Bid Optimization and Inventory Scoring · • down-sample negatives • 50K test set for performance • same training set for both, just one less feature • correct predictions

© 2012 Media6Degrees. All Rights Reserved. Proprietary and Confidential

Bid Optimization and Inventory Scoring

Claudia Perlich, Brian Dalessandro, Rod Hook,

Ori Stitelman, Troy Raeder, Foster Provost

Media6Degrees

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Shopping at one of our campaign sites

cookies

100 Million

URL’s

100 Million

Browsers

0.0001% to 1%

baserate Billions of

Auctions

per day

conversion

Ad Exchange

Where should

we advertise and

at what price?

Does the ad

have an effect?

Can we group

content?

Data Quality

Management?

Attribution?

Who should

we target for

a product?

M6D Display Advertising in a Nutshell


Life of a Browser

3

1. Initiate: create Cookie when the browsers first comes across one of our data partners

2. Monitor:

track browsing activity in the scope of our data

partners

track ‘brand actions’ for our advertisers


The Non-Branded Web

BrowserId: 1234

URLId:Type abkcc:SN

kkllo:blog

88iok:SN

7uiol:twitter

Cookie stats user agent

date of first seen

# of interactions

Purchases 3012L20 4199L30 … 3075L50

A consumer’s online activity

The Branded Web

gets recorded like this:

Monitor: This is what we see

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Life of a Browser

5

1. Initiate

2. Monitor

3. Score and Segment


Media

Traditional Approach

Use web browsing behavior to model

consumers into buckets, then sell those

buckets to advertisers. This is a three-

model process that is largely a legacy

of offline marketing science.

Use web browsing behavior to model

direct associations between media and

brands. This is a one-model process that

is only possible because of innovations in

web-scale technology.

Advances in web-scale data and technology require a new approach

to audience selection.

Brand Media Brand

Computational Advertising

Descriptive Buckets Description Not Necessary

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Modeling Your ‘Brand Affinity’/ Prospect Rank

Using Bayesian Statistics and Stochastic Gradient Decent Logistic Regression, we

estimate statistical correlations between 10s of millions of web URLs and 1000s of

branded actions.

The output of this process is the basis of our ProspectRank scoring

Lik

elih

oo

d t

o C

on

ve

rt g

ive

n V

isit

Passion

Aversion

non-branded websites

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Brand Interest

Ad Ad Ad

Category Awareness

Dynamic Scoring and ‘Segmenting’

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Ad

m6d ProspectRank

OBSERVATION

Achieves

ProspectRank

Visits Branded

Site

ProspectRank

Threshold

site visit with positive correlation

site visit with negative correlation

ENGAGEMENT

Some prospects fall

out of favor once their

in-market indicators

decline.

Sudden spikes in prospect rank

suggest in-market behavior


Summary

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• We have ‘segments’ of good

prospects (top 1 percentile) that

come out of a zoo of predictive

models in millions of dimensions

that we cannot really explain to

anybody


Life of a Browser

10

1. Initiate: create Cookie

2. Monitor

3. Score and Segment

4. Sync with the exchange

5. Activate ‘Segment’

6. Receive Bid request

7. Bid

8. Show Impression

9. Track Conversion

10. Cycle ….

11. Cookie Deletion


Real Time Bidding

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Modern Display Advertising:

Ad-Exchanges and Real Time Bidding

• Ad Exchange

Marketplace that brings together supplier of inventory - places to show ads and advertisers wanting to show ads

• Real Time Bidding

second price Vickrey auction

some strange rule that nobody knows

15 ms to submit a bid (not much room for deliberation …)

• Some M6D Numbers

# of bid request per day: ~3 Billion

# of bids: ~200 Million

# of ads: ~35 Million

# of exchanges: ~20

# of unique cookies we see per day: ~56 Million

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What exactly is Inventory?

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Where the ad will be shown: 7K unique inventories + default buckets


Why should the inventory matter?

Causal Impact on

Conversion Propensity

• Perceptiveness

• Inventory Quality

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Information about the Organic

Conversion Propensity

• Contextual Relevance

• Current Intention

• Cookie Life Expectation

The inventory tells us what the browser is doing RIGHT NOW

How do we measure this and how do we incorporate this into our targeting and bidding?


What does it mean anyway: Bid-Optimization?

Some musing …

Optimal?

o Vickrey suggest that you bid what it is worth …

o We do not really know what a conversion is worth …

Why separate the bid vs. no bid from the bid price?

o Inventory effect is probably fairly consistent across good prospects

o Targeting happens BEFORE we run media, inventory only later

o I do not have a lot of positives so I need to keep the dimensionality down

…

Why do we need a model at all: can’t we just measure it?

o Segments have different quality and trafficking decisions introduce

confounding

Other considerations: frequency, volume, attribution/timing

Interaction effects between campaigns …

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Inventory Model Estimation

Measure how much better a single piece of inventory i is than the

‘average’ inventory j for campaign a over all browsers u

Build separate models for each campaign a

Need to control for the trafficking decisions of the different segment s,

not really for the user u (speed up scoring time)

Instead of integrating the expectation separately, we just estimate it by

removing that features!

),|(),,|( iucpaiucp a

),|(),|( iscpiucp aa


Campaign Properties in Empirical Comparison

DATA

• 100 active campaigns

• 3 weeks of impressions

• conversions within 7 day

ESTIMATION DETAILS

• L1 constrained logistic

• down-sample negatives

• 50K test set for performance

• same training set for both, just one less feature

• correct predictions for down-sampling

• feature selection: online include i with 5 positives in expectation

• keep all instances for calibration

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Out of Sample Model Performance

Does the inventory carry ANY information?

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Example of Model Scores for Hotel Campaign

• Scores are calculated on de-duplicated training

pairs (i,s)

• We even integrate out s

• Nicely centered around 1

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Bidding Strategies

Strategy 0:

• always bid base price for segment

• equivalent to constant score of 1 across all inventories

• consistent with an uninformative inventory model

Strategy 1:

• auction-theoretic view: bid what it is worth in relative terms

• So we multiply the base price with ratio

Strategy 2:

• optimal performance is not to bid what it is worth but to trade of value for quality and only bid on the best opportunities

• apply a step function to the model ratio to translate it into a factor applied to the price:

ratio below 0.8 yields a bid price of 0 (so not bidding),

ratios between 0.8 and 1.2 are set to 1 and ratios above

1.2 bid twice the base price

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1


Impact on Life Campaigns

We either want better conversion at constant margin or higher margin at constant performance (we are typically paid on CPM)

Conversion Rate (PVSVR) strategy 2 should do well Percentage of impressions leading to site visits within 7 days.

Higher conversion rates are better.

Cost per Acquisition (CPA) strategy 1 should do well This metric combines cost and conversion rate and looks at the total

cost of impressions for a given strategy relative to the total number of conversion.

Lower CPA is better.

Potential Unintended Effects:

cross impact on other campaigns

delivery

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Performance Results of Bidding

Experiments:

• 15 campaigns in the initial trail

• 2 weeks of data for evaluation

• A/B test where we run in parallel all 3 strategies on random

subsets of browsers

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Comparison Type S1 vs. S0 S2 vs. S0

Conversion Prospects +6% + +21% +

Retargeting +3% +24% +

CPA Prospects +1% +18% +

Retargeting - 2% - 4% +


Closing Thoughts/Future work

• Bid optimization: Include frequency and attribution (in production)

• Data incest: By bidding more we are winning better browsers and it becomes a self-fulfilling prophesy

• Integrate the continuous targeting score into the ‘value’ assessment of the impression in addition to the inventory

• Measure cross-campaign impacts: we recently switched over all campaigns

• Pacing: if you know there are many good opportunities available for one campaign and few for the other, the second should get precedence

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Acknowledgement

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Data Science/Tech team

Foster Provost

Brian Dalessandro

Troy Raeder

Ori Stitelmann

Other Stuff we do in computational advertising

Paper on privacy preserving targeting at KDD 2009

Paper on attribution at KDD 2012

Paper on large scale machine learning in advertising at KDD 2012

Paper on observational methods to measure adfx at KDD 2011

Paper on clicks and alternative proxies submitted to JAR

Documents

Bid Optimization and Inventory Scoring · • down-sample negatives • 50K test set for performance • same training set for both, just one less feature • correct predictions