Competitive Price Targeting with Smartphone Coupons
1
Jean-Pierre Dubé
2
3
Nathan Fong
3
Xueming Luo
Zheng Fang
1 University
of Chicago, Booth School of Business and NBER
2 Sichuan University, Business School
3 Temple University, Fox School of Business
January 2015
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Mobile marketing and price targeting
mobile technology creates new opportunities to price discriminate
users carry them most of the time
unlike PC, tends to be tied to single user
location-services allow customers to be tracked
on geographic location
on behavior
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Mobile marketing and price targeting
mobile targeting service providers claim lift rates as high as 41% from
geo-targeting
more than 10 billion mobile coupons redeemed globally in 2013
academics conrm these ndings (Ghose et al. 2013, Luo et al 2014,
Danaher et al 2015)
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Mobile marketing and
geo-conquesting
What if we target a competitor's customers?
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Mobile marketing and
geo-conquesting
What if we target a competitor's customers?
Targeting competitive locations to drive
coupon redemption
Dunkin': 3.6%
Department store: 2%
A source of incremental sales
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Mobile marketing and
geo-conquesting
What if we target a competitor's customers?
Targeting competitive locations to drive
coupon redemption
Dunkin': 3.6%
Department store: 2%
A source of incremental sales
Not accounting for competitive response
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Price discrimination theory (PD)
long literature on third-degree monopoly PD (Pigou 1920, Varian 1989)
always (weakly) protable if
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1
rm has market power
2
consumers can be segmented
3
arbitrage through resale prohibited
Competitive price discrimination theory
Analogy of monopoly PD does not always apply in oligopoly markets
in equilibrium, must compare
1
gains from surplus extraction
2
potential losses associated with intensity of competition
see handbook chapter by Stole (2007)
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Competitive price discrimination theory
Equilibrium prots can rise or fall when competing rms engage in PD
Even when PD leads to lower prices and prots in every segment
targeting is a dominant strategy (Corts 1998)
can lead to a prisoner's dilemma even under commitment
Eect of PD on equilibrium prots is an empirical question
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Research objectives
Estimate the eect of mobile price targeting on prots in a competitive
market
geographic targeting
geo-conquesting
behavioral targeting
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Research objectives
Estimate the eect of mobile price targeting on prots in a competitive
market
geographic targeting
geo-conquesting
behavioral targeting
Evaluate the adequacy of unilateral optimization (i.e. monopoly PD)
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Research objectives
Estimate the eect of mobile price targeting on prots in a competitive
market
geographic targeting
geo-conquesting
behavioral targeting
Evaluate the adequacy of unilateral optimization (i.e. monopoly PD)
Challenge: rms (and researchers) lack information on own price response
under varying competitive prices
i.e. if competitor reacts, rm's optimal price changes
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Agenda
1
Introduction
2
Field Experiment
3
Model
4
Results
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Experimental design
Illegal to coordinate a price experiment between competing rms
We partnered with the mobile service provider
Duopoly market with two competing movie theaters in dierent malls
historic data:
mobile phones represent over 75% of malls' trac
observe mobile phones traveling between the malls
observe mobile phones dwelling in theaters
Send consumers coupons with price discounts from both competitors
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Experimental design
Randomly assigned prices
3 levels for oense (holdout, medium, high)
3 levels for defense (holdout, low, medium)
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Experimental design
Randomly assigned prices
3 levels for oense (holdout, medium, high)
3 levels for defense (holdout, low, medium)
4 observed segments
2 locations (symmetric design)
2 behavioral types (High and Low based on recency)
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Experimental design
Randomly assigned prices
3 levels for oense (holdout, medium, high)
3 levels for defense (holdout, low, medium)
4 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
o-peak period of demand (no capacity concerns)
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Aggregate response
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Aggregate response
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Aggregate response
Asymmetric cross-promotional eects
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Aggregate response
Asymmetric cross-promotional eects
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Aggregate response
Asymmetric cross-promotional eects
Defense is eective, but all rms still discount
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Observations
In equilibrium everyone chooses maximum discount on test grid
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Observations
In equilibrium everyone chooses maximum discount on test grid
But our test grid is too limited to observe best-response strategies
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Observations
In equilibrium everyone chooses maximum discount on test grid
But our test grid is too limited to observe best-response strategies
We need a model to study patterns of best-response and equilibrium
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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
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Estimating the impact on prots
Estimate a demand model
Probit, MCMC
Derive best response functions
Posterior represents rms' beliefs
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Estimating the impact on prots
Estimate a demand model
Probit, MCMC
Derive best response functions
Posterior represents rms' beliefs
Compute xed points
Compare prots across targeting scenarios
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Demand model
h
= 1, ..., H
y
∈ {A, B , C },
consumers
choices
theaters A, B
C is no-purchase
k
= 1, ..., K
observable segments, with population weights
pj is the ticket price at theater j
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λk
Utility
Consumer h's utility if a member of segment k :
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uhA
= θAk − αk pA + ˜hA
uhB
= θBk − αk pB + ˜hB
uhC
=
˜hC
Utility
Consumer h's utility if a member of segment k :
uhA
= θAk − αk pA + ˜hA
uhB
= θBk − αk pB + ˜hB
uhC
=
˜hC
Correlated errors allow for exible substitution patterns:
ηh ≡
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˜hA − ˜hC
˜hB − ˜hC
∼N
k
0, Ψ
Demand
Estimate choice probabilities for each segment:
Pr
choose theater j|p , Θ
k =
Pr
where
Θk
are demand parameters to be estimated
p are prices charged
Trinomial probit for each segment, k = 1, ..., 4
use Bayesian estimation (see book by Rossi et al 2005)
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uhj is highest utility
Supply side: pricing as decision-theoretic problem
assumptions about rms
risk neutral
form posterior demand beliefs conditional on data,F
(Θ|D)
rms use posterior expected prots as their objective
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Supply side: Posterior distribution of prots
rm sets prices, p
∗
,
to maximize data-based posterior expected prots:
π (p ) = (p − c ) E [Pr (j |p , Θ) |D]
where posterior simulated with the demand estimates
for pricing decision, p
∗
,
there will be a distribution of prots reecting the
rms' statistical uncertainty about demand
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Supply side: pricing scenarios
1
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Bertrand-Nash equilibrium with uniform pricing
Supply side: pricing scenarios
1
Bertrand-Nash equilibrium with uniform pricing
2
Bertrand-Nash equilibrium with targeted pricing
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Supply side: pricing scenarios
1
Bertrand-Nash equilibrium with uniform pricing
2
Bertrand-Nash equilibrium with targeted pricing
3
Unilateral targeting
A deviation from uniform pricing, without competitive response
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Supply side: uniform pricing
each rm j faces posterior expected demand
E [Qj (pj , p−j ) |D] =
PK
k k =1 λ E Pr
j |p , Θ
k |Dk 0
rm j s decision-theoretic pricing problem:
uniform =
pj
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argmax
p
j
{pj E [Qj (pj , p−j ) |D]}
Supply side: targeted pricing
recall there are k
= 1, ..., 4
segments
potential targeting:
geography
{location
Ω =
{{High,
A, location B}
A, Low A}, {High B, Low B}} ;
type
{
Ω =
{{High,
high type, low type}
A and B}, {Low, A and B}} ;
mixed
Ω = {{HighA} , {LowA} , {HighB } , {LowB } } .
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Supply side: targeted pricing
For customer partition
Ω,
each rm j faces expected demand
E [Qj (pj , p−j ) |D, Ω] =
P P k λ E Pr
ω∈Ωk ∈ω
j |, pω , Θ
k |Dk 0
rm j s decision-theoretic pricing problem:
Ω
pj
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=
argmax
p
j
P
pj ω E [Qj (pj , p−j ) |D, Ω]
ω∈Ω
Supply side: unilateral targeted pricing
rm j optimizes its targeted price
competitor holds its price xed at uniform equilibrium level
i.e. only rm j deviates from uniform equilibrium
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Agenda
1
Introduction
2
Field Experiment
3
Model
4
Results
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Demand Estimates
High
0.2
0.4
E[ Pr( j=A | pA , pB = 75 , ΘA ]
High
E[ Pr( j=A | pA , pB = 30 , ΘA ]
0.0
conversion rate
0.6
Theater A Demand (High Recency, Mall A)
0
20
40
60
80
Asymmetric substitution in defensive vs oensive market
price (RMB)
High
0.10
0.20
E[ Pr( j=A | pA , pB = 75 , ΘB ]
High
E[ Pr( j=A | pA , pB = 30 , ΘB ]
0.00
conversion rate
Theater A Demand, (High Recency, Mall B)
0
20
40
price (RMB)
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60
80
Demand Estimates
High
0.10
0.20
E[ Pr( j=A | pA , pB = 75 , ΘB ]
High
E[ Pr( j=A | pA , pB = 30 , ΘB ]
0.00
conversion rate
Theater A Demand (High Recency, Mall B)
0
20
40
60
80
price (RMB)
0.10
0.20
E[ Pr( j=A | pA , pB = 75 , ΘLow
]
B
E[ Pr( j=A | pA , pB = 30 , ΘLow
]
B
0.00
conversion rate
Theater A Demand, (Low Recency, Mall B)
0
20
40
price (RMB)
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60
80
Higher Substitution in High Recency Segment
i.e. more intense competition in High Recency Segment
Elasticity estimates
High, A
Low, A
High, B
Low, B
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
stronger substitution in High segments will intensify competition
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Best-response functions (uniform pricing)
Best Response
Uniform Pricing
40
Uniform Price Equilibrium
20
Theater A price
60
80
Best−Response theater A
Best−Response theater B
●
0
\mathbb{E}
0
20
40
Theater B price
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60
80
Unilateral targeting
Firm A
Firm B
Posterior mean
Posterior mean
Posterior mean
Posterior mean
prots (RMB)
% improvement
prots (RMB)
% improvement
Uniform
1.96
-
2.91
-
Location
1.98
1.18
3.02
3.82
Type
1.97
0.45
2.94
0.94
Type & Location
2.00
1.83
3.04
4.51
Geographic targeting has higher unilateral gains than behavioral targeting
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Unilateral targeting
Unilateral Location Targeting
Theater B
100
0
50
Frequency
300
0 100
Frequency
150
Unilateral Location Targeting
Theater A
0
5
10
15
0
10
15
200
100
0
200
Frequency
400
Unilateral Type Targeting
Theater B
0
Frequency
Unilateral Type Targeting
Theater A
5
0
5
10
15
0
10
15
Unilateral Type & Location Targeting
Theater B
100
0
0
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50
Frequency
300
100
0
Frequency
150
Unilateral Type & Location Targeting
Theater A
5
5
10
15
0
5
10
15
Distribution of precentage improvement in expected revenues
What if Competitor Responds?
25000
Theater A Revenues and Pricing
Mall B −− pA × E [ Pr ( choose A | pA , pB = 75 , ΘA )]
Mall B −− pA × E [ Pr ( choose A | pA , pB = 30 , ΘB )]
15000
10000
●
0
5000
Expected revenue (RMB)
20000
●
0
20
40
price (RMB)
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60
80
Best-response functions for geographic targeting
Best Response
Geographic Targeting
40
Mall A
Price
Equilibrium
20
Theater A price
60
80
BRA defense
BRA offense
BRB offense
BRB defense
●
Uniform Price Equilibrium
●
●
0
Mall B Price Equilibrium
0
20
40
Theater B price
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60
80
Best-response functions for geographic targeting
Best Response
Geographic Targeting
40
Mall A
Price
Equilibrium
20
Theater A price
60
80
BRA defense
BRA offense
BRB offense
BRB defense
●
Uniform Price Equilibrium
●
●
note the rms' asymmetric pricing incentives
0
Mall B Price Equilibrium
0
20
40
Theater B price
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60
80
Best-response functions for behavioral targeting
Best Response
Type Targeting
40
Theater A price
60
80
BRA High
BRA Low
BRB High
BRB Low
High type
Price
Equilibrium
20
●
●●
Low type
Price
Equilibrium
0
Uniform
Price
Equilibrium
0
20
40
Theater B price
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60
80
Best-response functions for behavioral targeting
Best Response
Type Targeting
40
Theater A price
60
80
BRA High
BRA Low
BRB High
BRB Low
High type
Price
Equilibrium
20
●
●●
Low type
Price
Equilibrium
0
0
20
40
Theater B price
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note the rms' symmetric pricing incentives
Uniform
Price
Equilibrium
60
80
Equilibrium targeting prots
Firm A
Firm B
Posterior mean
Posterior mean
Posterior mean
Posterior mean
prots (RMB)
% improvement
prots (RMB)
% improvement
Uniform
1.96
-
2.91
-
Location
1.96
0.13
2.98
2.5
Type
1.98
0.97
2.95
1.2
Type & Location
1.97
0.63
2.97
1.91
- Gains from behavioral better than unilateral case, geographic worse
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Equilibrium targeting prots
Frequency
9%
of mass
below 0
0
100
200
100
0
Frequency
49 %
of mass
below 0
300
Unilateral Location Targeting
Theater B
300
Unilateral Location Targeting
Theater A
−5
0
5
10
15
−5
5
10
15
Unilateral Type Targeting
Theater B
200
0
100
Frequency
400
200
0
Frequency
300
Unilateral Type Targeting
Theater A
0
−5
0
5
10
15
−5
5
10
15
300
200
Frequency
0
100
150
0 50
Frequency
0
Unilateral Type & Location Targeting
Theater B
250
Unilateral Type & Location Targeting
Theater A
−5
0
5
10
15
−5
0
5
10
15
Distribution of precentage improvement in expected revenues
cannot rule out that geographic targeting worse than uniform pricing
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Targeting as an equilibrium outcome
suppose game consists of simultaneously choosing whether to PD and what
prices to charge
from monopoly PD, we know PD emerges as weakly dominant strategy
relative to uniform pricing for each rm
even if geographic worse than uniform, rms would still choose PD
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Targeting as an equilibrium outcome
Theater B expected revenues
Uniform
Theater A expected revenues
location
Uniform
1.96
2.91
1.94
3.04
location
1.98
2.91
1.97
2.97
Probability geog worse than uniform 51% (A) and 9% (B)
Theater B expected revenues
Uniform
Theater A expected revenues
type
Uniform
1.96
2.91
1.97
2.94
type
1.97
2.92
1.98
2.95
Probability type is worse than uniform is close to 0% for both
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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 cherry-picking consumers (Chen, Li, and Sun, 2015)
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