a demand model

Probit, MCMC

Derive best response functions

Posterior represents rms' beliefs

Identify xed points

Compare prots across targeting scenarios

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Estimating the impact on prots

Estimate a demand model

Probit, MCMC

Derive best response functions

Posterior represents rms' beliefs

Identify xed points

Compare prots across targeting scenarios

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Estimating the impact on prots

Estimate a demand model

Probit, MCMC

Derive best response functions

Posterior represents rms' beliefs

Identify xed points

Compare prots across targeting scenarios

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Demand model

Consumers choose y ∈ A,B ,C, where j = A,B denote the theaters andj = C is the outside option

k = 1, ...,K observable segments, with population weights λk

pj is the ticket price at theater j

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Utility

Consumer h's utility if a member of segment k :

uhA = θkA − αkpA + εhA

uhB = θkB − αkpB + εhB

uhC = εhC

Correlated errors allow for exible substitution patterns:

ηh ≡[εhA − εhCεhB − εhC

]∼ N (0,Ψ)

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Utility

Consumer h's utility if a member of segment k :

uhA = θkA − αkpA + εhA

uhB = θkB − αkpB + εhB

uhC = εhC

Correlated errors allow for exible substitution patterns:

ηh ≡[εhA − εhCεhB − εhC

]∼ N (0,Ψ)

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Estimation

We can express utilities as:

Uh ≡[uhAuhB

]= BkX + ηh

And choice probabilities as:

Pr(yh = j |Bk ,X ,Ψk

)= Pr (uhj − uhi > 0,∀i 6= j)

Transformation of the utilities leads to a trinomial probit

Estimate using MCMC separately for each segment

Retain R posterior draws for subsequent computations,B r ,k ,Ψr ,k

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Estimation

We can express utilities as:

Uh ≡[uhAuhB

]= BkX + ηh

And choice probabilities as:

Pr(yh = j |Bk ,X ,Ψk

)= Pr (uhj − uhi > 0,∀i 6= j)

Transformation of the utilities leads to a trinomial probit

Estimate using MCMC separately for each segment

Retain R posterior draws for subsequent computations,B r ,k ,Ψr ,k

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Scenarios for comparison

Competitive equilibrium with uniform pricing

Competitive equilibrium with targeted pricing

Unilateral targeting

A deviation from uniform pricing, without competitive response

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Scenarios for comparison

Competitive equilibrium with uniform pricing

Competitive equilibrium with targeted pricing

Unilateral targeting

A deviation from uniform pricing, without competitive response

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Scenarios for comparison

Competitive equilibrium with uniform pricing

Competitive equilibrium with targeted pricing

Unilateral targeting

A deviation from uniform pricing, without competitive response

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Uniform pricing

Firm j ′s pricing problem

puniformj = argmaxp

p∑K

k=1λkE

[Pr(j |Bk , p,Ψk

)|Dk]

≈ argmaxp

p[∑K

k=1λk 1

R

∑Rr=1

Pr(j |B r ,k , p,Ψr ,k

)]

FONC

K∑k=1

λkR∑

r=1

Pr(j |B r ,k , p,Ψr ,k

)+puniformj

K∑k=1

R∑r=1

λk∂Pr

(j |B r ,k , p,Ψr ,k

)∂pj

= 0

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Uniform pricing

Firm j ′s pricing problem

puniformj = argmaxp

p∑K

k=1λkE

[Pr(j |Bk , p,Ψk

)|Dk]

≈ argmaxp

p[∑K

k=1λk 1

R

∑Rr=1

Pr(j |B r ,k , p,Ψr ,k

)]FONC

K∑k=1

λkR∑

r=1

Pr(j |B r ,k , p,Ψr ,k

)+puniformj

K∑k=1

R∑r=1

λk∂Pr

(j |B r ,k , p,Ψr ,k

)∂pj

= 0

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Targeted pricing

Firm j ′s pricing problem for a partition Ω of the K = 4 segments

pΩj = argmax

p

∑ω∈Ω pω

∑k∈ω λ

kE[Pr(j |Bk , p,Ψr ,k

)|Dk]

≈ argmaxp

∑ω∈Ω pω

∑k∈ω λ

k 1

R

∑Rr=1

Pr(j |B r ,k , p,Ψr ,k

)

FONC (∀ω ∈ Ω)

∑k∈ω

(λk

R∑r=1

Pr(j |B r ,k , p,Ψr ,k

)+ pΩ

R∑r=1

λk∂Pr

(j |B r ,k , p,Ψr ,k

)∂pj

)= 0

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Targeted pricing

Firm j ′s pricing problem for a partition Ω of the K = 4 segments

pΩj = argmax

p

∑ω∈Ω pω

∑k∈ω λ

kE[Pr(j |Bk , p,Ψr ,k

)|Dk]

≈ argmaxp

∑ω∈Ω pω

∑k∈ω λ

k 1

R

∑Rr=1

Pr(j |B r ,k , p,Ψr ,k

)FONC (∀ω ∈ Ω)

∑k∈ω

(λk

R∑r=1

Pr(j |B r ,k , p,Ψr ,k

)+ pΩ

R∑r=1

λk∂Pr

(j |B r ,k , p,Ψr ,k

)∂pj

)= 0

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Agenda

1 Introduction

2 Field Experiment

3 Model

4 Results

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Parameter estimates

Coecient High, A Low, A High, B Low, B

θA -0.344 0.25 -1.066 -1.413

(-0.651,-0.028) (-0.178,0.695) (-1.344,-0.79) (-1.737,-0.964)

θB -1.043 -0.628 -0.376 0

(-2.002,-0.425) (-1.499,-0.023) (-0.741,-0.035) (-0.311,0.349)

α -0.027 -0.044 -0.027 -0.028

(-0.033,-0.021) (-0.053,-0.035) (-0.036,-0.019) (-0.043,-0.017)

ρA,B 0.796 -0.951 0.962 0.348

(0.443,0.931) (-0.99,-0.826) (0.926,0.985) (-0.953,0.955)

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Elasticity estimates

High, A Low, A High, B Low, B

Both set regular prices of 75 RMB

pA pB pA pB pA pB pA pB

Firm A -5.33 0.15 -10.17 0.00 -16.99 13.17 -7.88 3.72

Firm B 3.44 -8.35 0.00 -11.82 0.02 -4.84 0.42 -8.96

Both set prices of 30 RMB (60% o)

pA pB pA pB pA pB pA pB

Firm A -1.40 0.10 -2.07 0.00 -7.97 5.95 -3.10 0.77

Firm B 1.52 -3.44 0.00 -4.33 0.01 -1.25 0.03 -1.91

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Best-response functions (targeting on one dimension)

0 20 40 60 80

020

4060

80

Best ResponseGeographic Targeting

Theater B price

The

ater

A p

rice

BRA defenseBRA offenseBRB offenseBRB defenselocation A equillocation B equiluniform equil

0 20 40 60 80

020

4060

80

Best ResponseType Targeting

Theater B price

The

ater

A p

rice

BRA HighBRA LowBRB HighBRB LowHigh equilLow equiluniform equil

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Equilibrium prots vs. unilateral targeting prots

Equilibrium

Firm A Firm B

Uniform 196 291

Location 196 298

Type 198 295

Type and Location 197 297

Unilateral

Firm A Firm B

198 302

197 294

200 304

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Equilibrium prots vs. unilateral targeting prots

Equilibrium

Firm A Firm B

Uniform 196 291

Location 196 298

Type 198 295

Type and Location 197 297

Unilateral

Firm A Firm B

198 302

197 294

200 304

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Conclusions

Competition moderates the eectiveness of price targeting

Firms could easily mis-estimate the protability of targeting

Overestimate geographical targeting (asymmetric best response)

Underestimate behavioral targeting (symmetric best response)

Future research: consumer response

Consumer dynamics (Shin and Sudhir, 2010)

Strategic consumers (Chen, Li, and Sun, 2015)

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Uniform pricing equilibrium

Firm A Firm B

Price 19.2942 18.8641

Share: High type, location A 0.1896 0.0168

Low type, location A 0.2795 0.0465

High type, location B 0.0005 0.2039

Low type, location B 0.0106 0.2380

Expected prot per 100 customers messaged 196.04 291.33

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Equilibrium prices

Market Firm A Price Firm B Price

Uniform Pooled 19.294 18.864

by geography Loc A 19.575 10.564Loc B 10.485 20.064

by type High 22.948 23.786Low 18.597 17.775

by geography and type A High 21.335 10.870A Low 19.146 10.546B High 5.230 20.595B Low 11.874 19.322

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Importance of considering competitive response

Firm A Prot Firm B Prot

Uniform pricing 196 291

Equilibrium targeting 197 297

Unilateral targeting 200 304

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Targeting choice as a strategic game

Firm B

Firm A Uniform pricing Unilateral targeting

Uniform pricing 196, 291 194, 304

Unilateral targeting 198, 291 197, 297

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Experimental purchase response by segment

0.0

2.0

4.0

6

Defense: 0% Offense: 0% Defense: 0% Offense: 40% Defense: 0% Offense: 60%

0.0

2.0

4.0

6

Defense: 0% Offense: 0% Defense: 0% Offense: 40% Defense: 0% Offense: 60%

0.0

2.0

4.0

6

Defense: 20% Offense: 0% Defense: 20% Offense: 40% Defense: 20% Offense: 60%

0.0

2.0

4.0

6

Defense: 20% Offense: 0% Defense: 20% Offense: 40% Defense: 20% Offense: 60%

0.0

2.0

4.0

6

Defense: 40% Offense: 0% Defense: 40% Offense: 40% Defense: 40% Offense: 60%

0.0

2.0

4.0

6

Defense: 40% Offense: 0% Defense: 40% Offense: 40% Defense: 40% Offense: 60%

0.0

2.0

4.0

6

Defense: 0% Offense: 0% Defense: 0% Offense: 40% Defense: 0% Offense: 60%

0.0

2.0

4.0

6

Defense: 0% Offense: 0% Defense: 0% Offense: 40% Defense: 0% Offense: 60%

0.0

2.0

4.0

6

Defense: 20% Offense: 0% Defense: 20% Offense: 40% Defense: 20% Offense: 60%

0.0

2.0

4.0

6

Defense: 20% Offense: 0% Defense: 20% Offense: 40% Defense: 20% Offense: 60%

0.0

2.0

4.0

6

Defense: 40% Offense: 0% Defense: 40% Offense: 40% Defense: 40% Offense: 60%

0.0

2.0

4.0

6

Defense: 40% Offense: 0% Defense: 40% Offense: 40% Defense: 40% Offense: 60%

Low

Hig

hLocation A Location B

Defensive Purchases Offensive Purchases

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Experimental revenues by segment

0.0

1.0

2.0

3.0

4

Defense: 0% Offense: 0% Defense: 0% Offense: 40% Defense: 0% Offense: 60%

0.0

1.0

2.0

3.0

4

Defense: 0% Offense: 0% Defense: 0% Offense: 40% Defense: 0% Offense: 60%

0.0

1.0

2.0

3.0

4

Defense: 20% Offense: 0% Defense: 20% Offense: 40% Defense: 20% Offense: 60%

0.0

1.0

2.0

3.0

4

Defense: 20% Offense: 0% Defense: 20% Offense: 40% Defense: 20% Offense: 60%

0.0

1.0

2.0

3.0

4

Defense: 40% Offense: 0% Defense: 40% Offense: 40% Defense: 40% Offense: 60%

0.0

1.0

2.0

3.0

4

Defense: 40% Offense: 0% Defense: 40% Offense: 40% Defense: 40% Offense: 60%

0.0

1.0

2.0

3.0

4

Defense: 0% Offense: 0% Defense: 0% Offense: 40% Defense: 0% Offense: 60%

0.0

1.0

2.0

3.0

4

Defense: 0% Offense: 0% Defense: 0% Offense: 40% Defense: 0% Offense: 60%

0.0

1.0

2.0

3.0

4

Defense: 20% Offense: 0% Defense: 20% Offense: 40% Defense: 20% Offense: 60%

0.0

1.0

2.0

3.0

4

Defense: 20% Offense: 0% Defense: 20% Offense: 40% Defense: 20% Offense: 60%

0.0

1.0

2.0

3.0

4

Defense: 40% Offense: 0% Defense: 40% Offense: 40% Defense: 40% Offense: 60%

0.0

1.0

2.0

3.0

4

Defense: 40% Offense: 0% Defense: 40% Offense: 40% Defense: 40% Offense: 60%

Low

Hig

hLocation A Location B

Defensive Revenues Offensive Revenues

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Posterior prot dierences: unilateral/equilibrium vs.

uniform pricing

Unilateral Location TargetingTheater A

Rev(unif)−Rev(geog) (RMB)

Fre

quen

cy

0.00 0.05 0.10 0.15

050

150

250

Unilateral Location TargetingTheater B

Rev(unif)−Rev(geog) (RMB)

Fre

quen

cy0.0 0.1 0.2 0.3

040

8012

0

Unilateral Type TargetingTheater A

Rev(unif)−Rev(type) (RMB)

Fre

quen

cy

−0.01 0.00 0.01 0.02 0.03

050

100

150

Unilateral Type TargetingTheater B

Rev(unif)−Rev(type) (RMB)

Fre

quen

cy

−0.04 −0.02 0.00 0.02 0.04 0.06 0.08

020

6010

0

Unilateral Type & Location TargetingTheater A

Rev(unif)−Rev(geog & type) (RMB)

Fre

quen

cy

0.00 0.05 0.10 0.15

050

150

Unilateral Type & Location TargetingTheater B

Rev(unif)−Rev(geog & type) (RMB)

Fre

quen

cy

0 1 2 3

050

100

150

Location TargetingTheater A

Rev(unif)−Rev(geog) (RMB)

Fre

quen

cy

−0.05 0.00 0.05 0.10

050

100

150

Location TargetingTheater B

Rev(unif)−Rev(geog) (RMB)

Fre

quen

cy

−0.1 0.0 0.1 0.2 0.3

040

8012

0

Type TargetingTheater A

Rev(unif)−Rev(type) (RMB)

Fre

quen

cy

0.00 0.01 0.02 0.03 0.04

050

100

150

Type TargetingTheater B

Rev(unif)−Rev(type) (RMB)

Fre

quen

cy

−0.02 0.00 0.02 0.04 0.06 0.08

040

8012

0

Type & Location TargetingTheater A

Rev(unif)−Rev(geog & type) (RMB)

Fre

quen

cy

−0.05 0.00 0.05 0.10

050

100

150

Type & Location TargetingTheater B

Rev(unif)−Rev(geog & type) (RMB)

Fre

quen

cy

0 1 2 3

050

100

150

7/7

Page 57: Competitive Price Targeting...Mobile marketing and price targeting aTrgeting competitive locations to drive coupon redemption Dunkin': 3.6% Department store: 2% A source of incremental

Posterior prot dierences: unilateral/equilibrium vs.

uniform pricing

Unilateral Location TargetingTheater A

Rev(unif)−Rev(geog) (RMB)

Fre

quen

cy

0.00 0.05 0.10 0.15

050

150

250

Unilateral Location TargetingTheater B

Rev(unif)−Rev(geog) (RMB)

Fre

quen

cy0.0 0.1 0.2 0.3

040

8012

0

Unilateral Type TargetingTheater A

Rev(unif)−Rev(type) (RMB)

Fre

quen

cy

−0.01 0.00 0.01 0.02 0.03

050

100

150

Unilateral Type TargetingTheater B

Rev(unif)−Rev(type) (RMB)

Fre

quen

cy

−0.04 −0.02 0.00 0.02 0.04 0.06 0.08

020

6010

0

Unilateral Type & Location TargetingTheater A

Rev(unif)−Rev(geog & type) (RMB)

Fre

quen

cy

0.00 0.05 0.10 0.15

050

150

Unilateral Type & Location TargetingTheater B

Rev(unif)−Rev(geog & type) (RMB)

Fre

quen

cy

0 1 2 3

050

100

150

Location TargetingTheater A

Rev(unif)−Rev(geog) (RMB)

Fre

quen

cy

−0.05 0.00 0.05 0.10

050

100

150

Location TargetingTheater B

Rev(unif)−Rev(geog) (RMB)

Fre

quen

cy

−0.1 0.0 0.1 0.2 0.3

040

8012

0

Type TargetingTheater A

Rev(unif)−Rev(type) (RMB)

Fre

quen

cy

0.00 0.01 0.02 0.03 0.04

050

100

150

Type TargetingTheater B

Rev(unif)−Rev(type) (RMB)

Fre

quen

cy

−0.02 0.00 0.02 0.04 0.06 0.08

040

8012

0

Type & Location TargetingTheater A

Rev(unif)−Rev(geog & type) (RMB)

Fre

quen

cy

−0.05 0.00 0.05 0.10

050

100

150

Type & Location TargetingTheater B

Rev(unif)−Rev(geog & type) (RMB)

Fre

quen

cy

0 1 2 3

050

100

150

7/7