Ai plus-digital ads--theory and applications-20170605

Big Data Consultant &
Data Scientist
Craig Chao
chaocraig@gmail.com
A0!
+Bg -ata -BgBtaD Ads

Agenda
•  Big Data
•  Machine Learning
•  Digital Ads

Prelog – Myths of Big Data
•  Big Data Big Hype?

•  Machine Learning Statistics have been
used in many places, nothing new in Big
Data?

•  Big Data is Hadoop / Open Source?

,AaDDeFges o? +Bg -ata - 4V
abcnews.com.co

4V Solution Directions
Challenges Directions
Volume Scalability
Velocity Reactive / Streaming
Variety All data
Veracity Knowledge
engineering
Machine intelligence

The Revolution of Big Data
DATA
Hypotheses
Statistical Analysis
BIG DATA
Hypotheses
Machine Learning
Data Mining
Machine-generated
Sampling, Multi-variant… All, Hyper space, …
Volume, Velocity, Variety, Veracity
Human-explainable

Models ! Cases
Russ Merz, An Integrated Model of Media Satisfaction and Engagement: Theory, Empirical
Assessment and Managerial Implications, Journal of Applied Marketing Theory, Nov 2011

Models ! Cases
Russ Merz, An Integrated Model of Media Satisfaction and Engagement: Theory, Empirical
Assessment and Managerial Implications, Journal of Applied Marketing Theory, Nov 2011
BIG DATA
Hypotheses
Machine Learning
Data Mining
Machine-generated
All, Hyper space, …
Volume, Velocity, Variety, Veracity
deductive inductive
Cases
Models
Models
Cases

1ET’S STA6T F642 UN0TE-
STATES P6ES0-ENT0A1 E1E,T04N,
016

Predictions in One Thousand of
Years Ago
• 
• 
《gohi》— aUx軾

Range
RADD dataS, B? Fot eOAaLstBMe, Bs aDso RsaEpDBFgS!

Why Big Data?
•  Internet Data
– 4 V
•  Analysis Inference Action
– Data-driven with no people interception
•  Building model programmatically
– Hyperparameters optimization
ALtoEatBoF? PeIsoFaDBPatBoF?

Key Data is the Key
c不在高Vk仙則名。

n不在pVk龍則靈。

s討會中Vzw演zruetd

和v路頻寬flm。

Data Science Venn Diagram
Stat vs. MLDM?
(Big) Programming?
Tags?

© 2015
Segments Reports
For Human
(Explanatory)
Models Data-driven
Actions
Efficiency Intelligence Effectiveness
Data Science is the art of turning data into actions.

© 2015
Unsupervised Learning

© 2015
• Classiﬁca3on
•  These algorithms are used for predic8ng
responses that can have just a few known value
s—such as married, single, or divorced—based on
the other columns in the dataset.
• Regression
•  These algorithms can predict one or more
con8nuous variables, such as proﬁt or loss, based
on other columns in the dataset.

© 2015
• Socioeconomic 8ers
• Income, educa8on, profession, age, number of children, size of city or
residence, and so on.
• Psychographic data
•  Personal interests, lifestyle, mo8va8on, values, involvement.
• Social network graphs
•  Groups of people related to you by family, friends, work, schools,
professional associa8ons, and so on.
• Purchasing paIerns
•  Price range, type of media used, intensity of use, choice of retail
outlet, ﬁdelity, buyer or non-buyer, buying intensity.
Unsupervised Learning

LM LR
Source: http://www.saedsayad.com/logistic_regression.htm

Likelihood

© 2015
Azure ML Studio
•  hps://asiasoutheast.studio.azureml.net/#

© 2015
• ADSC-01-Automobile price - Linear
Regression
• hIp://goo.gl/9l8F1V
• ADSC-02-Mul3-Model and Cross Evaluate
• hIp://goo.gl/Fx1bXA
• ADSC-03-Clustering: Group Iris Data
• hIp://goo.gl/9FmzBY
• ADSC-04-Churn Analysis of a Telcom
Company
• hIp://goo.gl/qC25sU
4 Prac3ces

User-based / Item-based
Recommendation

Matrix = Associations
Rose Navy Olive
Alice 0 +4 0
Bob 0 0 +2
Carol -1 0 -2
Dave +3 0 0
•  Things are associated
Like people to colors
•  Associations have
strengths
Like preferences and
dislikes
•  Can quantify associations
Alice loves navy = +4,
Carol dislikes olive = -2
•  We don’t know all
associations
Many implicit zeroes
Source: Sean Owen(2012), Cloudera

In Terms of Few Features
•  Can explain associations by appealing to underlying features in
common (e.g. “blue-ness”)
•  Relatively few (one “blue-ness”, but many shades)
(Alice)
(Blue)
(Navy)

Losing Information is Helpful
•  When k (= features) is small, information is lost
•  Factorization is approximate
(Alice appears to like blue-ish periwinkle too)
(Alice)
(Blue)
(Navy)
(Periwinkle)

© 2015
ALS Algorithm
•  Optimizing X, Y simultaneously is non-convex, hard
•  If X or Y are fixed, system of linear equations: convex, easy
•  Initialize Y with random values
•  Solve for X
•  Fix X, solve for Y
•  Repeat (“Alternating”)
X
YT

Singular Value Decomposition
A m
=
n
S
k
k• T’
n
m
•Σ

Context aware Matrix
Factorization

Richness
•  Data Model Richness

Src: Fortune, 2016/09
Training with 2 hidden
layers
Training with 4 hidden
layers

Common Data Categories
•  Persona
–  Age, Gender, Birth date, City,
…
•  Attributes
–  Phone brand/model, location,
time, App, browser, banner…
•  Behavior
–  Click, Conversion(Installation,
Cart, Purchase, …),
Activation, Payment…

APP
Targeting Capability
1
2
3

Code Size Comparison
Source: Matei Zaharia(2013)

Performance Comparison

Integrated Framework

Data Sharing in Spark

Example: Logistic Regression

Example: Logistic Regression
•  val data = spark.textFile(...).map(readPoint).cache()
•  var w = Vector.random(D)
•  for (i - 1 to ITERATIONS) {
•  val gradient = data.map(p =
•  (1 / (1 + exp(-p.y*(w dot p.x))) - 1) * p.y * p.x
•  ).reduce(_ + _)
•  w -= gradient
•  }
•  println(Final w: + w)

Logistic Regression Performance

Pricing Engine Framework
Kafka
HDFS
Apache
Spark
Jenkins
Real-time processors
( Flink Streaming)
DataInjection
Speed Layer
Batch Layer
ServingLayer
Kafka
DataStreaming
Couchbase
Docker Container
Avro
Avro
Akka/Scala Actors

Internet as a mass media
“Half the money I spend on advertising is
wasted; the trouble is I don‘t know which half.”
-- John Wanamaker, ~ 1875a pioneer in marketing

Current Challenges
Find the best match between a given user in
a given context and a suitable advertisement.
-- Dr. Andrei Broder and Dr. Vanja Josifovski, Standford University
How to identify?
Limited Info.
Budget? Creative?...
Bid Price?

Channels play different roles
in the customer journey
Source: http://www.thinkwithgoogle.com/

Advertiser Utility: The Value Funnel
CPM campaign:
Expense = N/1000 ⋅CPM
CPC campaign:
Expense = N ⋅ CTR ⋅ CPC
CPA campaign:
Expense = N ⋅ CTR ⋅ CVR⋅ CPA
CPM = 1000 * impressions
CTR = Click# / impression#
CPC = Cost / Click
CVR = CV# / Click#
CPA = Cost / Click
https://www.clickz.com/static/cpa-calculator

Valuation of Ads
∑T
i=1CTR * v(ai, ui) = ∑T
i=1e(ai, ui, ci) =
eCPM
Advertiser:
Media:

How DSP Track Optimize Bidding
•  Pixel/Beacon: landing, browse, shopping cart, conversion …
•  Cookie in web (Cookie mapping)
•  IDFA/AID in mobile
Audience Tracking
•  Feature engineering/Pre-generated tags, Look alike, Re-targeting
•  Privacy - Campaign-based
•  P(c|u)
Audience Selection
•  Campaign/Ad, TA, Creative…
•  Base/up bound price, freq. cap…
Campaign
Mgmt
•  100ms
•  Winning probability function, Traffic forecastingReal-time bidding
•  Click, CTRImpression
•  CVR, CPAConversion

Demand-side platform and its
bidding engine in RTB
Landscape?
Targeting Attr.?
Cold start?

Demand-side platform and its
bidding engine in RTB

Bid Landscape Forecasting
•  Only reference market price by base price
•  Imp, UU, Click, Conv.
•  DSP, per campaign, per targeting criteria…
•  the advertisers’ targeting profiles the
winning bid value
•  Pacing
Source: Ying Cui, Ruofei Zhang, Wei Li, Jianchang Mao(2011), Bid Landscape Forecasting in Online Ad Exchange Marketplace, Yahoo! Labs

Traffic forecasting
•  An impression on Jeremy Lin BBS post of
MiuPTT
•  wo product ads
– A: Linsanity T-Shirt
– B: Baseketball shoes
•  Not optimized if only bid for highest price
– B bid higher than A
– Inventory A is much fewer than inventory B

*
*
C1
*
*
C2
*
B1
C1
*
B1
C2
*
*
*
* *
*
* *
C1
* *
C2
*
B1
*
•  Remove few-imp path for
not too sparse
•  Easily to target all
Target Attribute
-- Bid Star Tree Expansion

Bidding Price Model Building
•  Cold start
•  Training feature of segment and inventory
respectively
•  Do not train combined feature for preventing over
fitting on few training data
φ = p(c|s, i) / p(c|s)
•  Cold start
•  Training feature of segment
AUC: the area under the ROC curve(TP/FP)
Lift: target response divided by average response.
bid = BasePrice(s, a) * p(c|s, I, a) / p(c|s, a)
DSP cross-campaigns:

Bidding Price Calibration
•  Forecasting
– Sampling learn
•  On-line adjustment
– Feedback control Re-learn
•  Loss reason
– Prior Probability Shift
•  Budget, Freq. cap, …
– Competition

4R: Reach, Richness,
Representation, Range
Reach
Richness
High
High
Low
(DAU)
(Behavioral data)
Range
High
( Affiliate of
whole context)
Representation
(Format
Content)

Data Economy
Traditional - Internet Economy
HighREACH
RICHNESS
High
Low
Traditional
Economy
Internet Economy
(quality)
(quantity)

Reach: The Value Funnel
CPM campaign:
Revenue = N/1000 ⋅CPM
CPC campaign:
Revenue = N ⋅ CTR ⋅ CPC
CPA campaign:
Revenue = N ⋅ CTR ⋅
CVR⋅ CPA
UU Reach (DAU)
ARPU = Life-time Value

Richness
Data Quality # Predictive Power

Richness: Predictive Power
APP
Conversions Logs
Behavioral Data Attribution Data

98
Facebook
https://www.facebook.com/ads/preferences/?entry_product=ad_settings_screen

Richness
•  Data Quality Richness
– Attr. vs. behavior
•  Data Utilization Richness
– Call taxi (short vs. long route)
– Download times vs. Activation days
•  Data Model Richness

+3.6
81%85%
TOTAL
(Impression)
- 5
$ 
CTR
3.6
$ 
5
TA
TA TA

Representation
TV campaign
Conversions
Click
Impression
Request
Range
Mobile Campaign Offline Campaign
Reach
Richness
Cross- creen Effect

4R
Cross-screen synergy
Big data synergy ith Cross-screen effect
+TV
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
14.00%
16.00%
0
5000
10000
15000
20000
25000
30000
35000
40000
Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun
APP
App Download Rate Optimized Conversion Rate

Range
- Roger Martin
Rothman School of Management, Toronto
If only attach importance to quantify the business
model, it will not have the ability to find a potential
growth opportunities: The pursuit of quantifying
the biggest problem is that people ignore the
context of the behavior generated, detached from
the context of the event, and have not been
included in the model ignores variables
effectiveness.

Range
•  Google trend, Viral install…

High
4R: Reach, Richness,
Representation, Range
Reach
Richness
High
High
Low (DAU)
(Behavioral data)
Range
( Affiliate of
whole context)
Representation
(Format
Content)

World, Model Theory
Credit: John F. Sowa

BIG
DATA
Humility Humanity

Use Data, not be Used.

Ai plus-digital ads--theory and applications-20170605

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