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Asim AnsariCarl Mela
E-Customization
Page 2
Introduction
MarketingTargeted Promotions List SegmentationConjoint AnalysisRecommendation Systems
Computer ScienceCollaborative filteringMachine learning
Customization key to managing relationships
Page 3
Customization and Electronic Media
Electronic media facilitate customizationLow production costsTimely data (received) and information (sent)PersonalizableReach
Page 4
Customization Benefits
Content ProvidersIncreasing site usage via customization can increase advertising revenueInternet Advertising forecast to grow to rapidly
E-commerceIncreasing sales via customization
Page 5
E-Customization Contexts
Content providers can customizecontent (editorial)design (how many links and what order) to increase site visits, advertising revenue and loyalty.
E-commerce firms can customizecontent (products, price, incentives, etc.) and design (how many items and what order) to increase sales and loyalty.
The structure of the problem is identical.
Page 6
E-Customization Strategies
Two customization StrategiesOnsiteExternal : e-mails
Customizable at low-costNeed not wait for customers to come to site
We take an external customization approach
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E-mail Example
Page 8
E-mail Marketing: Volume Growth
’99 ’00 ’01 ’02 ’03 ’04
Emails(billions)
050
100
150
200
250
Email retention servicesEmail acquisition services
Source: Forrester Report:Email Marketing Dialog, January 2000
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E-mail Marketing Services: Revenue Growth
Revenues(billions)
012345
’99 ’00 ’01 ’02 ’03 ’04
Email retention servicesEmail acquisition services
Source: Forrester Report:Email Marketing Dialog, January 2000
Page 10
Email Design Problem
Sports
International News
National News
Weather
Arts
Determine the Content and Layout of the e-mail on a one-on-one basis
Page 11
StatisticalModel
Approach
E-mailConfiguration
Click-throughData
OptimizationNew E-mail
Configuration
Individual level preferencecoefficients
Page 12
Statistical Model
Probability of clicking on a link depends upon utility to clickUtility of clicking on a link = f(observed e-mail variables (html, # links),observed link variables (content and order of
link),unobserved user effect, unobserved e-mail effect,unobserved link effect,error)
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Probit Model Population Component
Uijk= 1+2*Textj+3*NumItemsj+4*Positionjk
+5*Contentk
+i1+i2*NumItemsj+i3* Positionjk +i4*Contentk
+j+j*Positionjk+j*Contentk
+k1
+eijk
i is person, j is e-mail and k is link.
Random across Individuals
Random across Emails
Page 14
Modeling Heterogeneity
Random effects are assumed to come from a population distribution with zero mean
i ~ G1
j ~ G2
k ~ G3
Page 15
Modeling Heterogeneity
Finite Mixtures Continuous Mixtures
-2
0
2-2
0
2
00.050.1
0.15
-2
0
2
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Modeling Heterogeneity: Dirichlet Process Priors
Dirichlet Process Priors can be used to model the uncertainty about functional form of the population distribution GAllows semi-parametric estimation of random effects
Page 17
Dirichlet Process Priors
A Dirichlet Process prior for a distribution G has two parameters
A distribution function G0(.) and
A positive scalar precision parameter
We write
where, G0 represents the expected value of G and > 0, represents the strength of prior beliefs that sampled distributions G will be close to G0
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Dirichlet Process Priors
Let G be a random distribution from the Dirichlet Process, Let then,
q1
qi qNp1
pi
pN
G0G
),(~ 0 GDG},{ 1 Nppp
1
)1()(;)(
},,,(Dirichlet~ 21
iiiii
N
qqpVqpE
qqqp
Page 19
Dirichlet Process: Role of
Large Large number of distinct values from the base distributionSampled distribution approximates base distribution
Small Sample will have a small number of distinct valuesSampled distribution approximates a finite mixture
Page 20
Dirichlet Process Priors: Advantages
Accommodates non-normality, multi-modality and skewnessProvides a semi-parametric alternative to the normal distributionProvides accurate individual-level estimatesAllows a synthesis of Finite Mixtures and Normal Heterogeneity
Page 21
Modeling Heterogeneity
i ~ G1 ~D(N(0,), 1)
j ~ G2 ~D(N(0,), 2)
k ~ G3 ~D(N(0,), 3)
Page 22
Inference
Bayesian Inference
Priors ~ Multivariate Normal ~ Wishart ll
~ Inverse Gamma ~ Inverse Gamma 1, 2, 3, ~ Gamma
Page 23
Sampling Based Inference
Joint Posterior Density is very complex and cannot be summarized in closed formSampling Based InferenceGibbs Sampling
Page 24
Full Conditionals
Unknowns include{u}, , {i}, {j}, {k}, , , ,
Full conditionals for DP mixed model are very similar to those for normal population distributions
Page 25
Full Conditionals for Individual-level parameters: DP model
Mixture of distributions
And Gb is the posterior distribution under the normal base distribution
This is akin to collaborative filtering on parameterspace
Page 26
Application
Large content provider with many areas in siteOne area in the site sends e-mails to registered recipients in an effort to attract them to the area
Permission marketing
Design targeting issuesNumber of links, order of links, text or html
Content targeting issuesContent type (health, financial, etc.)
Page 27
Data
Three months of e-mails, 1048 usersE-mail file: e-mail date, number of links, order of links, link content, html or textUser file: when received, by whom (registration data), which links clicked (cookies)
Sample: 11,475 observations7% response rate for links36% click on more than one link
Page 28
Models
No heterogeneityPerson heterogeneityPerson, E-mail and Link heterogeneity (Full Model)
Page 29
Predictive AbilityA
ctu
al Click
No Click
Click
No Click
a b
c d
False Positives
Click
False Negative Fraction= c/(c+d), False Positive Fraction =b/(a+b)
False Negatives
Predicted
Page 30
Predictive Ability: Link Level ROC Curves
0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
False Positive Fraction
Tru
e P
os i
t ive
Fr a
c ti o
n [ 1
- FN
F]
Page 31
Predictive Ability: Email Level ROC Curves
0 0.2 0.4 0.6 0.8 1
0
0.2
0.4
0.6
0.8
1
False Positive Fraction
Tru
e P
os i
t ive
Fr a
c ti o
n [ 1
- FN
F]
Page 32
Results - Parameter Estimates Full Model
Parameter Value Prob( <0)Design Variables Intercept (0) -1.47 (1.0)
Person Random Effects ( Std. 0i) 0.51
E-mail Random Effects (Std. 0j) 0.45
Link Random Effects (Std. 0k) 0.21
E-mail Type (1) 0.29 (0.48)
Link Order (2) -0.37 (1.0)
Person Random Effects (Std. 2i) 0.49
E-mail Random Effects (Std. 2j) 0.22
Number of Links (3) -0.02 (0.55)
Person Random Effects (Std. 3i) 0.18
Page 33
Parameter Estimates
Dirichlet Process Precision parameters
User = 103 => 61 “clusters”
Email = 114 => 65 “clusters”
Links = 383 => 383 “clusters”
Page 34
Link Level Predictions - Calibration Data
1 2 3 4 5 6 7 8 9 100
5
10
15
20
25
30
35
40
45
50
Deciles
Clic
k P
erce
ntag
e
Page 35
Link Level Predictions - Validation Data
1 2 3 4 5 6 7 8 9 100
5
10
15
20
25
30
35
Deciles
Clic
k P
erce
ntag
e
Page 36
E-mail Level Prediction - Calibration Data
1 2 3 4 5 6 7 8 9 100
10
20
30
40
50
60
70
80
90
Deciles
Clic
k P
erce
ntag
e
Page 37
E-mail Level Predictions - Validation Data
1 2 3 4 5 6 7 8 9 100
10
20
30
40
50
60
70
80
Clic
k P
erce
ntag
e
Deciles
Page 38
Optimization Model Overview
Editorial content is fixed on a given day.n links available for k positions, n ¸ k
How many links to include, what content to include, and how should it be ordered?
ObjectiveMaximize the expected number of click-backs to the siteMaximize the likelihood of returning to the site
Page 39
Optimization Procedures
Alternative 1: Complete EnumerationWith many links, computational constraints
Alternative 2: Assignment Algorithm
Page 40
Optimization: Objective Function
Maximize expected number of click-throughs to site
Let xij =1 if link i is in position j
Let pij be the probability of click through if link i is in position jMaximize Objective function
Maximize likelihood of at least one click-throughMinimize Objective function
Page 41
Optimization Model
Step 1:Maximize: Obj (x; p(x)|k)Subject to
Assignment algorithm provides exact solution
Step 2:Maximize over k={1, …, n}.
k,...,2,1jfor,1x
n,...,2,1ifor,1x
n
1iij
k
1jij
Page 42
Heuristic Approaches
Original - No change in content or orderGreedy - No change in content, order highest utility firstOrder - No change in content, optimize orderOptimal - Optimize content (#number of links) and order (our procedure)
Page 43
Optimization Results
0.34
0.51 0.530.55
0.230.34 0.35 0.36
0
0.1
0.2
0.3
0.4
0.5
0.6
Original Greedy Order Optimal
P(Click)
E(Clicks)
P(Click) E(Clicks)
Page 44
Optimization Results
Objective: At Least One ClickOptimal leads to 56% increase in at least one click.Re-ordering gives 52% improvement, content selection is the balance.Optimal improves over Order for 43% of e-mails (those adverse to clutter).Greedy and Order are similar, however for users who have high positive effect for order (scroll to bottom), Greedy does poorly (one user went from 81% to 43%).
Objective: Expected Number of ClicksSimilar results
Page 45
Optimization Results
13%15%
56%
62%
0%
10%
20%
30%
40%
50%
60%
70%
P(Click) E(Clicks)
Impr
ovem
ent
in R
espo
nse
No Heterogeneity Heterogeneity
Page 46
Conclusions
Modeling link responseVaries with content (information) and design (how much, what order)Heterogeneity in persons, links, and e-mails
E-targetingPotential to considerable enhance clicks (and presumably advertising revenue and loyalty)Our approach can be applied to both internal and external targeting strategiesOur approach can also be applied to e-tailing
Page 47
Future
Targeting Products and services for purchasesAdvertisingE-grocers (features, displays, prices)
How much is a feature worth?
Other areasOn-line choice processesAgent queries
Page 48
Dirchlet Process Moments
E[G(B)]=E[G0(B)]
and Var[G(B)]=G0(B)(1-G0(B))/
Page 49
Full Conditionals for Individual Level Model: Normal Heterogeneity
Standard Case (Simple Model)
)(,
),(}){,,|(
),0(~),1,0(~,
1
11
in
jiji
iji
iijijiij
uvlnv
vlNup
NNeeu
Page 50
Dirichlet Process Priors
A c.d.f., G on follows a Dirichlet Process if for any measurable finite partition of (B1,B2, .., Bm), of the joint distribution of the random variables
( G(B1), G(B2), …, G(Bm)) is
Dirichlet(G0(B1), …., G0(Bm)),
where, G0 is a the base distribution and is the precision parameter
