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Jianqiang (Jay) Wang
Oct 18, 2014
Introduction to Data Science
and Online Advertising
What’s the effect of adding a price tag to each pinterest
pin?
What’s the impact of facebook launching priority-based
news feed on end users?
What is data science workflow
problem / hypotheses
Computer simulation / contrast
data sources
data infra/engineer
Data munging
Skip problem definition & data collection
Data
Transaction
Web clicks and logs
Senor data (satellite, wearable device...)
Docs, emails, social feeds,
...
Techniques
SQL or similar in transactional or analytic database,...
ETL tools in data warehouse
MapReduce in Hadoop,...
Techniques
Distribution & summary statistics: centrality, variation,
outliers
Scatterplot, side-by-side boxplot, histogram
PCA, multidimensional scaling, projection pursuit..
Toolset
Hadoop & equivalents: read terabytes of data and
aggregate
R, python, ruby,excel, …
Exploratory Data Analysis
70% data munging + EDA, 20% modeling, 10% viz &
presentation, reporting
42 heads out of 100 coin flips, does it indicate the
coin is unfair?
Is the traffic on 101-N heavier on Wednesdays?
Techniques
Hypothesis testing
Time series analysis
Toolset
Statistical packages like R
Teasing out signal from noise
Techniques
Regression
A/B testing
Contrast
Computer simulation
Toolset
Statistical packages like R
Experimentation framework
Example
A/B testing: Order news feeds by time vs by priority
Estimate the effects of various
factors
Techniques
Classification
Prediction/forecasting
Recommendation/ranking
Optimization
Toolset
R, Python MLlib, weka (java), VW (C++)…
mahout, spark
Examples
Recommendations
Algorithmic trading
Machine learning, optimization...
simulate / historical data
present vs future value
Differential shelf life
Restaurant procurement, anyone interested?
Demand vs freshness
Ads on twitter platform
Ads serving pipeline
Advertiser campaigns
Supply (platform users) vs demand (advertisers)
Creating your own campaign
Tweet engagement
Followers
App install
Website visits
Lead generation
Targeting
Targeting criteria
Keywords (tweet or tweet engagement)
Interests
Followers : (similar) followers of a handle
Tailored audiences
How to match users to targeting criteria
Interest/age prediction: we don’t ask the users to explicitly
indicate their interests/age but infer them from who they
follow and what they tweet about.
Algorithm & analytics
Interest (NLP), age (classification)
Filtering ad candidates
Campaigns currently active with budget left
Same advertiser/tweet fatigue rules
How many times per week for the same user?
How to make such decisions?
Dismiss/block/spam filters
Click through rate (CTR)
prediction
How likely is the user to ...
Click on the url
Expand the image
Download the app
Online machine learning with 10k+ features
User request and candidate features
Request : user geo, user type, login frequency, interest,..
Ad : advertiser vertical, popularity, tweet content
Model fitting & diagnostics
Ranking
Second price auction on Expected Cost per Impression
(ECPI)
Advertisers bid for engagement (Bid)
Predicated engagement rate (pCTR)
Naïve ranking function : ECPI=Bid * pCTR
Pricing
Minimum bid required to win auction
Winner has (bidCPE1, pCTR1), runner-up has (bidCPE2, pCTR2)
Winner pays paidCPE = bidCPE2 * pCTR2 / pCTR1
Interesting problems
Click through rate of ads against timeline position?
Multiple ads in single request (how many and how to
design the auction?).
How to control campaign pacing?
Two campaigns (target US and entire world).
Delayed clicks or conversions
Questions
We can not teach you passion and attitude, but we will
influence you with our passion and attitude.
Questions
Questions
Questions
Questions
Questions
Questions
Questions
We can not teach you passion and attitude, but we will
influence you with our passion and attitude.
Ads + Exchange
Revenue
Q1 : $255M, Q2: $312M
~20% data licensing; 80% ads
Ads + mobile exchange (Mopub)
Native ads on twitter platform
Exchange: buying and selling ads from multiple ad
networks (broker in financial market)
Big data landscape
Big data landscape
Introduction to data science and its application in online advertising

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Introduction to data science and its application in online advertising

  • 1. Jianqiang (Jay) Wang Oct 18, 2014 Introduction to Data Science and Online Advertising
  • 2. What’s the effect of adding a price tag to each pinterest pin? What’s the impact of facebook launching priority-based news feed on end users? What is data science workflow problem / hypotheses Computer simulation / contrast data sources data infra/engineer
  • 3. Data munging Skip problem definition & data collection Data Transaction Web clicks and logs Senor data (satellite, wearable device...) Docs, emails, social feeds, ... Techniques SQL or similar in transactional or analytic database,... ETL tools in data warehouse MapReduce in Hadoop,...
  • 4. Techniques Distribution & summary statistics: centrality, variation, outliers Scatterplot, side-by-side boxplot, histogram PCA, multidimensional scaling, projection pursuit.. Toolset Hadoop & equivalents: read terabytes of data and aggregate R, python, ruby,excel, … Exploratory Data Analysis 70% data munging + EDA, 20% modeling, 10% viz & presentation, reporting
  • 5. 42 heads out of 100 coin flips, does it indicate the coin is unfair? Is the traffic on 101-N heavier on Wednesdays? Techniques Hypothesis testing Time series analysis Toolset Statistical packages like R Teasing out signal from noise
  • 6. Techniques Regression A/B testing Contrast Computer simulation Toolset Statistical packages like R Experimentation framework Example A/B testing: Order news feeds by time vs by priority Estimate the effects of various factors
  • 7. Techniques Classification Prediction/forecasting Recommendation/ranking Optimization Toolset R, Python MLlib, weka (java), VW (C++)… mahout, spark Examples Recommendations Algorithmic trading Machine learning, optimization...
  • 8. simulate / historical data present vs future value
  • 9. Differential shelf life Restaurant procurement, anyone interested? Demand vs freshness
  • 10. Ads on twitter platform
  • 12. Advertiser campaigns Supply (platform users) vs demand (advertisers) Creating your own campaign Tweet engagement Followers App install Website visits Lead generation
  • 13. Targeting Targeting criteria Keywords (tweet or tweet engagement) Interests Followers : (similar) followers of a handle Tailored audiences How to match users to targeting criteria Interest/age prediction: we don’t ask the users to explicitly indicate their interests/age but infer them from who they follow and what they tweet about. Algorithm & analytics Interest (NLP), age (classification)
  • 14. Filtering ad candidates Campaigns currently active with budget left Same advertiser/tweet fatigue rules How many times per week for the same user? How to make such decisions? Dismiss/block/spam filters
  • 15. Click through rate (CTR) prediction How likely is the user to ... Click on the url Expand the image Download the app Online machine learning with 10k+ features User request and candidate features Request : user geo, user type, login frequency, interest,.. Ad : advertiser vertical, popularity, tweet content Model fitting & diagnostics
  • 16. Ranking Second price auction on Expected Cost per Impression (ECPI) Advertisers bid for engagement (Bid) Predicated engagement rate (pCTR) Naïve ranking function : ECPI=Bid * pCTR Pricing Minimum bid required to win auction Winner has (bidCPE1, pCTR1), runner-up has (bidCPE2, pCTR2) Winner pays paidCPE = bidCPE2 * pCTR2 / pCTR1
  • 17. Interesting problems Click through rate of ads against timeline position? Multiple ads in single request (how many and how to design the auction?). How to control campaign pacing? Two campaigns (target US and entire world). Delayed clicks or conversions
  • 18. Questions We can not teach you passion and attitude, but we will influence you with our passion and attitude.
  • 25. Questions We can not teach you passion and attitude, but we will influence you with our passion and attitude.
  • 26. Ads + Exchange Revenue Q1 : $255M, Q2: $312M ~20% data licensing; 80% ads Ads + mobile exchange (Mopub) Native ads on twitter platform Exchange: buying and selling ads from multiple ad networks (broker in financial market)