Welcome
Overview of Predictive Analy

Claudia Perlich

Chief Scientist, Dstillery
Predictive Modeling:

Algorithms that Learn from 

Example: Micro Loans

Age Income Default35 75K no68 83K yes43 61K no71 56K yes...... ...
Balance

Age

Bad risk (Default) – 16 cases

Good risk (Not default) – 14 cases

Split over age

Split over balance

50K

45

Prob.=4

Balan

<50K 

<4

Default

Defau

Learning to Classify

Classificat

Prob.=12/13

Probability of 
Learning to Classify

Balance

Age

Bad risk (Default) – 16 cases

Good risk (Not default) – 14 cases

50K

45

Logistic Re

p(+|x) = 0.48

p(+|x)=

β0 = 12

β1 = -1
Lending Club Data

• Text• Loan Category• Demographic information• Credit Score

Targeted Online Display Adver
Shopping at one of

our campaign sites

cookies

100 Million URL’s

100 Million Browsers

0.0001% to 1% baserate B

Ap

conversion

AdExchange

Where shouldwe advertise at what price

Does the ad have an effect?

What data should we pay for?

Attribution?

Who should we target fora product?

Which requestare fraud?
The Non-Branded Web

A consumer’s online activity

The Branded Web

gets recorded 

Agnostic Data

I do not want/need to ‘understand’ who you 

Browsing HistoryHashed URL’s:date1 abkccdate2 kkllodate3 88iokdate4 7uiol...Browsing HistoryHashed URL’s:date1 abkccdate2 kkllodate3 88iokdate4 7uiol...

PuEndada...daPuEndada...da
Model in 10 Million Dimensio

Using Naïve Bayes and Stochastic Gradient Decent Logistic Regressiostatistical correlations between 10s of millions of web URLs and 1000s oactions. 

Likelihood to Convert given Visit

Passion

Aversion

non-branded websites

p(buy|urls) =
Ad Ad Ad

Real‐time Scoring of a Brow

Ad

OBSERVATION

Purcha

ProspectRank

Threshold

site visit with positive correlation

site visit with negative correlation

ENGAGEMENT

p(buy|urls) =
Models in Our World

• Spam Detection• Fraud/Fault Detection• Financial Trading• Medial Diagnosis/Quality control• Sentiment Analysis• Prioritization in General• CRM• Recommender systems• Advertising/Targeting
Important Takeaways

• The algorithm is secondary• The data is KEY• Quality control is HARD

• Model is only as good as the modeler• Very difficult to really understand the 
Panel Discussion

• Pamela Dixon, Founder, World Privacy Forum• Edmund Mierzwinski, Consumer Program Directo

Senior Fellow, U.S. Public Interest Research Group

• Claudia Perlich, Chief Scientist, Dstillery• Stuart Pratt, President and CEO, Consumer Data

Industry Association

• Ashkan Soltani, Independent Researcher and Con• Rachel Nyswander Thomas, Executive Director of 

Driven Marketing Institute, and Vice President ofGovernment Affairs, Direct Marketing Association

• Joseph Turow, Professor, University of Pennsylvan
Presentation

Ashkan Soltani

Independent Researcher and

Consultant
twitter: @ashk4n

ashkan.soltani@gmail.com

independent researcher & consultant

whoami
• methodology

• findings• data sources

today: alternative scoring

methodology
user‐agent
older findings: orbitz
findings: orbitz

Some sites, for example, gave discounts based

on whether or not a person was using a mobiledevice. A person searching for hotels from theWeb browser of an iPhone or Android phoneon travel sites Orbitz and CheapTickets wouldsee discounts of as much as 50% off the list

price, Orbitz said.Both sites are run by Orbitz Worldwide Inc.,

which in fact markets the differences as"mobile steals." Orbitz says the deals are alsoavailable on the iPad if a person installs theOrbitz app.
findings: gogo inflight

User‐Agent: Desktop

$12.95

User‐Agent: 

$7.95
location
findings: staples
findings: staples
Home Depot's website offered

price variations that appeared tobe based on the nearest brick‐and‐

mortar store as well. A 250‐footspool of electrical wiring fell intosix pricing groups, including$70.80 in Ashtabula, Ohio; $72.45

in Erie, Pa.; $75.98 in Olean, N.Y

and $77.87 in Monticello, N.Y.

findings: more geography

Location also seemed to be imsome international companies

saw Rosetta Stone, which sells 

learning languages, offering di

much as 20% for people who b

levels of its German lessons fr

locations in the U.S. or Canada

from the U.K. or Argentina.
findings: discover

In the tests, Discover, for instance, showed a prominent offer fo

the company's new "it" card to computers connecting from citincluding Denver, Kansas City, Mo., and Dallas, Texas. Compute

connecting from Scranton, Penn., Kingsport, Tenn., and Los Angdidn't see the same offer.A Discover spokeswoman said that the company was testing th

card, but that for competitive reasons, it wouldn't comment fu

on its "acquisition strategy" for new customers.
findings: staples

In the Journal's examination of Staples' onlinepricing, the weighted average income among

ZIP Codes that mostly received discount prices

was roughly $59,900, based on InternalRevenue Service data. ZIP Codes that sawgenerally high prices had a lower weighted

average income, $48,700.

higher income = lower price
profiles*
findings: nextag / shoplet
findings: nextag / shoplet
Capital One was showing different users different cards first—

either those for "excellent credit" or "average credit."

findings: capital one
findings: capital one
data sources
data sources
data sources
data sources
data sources
conclusion
conclusion: staples

As

a final test, the Journal ordered two

separate Swingline staplers from Staples.com,

from two nearby ZIP Codes—one costing

$14.29 and the other one $15.79. The staplersarrived the same day. They appear to be

indistinguishable from one another and do an

equally thorough job of stapling.
Panel Discussion

• Pamela Dixon, Founder, World Privacy Forum• Edmund Mierzwinski, Consumer Program Directo

Senior Fellow, U.S. Public Interest Research Group

• Claudia Perlich, Chief Scientist, Dstillery• Stuart Pratt, President and CEO, Consumer Data

Industry Association

• Ashkan Soltani, Independent Researcher and Con• Rachel Nyswander Thomas, Executive Director of 

Driven Marketing Institute, and Vice President ofGovernment Affairs, Direct Marketing Association

• Joseph Turow, Professor, University of Pennsylvan