cikm 2013 tutorial: real-time bidding: a new frontier of computational advertising research

Real-Time BiddingA New Frontier of Computational Advertising Research

Jun Wang and Shuai Yuan, University College London

Xuehua Shen, iPinyou

Samuel Seljan, AppNexus

About us

• Dr Jun Wang and Shuai Yuan from University

College London

– Media future research group

– Computational advertising (big data analytics

and web economics)

• Dr Jun Wang is a Senior Lecturer (Associate

Professor) of Department of Computer Science,

UCL

• Shuai Yuan is Jun’s PhD student in CRS

(completing research status)

2

About us contd.

• Dr Xuehua Shen is CTO and co-founder of

iPinYou

– He received his PhD of Computer Science at

University of Illinois at Urbana-Champaign,

USA

iPinYou is the largest Demand Side Platform (DSP) and the leader of audience

targeting and real-time advertising in China. It makes intelligent decision for more

than 3 billion ads impressions each day. In the past two years, iPinYou is the pioneer

of programmatic buying of display media in China and organizes the annual RTB

Summit. It has more than 150 employees and is headquartered in Beijing and has

offices in Shanghai, Guangzhou, and Silicon Valley.

3

About us contd.

• Dr Samuel Seljan is a Quantitative Analyst at AppNexus

– Supply-side optimization

• to improve the allocation of impressions across RTB and non-RTB markets

• reserve price optimization

– He obtained a PhD in Political Science from the University of California, San

Diego

• AppNexus is one of the largest real-time advertising platforms (exchanges)

– Offers one of the most powerful, open and customizable advertising technology

platforms

– Serves Microsoft Advertising Exchange, Interactive Media (Deutsche Telekom),

and Collective Exchange

4

Outline• The background of RTB (25min)

– history, glossaries, fundamental challenges, players and their objectives

• An empirical study of RTB auctions (15min)

– periodic features, bids’ distribution, daily pacing, frequency & recency control

• Demand side optimisation (40min)

– bidding algorithms, conversion attribution

• The iPinyou global bidding algorithm competition (30min, break 30min)

– results, prizes, how to participate

• Supply side optimisation (40min)

– ad density, reserve prices, revenue channel selection, bid landscape

forecasting, pricing guaranteed delivery, data leakage

• Financial methods in computational advertising (15min)

– game theory basic, ad options

• Panel discussion (15min)5

Part 1: The background of RTB

• Egyptians used papyrus to make sales

messages and wall posters (4000 BCE)

• In the 18th century, ads started to appear

in weekly newspapers in England

• Thomas J. Barratt has been called "the

father of modern advertising"

1806 1890

1900

courtesy of Wikipedia 6

1952

Ads can be not annoying

7courtesy of lostateminor.com

Glossaries

• Real-Time Bidding is an important aspect of Programmatic buying, which is

getting more and more popular in Display (related) advertising. Another

major part of Online advertising is Sponsored search

• An Impression is an ad display opportunity which generates when a User

visits a webpage containing ad Placements

• The Publisher sends a bid request of this impression to an Ad network, or an

Ad exchange via his Supply side platform (SSP), then to Demand side

platforms (DSP) to reach Advertisers

• Usually, DSPs contact Data management platform (DMP) to check the

Segments of the current user, i.e., his intents or interests. Then a bid will be

computed for the Campaign

• The payment among these entities is usually in Cost per mille (CPM), but

sometimes could be Cost per click (CPC) or Cost per acquisition (CPA)

• If the advertiser wins the impression, his Creative will be displayed to the

user8

The fundamental challenges

• To find the best match between a given user in a given context and a

suitable advertisement?

• To achieve the best campaign performance (e.g., ROI) within the budget

constraint?

• To generate the most revenue given the traffic and demand?

• To maintain a healthy environment so that users get less annoyed (both

quality and quantity)?

Computational advertising, AZ Border, 2008Dynamics of bid optimization in online advertisement auctions, C Borges et al. 2007Dynamic revenue management for online display advertising, G Roels and K Fridgeirsdottir, 2009Advertising in a pervasive computing environment, A Ranganathan and RH Campbell, 2002 9

The simplified history of online (display) advertising

Real-time Bidding for Online Advertising: Measurement and Analysis, S Yuan et al., 2013 10

Direct sales

27th Oct 1994, AT & T on HotWired.com(78% CTR)

• Advertisers and publishers talk to (4A) agencies

• Still popular in today’s marketplace

courtesy of Ad Age 11

Trading in ad networks

courtesy of Admeld

Why?• After direct sales, some impressions

will remain unsold (remnants)• Small publishers cannot find buyers

directly

Ad networks are first-level aggregators of (long-tail) demand and supply.

12

Introducing the ad exchange

courtesy of www.liesdamnedlies.com

single ad network is easy a few ad networks are manageable

hundreds of ad networks are nightmare13

A video

courtesy of Internet advertising bureau, src: http://www.youtube.com/v/1C0n_9DOlwE 14

Introducing the ad exchange contd.

• Ad exchanges are

marketplaces

• Advertisers and

publishers have to rely

on tools to connect

• Real-Time Bidding

promotes user-oriented

bidding

Ad exchanges are second-level aggregators of demand and supply

15

The complex display ad eco-system

courtesy of LUMAscape 2011 16

A new picture in 2013

courtesy of Rare Crowd

AggregatorsDemand side Supply side

17

Boundaries are getting blurry

courtesy of Google

Google is introducing display ads to search result pages

18

Introducing the Demand Side Platform (DSP)

courtesy of LUMAscape 2011

• To connect to ad exchanges and SSPs

• To buy user-data from DMPs

• To provide campaign management

functions

• To bid by targeting rules and

optimisation algorithms

• To report and analyse the performance

A demand side platform (DSP), also called buy side optimizer and buy side platform is a technology platform that provides centralized and aggregated media buying from multiple sources including ad exchanges, ad networks and sell side platforms, often leveraging real time bidding capabilities of these sources.

IAB Wiki

19

DSP contd.

Bidding algorithm is the core of a DSP

20

Introducing the Supply Side Platform (SSP)


• To upload advertisements and rich media

• To traffick ads according to differing business

rules

• To target ads to different users, or content

• To tune and optimise

• To report impressions, clicks, post-click &

post-impression activities, and interaction

metrics

A sell side platform (SSP), also called sell side optimizer, inventory aggregator, and yield optimizer is a technology platform that provides outsourced media selling and ad network management services for publishers.

IAB Wiki

21

SSP contd.

Yield optimisation is the core of a SSP

22

Introducing the Data Management Platform (DMP)

• To collect users’ online behaviour data across

websites

(Mainly via 3rd party cookies)

• To predict users’ segments (intents/interests)

bases on online behaviour data

• To answer the query of users’ segments

• To provide audience profiling and expansion

services


A Data Management Platform (DMP) is a system that allows the collection of audience intelligence by advertisers and ad agencies, thereby allowing better ad targeting in subsequent campaigns.

IAB Wiki

23

DMP contd.

The user base and the learning engine are the cores of a DMP

24

Behind the banner

cmsummit.com/behindthebanner

Behind the banner

(A visualization of the adtech ecosystem)

Adobe, 2013

25

http://cmsummit.com/behindthebanner/

Part 2: An empirical study of RTB

• To understand the bidding behaviours in RTB auctions

• To present some research challenges

• To help to get familiar with RTB in the real-world

• The data is from production DSP & SSP based in UK

– 52m impressions, 72k clicks, and 37k conversions from Feb to May 2013

• Started from convs/clicks and back-traced to imps

– 12m auctions from 50 placements from Dec 2012 to May 2013

• 16 websites of different categories

26

Periodic patterns

The numbers of imp (left) and click (right) both show strong daily and weak weekly patterns,corresponding to the normal human activity

27

Periodic patterns contd.

Daily periodic patterns for conv (left) and cvr (right) show thatpeople are less likely to convert during late night

28

Frequency distribution

The frequency against CVR plot from two different campaignsCampaign 1 sets a frequency cap of 2-5 -> poor performanceCampaign 2 sets a frequency cap of 6-10 -> waste of budget

29

Recency distribution and conversion window

The recency factor affects the CVR (left)Campaign 1 sets a long recency cap -> waste of budgetCampaign 2 sets a short recency cap -> poor performance

The wide conversion window (right) challenges attribution models

30

Periodic patterns contd.

The winning bids peak at 8-10am due to intensive competition

31

Level of competition(number of bidders)

Change of winner

The more bidders, the higher chance of winner change, which makes it harder to detect a dynamic reserve price

32

Bids’ distribution

Accepted (p>0.05) Rejected

AD test per auction 0.343 0.657

AD test per placement 0.000 1.000

CQ test per auction 0.068 0.932

The commonly adopted assumption of Uniform distribution or Log-normal distribution

were mostly rejected

• Anderson-Darling test for Normality

• Chi-squared test for Uniformity

Finding the best fit of bids’ distribution is important:

• Optimal reserve price

• Bid landscape forecasting

• etc.

And what’s the granularity? (placement, geographical location, time & weekday, etc.)33

Budgeting and daily pacing

Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising, KC Lee et al., 2013 34

Budgeting and daily pacing

35

A mixture of 1st and 2nd price auctions

• A high soft floor price can make it 1st price auction(In RTB, floor prices are not always

disclosed before auctions)

• In our dataset, 45% 1st price auctions consumed 55% budgets

• The complicated setting puts advertisers in an unfavourable position and could damage the ad eco-system

36

Overview: references

• The History of Advertising: How Consumers Won the War for Their Attention, HubSpot, 2013blog.hubspot.com/the-history-of-advertising-war-for-consumer-attention-slideshare

• How Cluttered Is the Advertising Landscape, Really? HubSpot, 2013blog.hubspot.com/how-cluttered-is-advertising-landscape-timeline

• Navigating Planet Ad Tech, MIT Technology Review, 2013www.technologyreview.com/view/518551/the-evolution-of-ad-tech/

• Internet Advertising: An Interplay among Advertisers, Online Publishers, Ad Exchanges and Web Users, S Yuan et al., 2013arxiv.org/abs/1206.1754

• Ad exchanges: research issues, S Muthukrishnan, 2009sites.google.com/site/algoresearch/start2.pdf

• Behind the banner (A visualization of the adtech ecosystem), Adobe, 2013cmsummit.com/behindthebanner/

37

http://blog.hubspot.com/the-history-of-advertising-war-for-consumer-attention-slideshare

http://blog.hubspot.com/how-cluttered-is-advertising-landscape-timeline

http://www.technologyreview.com/view/518551/the-evolution-of-ad-tech/

http://arxiv.org/abs/1206.1754

http://sites.google.com/site/algoresearch/start2.pdf

http://cmsummit.com/behindthebanner/

Part 3: Demand side optimisation

• Bid optimisation

• Conversion attribution

38

Bid optimisation

• Input

– logs for auctions, impressions and

events

– targeting rules

– budgets and pacing preference

– internal/external user data

• The Decision Engine

– Gradient Boosting Regression

decision Tree, etc.

– fast & scalable

• Output

– to bid or not

– how much

39

Bid optimisation

• Baseline (constant or random, for exploration)

• Simple rule based (to bid high if the return is high)

– bid = base * pred_CTR / avg_CTR

– bid = conv_value * CVR * ROI

• Regression for estimation

– Generalised linear regression models (logistic, Bayesian probit, FTRL-Proximal,

etc.)

– Tree based models (random forest, gradient boosting regression tree, etc.)

– Neural networks and deep learning

40

Bid optimisation

• Looks good, but…

– metrics for evaluation?

– exploration vs.. exploitation (esp. for cold-start campaigns)

– risks (variance or confidence intervals from estimation)

– practical constraints (branding, overspending risks, inconsistent billing units, etc.)

• E-E problem

– Interactive collaborative filtering

– dimension deduction, correlation, etc.

• Risks

– Defining the Utility as the objective

– Portfolio theory

41

Metrics

• Top funnel metrics (to gain brand awareness)

– brand recall (awareness uplift)

– branded search

– direct website traffic

• Mid funnel metrics (to educate and engage the prospects)

– cost per new website visitor

– page view & form uplift

• Bottom funnel metrics (to generate value both online and offline)

– total conversion

– cost per conversion

– opportunity contribution (interested but not converted yet)

– revenue

courtesy of Adexchange 42

Transfer learning

• The Problem

– CTR is no good metrics but CVR is

too low

• Task

– To train on site visits

• Challenge

– Which site visits, and weight?

– Data availability

• Solution

– Similarity (contextual as a priori,

Bayesian)

Evaluating and Optimizing Online Advertising: Forget the Click, But There are Good Proxies, B Dalessandro, 2012 43

Conversion attribution problem

Dr Samuel Seljan from AppNexus

44

AppNexus

• AppNexus – Open and customizable advertising technology platform

• Process over 50 Billion ad requests per day

• Allow buyers to buy from over 90% of the web, including Facebook

• Clients are advertisers, ad agencies, content providers, and ad networks

• Major clients include:

• Microsoft

• Netflix

• eBay

• Zynga

• Interactive Media

• Orange - European telecom/media conglomerate

• WPP - world’s second largest ad agency

45

Why advertise?

“Done well, advertising sends a whisper to your

impulses – a primal wind at the back of your neck,

suggesting where to go and what to do.”

-Don Draper

46

Fundamental question of advertising

• But, does advertising work?

• Which ads work with what audience?

• Do the benefits of advertising outweigh it’s costs?

– Incremental revenue > Marginal cost?

47


• The promise of digital advertising: precise measurement of users’ responses

to ads

• 1990s: Click tracking

• Compare CTRs across many, many dimensions:

• Campaign, image, time of day, region, location on page, gender, etc.

• Limitations?

48


• The promise of digital advertising: precise measurement of users’ responses

to ads

• 1990s: Click tracking

• Compare CTRs across many, many dimensions:

• Campaign, image, time of day, region, location on page, etc

• Limitations?

• Late 2000s: Conversion tracking

• Conversion: when a user sees and ad and then takes an action, e.g. buys a pair or shoes

• Cost Per Action (CPA) payment: advertisers only pay when a conversion occurs

• Traders (agents of buyers) or sellers take on all the risk

• Does this answer the fundamental question of advertising?

49

Problems with CPA advertising

1. Users often see many ads for the same brand on many sites

2. Only includes online actions

3. Causal inference – what does the association between seeing an ad and

converting mean?

4. Ration of conversions per ads is often very small

– Between 1-5 conversions per 100,000 impressions is common

– Thus, takes many ads to learn “true” conversion rate.

• For a one month campaign with a $5,000 budget, possible to learn conversion rate with

5% error on roughly 10 different web sites!

– Difference between 1 and 5 conversions per 100,000 impression is difference

between a profitable and unprofitable campaign

50


1. Users often see many ads for the same brand on many sites

• Which ad “is responsible” for the conversion?

• Industry standard is “last touch” attribution

– Previous graph is misleading!

– Doesn’t show how many times a user saw ad

• Connection to frequency optimization: creates a bias towards higher frequency ads

– Last touch is the most recent ad, but we don’t know for sure that user even saw the

most recent ad – we just have a record of it

– If effect of seeing ad is cumulative, this under weights importance of first view

51


1. Users often see many ads for same brand on many sides

– Which ad and site gets credit for the conversion?

2. No tracking of offline purchases

– Technological and privacy challenges

• 3. The fundamental problem of causal inference

(Ad tech version)

Incremental revenue from ad j for user i =

Lifetime revenuei | i sees j– Lifetime revenuei | i does not see j

– But, each user either sees or does not see an ad so this cannot be calculated, even with

limitless data!

– Moreover, advertisers target users that are more likely to buy! (retargeting), thus lifetime

revenuei | i sees j is very likely to overestimate incremental revenue.

• Most CPA optimization creates selection bias


• Alternative framing: most CPA buying algorithms are predicting who is most

likely to buy and then focusing (targeting) buying on these users

• Thus, advertisers may see a lot of conversions associated with ads on some sites

on some users, but do not know how much revenue they would have had without

those ads

• Example: amazon targets ads at people who have recently searched for an item

on their website.

• These people are more likely to buy on Amazon than those who have not recently

searched

• But, they are also more likely to buy without seeing an ad


• Problem is more important for some brands than others

• For internet advertising, the potential scale of the problem can be considered by

thinking about:

Lifetime revenuei | i does not see digital ad

• For what types of campaigns is this likely to be a big problem? A small problem?

• Principal agent problem: the people that need to understand this are the

brands themselves

• Agents buying for brands, do not have a short term incentive to solve problem

– they get paid per conversion!

• Selling more rigorous CPA optimization to brands is challenging

54

Solutions to problems with CPA advertising

Problem 1: Many ads per conversion

• AppNexus Solution 1: custom conversion attribution

• We track conversions, but allow clients to divide conversion among ads

using their own “secret sauce”

• AppNexus Solution 2: Conversion Funnel

• Use events higher up the funnel to predict final sale

56

Problem 2: Offline tracking

• AppNexus Solution 1: allow for integrations with offline data providers and

the insertion of external data into optimization

• Other solutions?

• A privacy quid pro quo? Data for savings?

57

Problem 3: Fundament Problem of Causal Inf.

• AppNexus Solution: Random assignment

• Randomly assign users to group A (sees ad) or B (doesn’t see ad)

• Estimate incremental revenue from ad as:

conversion value * p(Conversion |A ) – p(Conversion | B)

– limitations: scalability, does not include off-line revenue or model social returns

• This is a problem for both group A and group B for larger brands

58


• Gold standard:

• Fowler et al. (2012): Facebook “I voted” and turnout

– RA to three groups:

» 1. Top panel

» 2. Second panel

» 3. No message

• Used federal data to compare turnout rates in groups & friends of those in each gorup

– .4% higher turnout of second gorup

• Combines solutions 2 and 3 (randomization addresses problems 2 and 3!)

59


• Remaining questions for RA: how to combine RA with other elements of a

CPA optimization algorithm…

– Over how many groups should one randomize?

» E.g. it could solve the frequency – conversion attribution problem, but that’s

a lot of groups

– What percent of impressions should be in test and control group?

60

Demand side optimisation: references

• Optimal bidding on keyword auctions, B Kitts and B Leblanc, 2004

• Stochastic gradient boosted distributed decision trees, J Ye et al., 2009

• Web-scale bayesian click-through rate prediction for sponsored search advertising in Microsoft's Bing search

engine, T Graepel et al., 2010

• Web-search ranking with initialized gradient boosted regression trees, A Mohan et al., 2011

• A Gentle introduction to random forests, ensembles, and performance metrics in a commercial system, D

Benyamin, 2012

citizennet.com/blog/2012/11/10/random-forests-ensembles-and-performance-metrics/

• A 61-million-person experiment in social influence and political mobilization, RM Bond et al., 2012

• Deep metworks for predicting ad click through rates, G Corrado, 2012

• Click modeling for display advertising, O Chapelle, 2012

• Causal reasoning and learning systems, L Bottou and E Portugaly, 2012

• Ad click prediction: a view from the trenches, HB McMahan et al., 2013

• Deep learning, yesterday, today, and tomorrow, K Yu et al., 2013

• Deep learning of representations: looking forward, Y Bengio, 2013

61

http://citizennet.com/blog/2012/11/10/random-forests-ensembles-and-performance-metrics/

iPinyou global bidding algorithm competition

Dr Xuehua Shen from iPinyou

62

Best Algorithm

Maximize #clicks + N * #conversions

Subject to the fixed budget

3 Milestones +

1,000,000

Grand Prize

iPinYou Contest vs. Netflix Prize

Open Question

Offline & Online Evaluation

Production setting

Meaningful metrics

Dynamic data set

Short time span

7.5Gbid: 13.6Mimp: 9.2Mclk: 7.5Kconv: 72

Dropbox: https://www.dropbox.com/sh/xolf5thu8jsb

mfu/kBrAsSxtAN

百度网盘: http://pan.baidu.com/share/link?shareid=

374646&uk=3037373637

https://www.dropbox.com/sh/xolf5thu8jsbmfu/kBrAsSxtAN

http://pan.baidu.com/share/link?shareid=374646&uk=3037373637

April 20 May 3 May 16

2000+

submissions

05.16 ~ 05.22 Warmup

05.23 ~ 05.25 Bidding

ml_rush, the9thbit, newline

Not just CTR

June 1 ~ August 31 Offline

Sept 1 ~ Sept 30 Online

Sept 6 ~ Sept 12 Warmup

Sept 13 ~ Sept 15 Bidding

Season 2 vs. Season 1

Support Python, R, Java

Competition platform

Top 5 go to Online stage

More bonus

Team membership (6 to 10)

2x DataUser Profile

July 12July 5 Aug 31 Aug 31’

4000+

submissions

Bidathon 2

RankTeam name

#Bid #Imp #Clk#Con

Spending

Win Rate

CTRFinal Score

1 o_o 50,874,976 4,658,979 4,265 4 1497.55 9.2% 0.092% 4289

2梦想照进现实

79,426,606 7,196,004 4,061 1 1481.59 9.1% 0.056% 4067

3 UCL-CA 3,597,284 2,059,509 1,989 1 1500.01 57.3% 0.097% 1995

4 Again 21,099,070 3,503,178 1,397 2 1499.99 16.6% 0.040% 1409

5 deep_ml 4,682,521 1,886,929 1,142 1 1500.08 40.3% 0.061% 1148

Bid More, Bid

Less

Season 3Oct 1 ~ Nov 15 Offline

Nov 15 ~ Dec 15 Online

Season 3 vs. Season 1/2

Even bigger data set

Focus on Online Stage

Mobile Campaign

Any question sent to

[email protected]

全球RTB算法大赛

320076711

iPinYou Global RTB Bidding

Algorithm Competition

http://q.weibo.com/1909328

http://www.linkedin.com/groups?home=&gid=4973527&trk=anet_ug_hm

Part 4: Supply side optimisation

• Typical revenue models

• Ad density optimal control

• Reserve price optimisation

• Ad channel selection

• Connecting the supply side markets

• Data leakage protection and pricing

103

Typical revenue models for the supply side

• Subscription access to content (FT.com)

• Pay Per View access to document (Downloading a paper outside the campus)

• CPM display advertising on site

• CPC advertising on site (Google AdSense)

• Sponsorship of site sections or content types (typically fixed fee for a period)

• Affiliate revenue (Compare shopping, CPA/CPC)

• Subscriber data access for marketing (VISA & MasterCard)

• User contributed data for marketing (Surveys)

Publishers will seek to use the best combination of these techniques

104

Ad density

The task:

• To find the optimal advertising

density (number of ad placements)

for a given website

The challenges:

• Users’ preference model

• Expected CPM

• Competition

The assumption:

• Using real-time bidding only

105

No ads

courtesy of www.gov.uk

Some websites do not rely on

ads to compensate the

maintenance cost

• Government

• Education

• Most of .org

106

All ads

• Created by Alex Tew in

2005

• Selling 100k 100-pixels

at $100 each

• Sold out in 4 months

• Almost 0% CTR

courtesy of www.milliondollarhomepage.com 107

Ad density: example

Apr 2010Sep 2013

courtesy of archive.web.org 108

Ad density: example

courtesy of Quantcast

Question: is it reasonable to put on so many more ads?

109

Ad density: an optimal control problem

• Assumptions

– Ad density and impressions determine revenue

– Ad density determines impressions

Ad density

Impressions (page views)CPM

Maintenance cost factor

Content attraction Ad repellence Natural growth

Management and valuation of advertisement-supported web sites, Dewan et al. 2005

Question: what’s the optimal densities of multiple publishers under competition?

110

Reference: ad density, layout & pricing

• Management and valuation of advertisement-supported web sites, RM Dewan, 2003

• Optimal pricing and advertising policies for web services, S Kumar et al., 2004

• Is revamping your web site worthwhile? EY Huang, 2005

• An economic analysis of ad-supported software, BJ Jiang, 2007

• Dynamic pricing and advertising for web content providers, S Kumar and SP Sethi, 2009

• Pricing display ads and contextual ads: Competition, acquisition, and investment, YM Li and JH Jhang-Li, 2009

• Dynamic ad layout revenue optimization for display advertising, H Cheng et al., 2012

• Automatic ad format selection via contextual bandits, L Tang et al., 2013

111

Reserve price optimisation

The task:

• To find the optimal reserve

prices

The challenge:

• Practical constraints v.s

common assumptions (bids’

distribution, bidding private

values, etc.)

The assumptions:

• 2nd price auction

• With only hard floor price

Even in the 2nd price auction, the winner does not always pay the 2nd highest bid (or minimal + $0.01)

112

Reserve price: flowchart

Suppose it is 2nd price auction

• Normal case: b2 > a• Preferable case: b1 > a > b2• Undesirable case: a > b1

113

• 2 bidders, Uniform[0, 1]

• Without a reserve price

• With the optimal auction theory

Reserve price: example

Reserve prices in internet advertising auctions: A field experiment, Ostrovsky and Schwarz, 2011 114

Reserve price: the optimal auction theory

• In the 2nd price auctions, advertisers bid their private values [𝑏1, … , 𝑏𝐾]

• Values are independently distributed and drawn from certain distributions

– Uniform

– Log-normal

• The publisher also has a private value 𝑉𝑝

• The optimal reserve price is given by

Questions:

• Are advertisers bidding their private values?

• Does Uniform/Log-normal fit well?

Optimal Reservation Prices in Auctions, Levin and Smith, 1996

𝐹 𝒃 = 𝐹1 𝑏1 ×⋯× 𝐹𝐾(𝑏𝐾)

𝛼 −1 − 𝐹 𝒃

𝐹′ 𝒃− 𝑉𝑝 = 0

115

Reserve price: field experiment results


Reserve price: detection and reaction

• A dynamic and repeated game between the winner (w) and the publisher (p)

• Extension form representation

– Information nodes:

• 𝐼1: the winning bid 𝑏1 is higher

• 𝐼2: the reserve price 𝛼 is higher

– Actions:

• 𝑎𝑤1: to increase 𝑏1 so that 𝑏1 ≥ 𝛼

• 𝑎𝑤2: to increase 𝑏1 so that 𝑏1 < 𝛼

• 𝑎𝑤3: to decrease 𝑏1 so that 𝑏1 ≥ 𝛼

• 𝑎𝑤4: to decrease 𝑏1 so that 𝑏1 < 𝛼

• 𝑎𝑝1: to increase 𝛼 so that 𝛼 ≥ 𝑏1

• 𝑎𝑝2: to increase 𝛼 so that 𝛼 < 𝑏1

• 𝑎𝑝3: to decrease 𝛼 so that 𝛼 ≥ 𝑏1

• 𝑎𝑝4: to decrease 𝛼 so that 𝛼 < 𝑏1

118


The dynamic and static game tree for the auction with reserve price

payoff of the winner and the publisher

119


• Consider playing the game repeatedly

– If the reserve price was higher, should the publisher lower it?

– The optimal auction theory

• Advertisers want to learn the publisher’s private value distribution, too

– What is the absolute minimal price that can be accepted?

• Questions

– What are the best response functions for both players?

– What are the dominant strategies and equilibrium?

120

Reserve price: references

• On optimal reservation prices in auctions, Engelbrecht-Wiggans, 1987

• Optimal reservation prices in auctions, Levin and Smith, 1996

• Auction theory: a guide to the literature, Klemperer, 1999

• Reserve prices in internet advertising auctions: a field experiment, Ostrovsky and Schwarz, 2009

• Auction theory 2nd edition, Krishna, 2009

• Optimal reserve price for the generalized second-price auction in sponsored search advertising, Xiao et al.,

2009

• Optimal auction design and equilibrium selection in sponsored search auctions, Edelman and Schwarz, 2010

• Optimal auction design in two-sided markets, R Gomes, 2011

121

• The task:

– There are multiple ad

channels giving

different payoffs over

time. Which one to

use?

• The challenge:

– Too many possible

candidates

– The payoffs change

over time

Ad channel selection

Sequential Selection of Correlated Ads by POMDPs, Yuan and Wang, 2012 122

Ad channel selection contd.

• A sequential selection problem

• Value iteration exact solution (high computational complexity)

• Multi-armed bandit approximation

Sequential Selection of Correlated Ads by POMDPs, Yuan and Wang, 2012 123

Sequential selection: references

• Dynamic programming, RE Bellman, 1957

• Multi-armed bandits and the Gittins index, P Whittle, 1980

• A survey of POMDP applications, AR Cassandra, 1998

• A survey of POMDP solution techniques, KP Murphy, 2000

• Finite-time analysis of the multi-armed bandit problem, P Auer, P. et al.,2002

• Multi-armed bandit algorithms and empirical evaluation, J Vermorel and M Mohri, 2005

124

Impression allocation between GD and NGD

• Sometimes, ad channels could have different attributes:

– GD: Guaranteed Delivery (contracts)

– NGD: Non-Guaranteed Delivery (auctions)

• Given a time window, the publisher decides

– To accept or reject a contract proposal

– To allocate impressions among multiple contracts and auctions

(NGD can be modelled as an already accepted contract with ∞ required

impressions and 0 under-delivery penalty)

Dynamic Revenue Management for Online Display Advertising, Roels and Fridgeirsdottir, 2008 125

Connecting GD and NGD

courtesy of Internet advertising bureau

Four (4) major types of inventories in Programmatic selling

Now they are priced differently and separately

Separated markets implies inefficiency and arbitrage

126

Automated (programmatic) guaranteed delivery

• AOL Upfront

– Will take effect January 1, 2014

– Two brands and five agencies have committed, around $10m for each agency

(undisclosed)

– AOL’s ad placements, e.g. The Huffington Post, TechCrunch and StyleList

– A private marketplace in the beginning

• Question

– Selling mechanisms (auction, queue, etc.)

– Inventory allocation and reserve prices

courtesy of AOL 127

Optimal pricing of a guaranteed delivery contract

• Suppose the publisher wants to sell 𝑆 impressions from time step 𝑇 + 1

– In advance: guaranteed delivery contracts

– On spot: RTB auctions (non-guaranteed, 2nd price auction)

• Consider the total demand as 𝑄

• The private value distribution of an advertiser is 𝐹 ⋅

• The utility of an advertiser is 𝑈𝑎 ⋅ = 𝐹 ⋅ + 𝑔 𝑡

– Willing to pay higher if could buy in advance

• The utility of the publisher is 𝑈𝑝 ⋅ = 𝑅 ⋅ + ℎ(𝑡)

– Willing to charge lower if could sell in advance

What is the optimal price at 0 < 𝑡 < 𝑇 + 1 ?

128

Guaranteed delivery: references

• Optimal dynamic auctions for display advertising, YJ Chen, 2009

• Pricing guaranteed contracts in online display advertising, V Bharadwaj, 2010

• Risk-aware revenue maximization in display advertising, A Radovanovic and WD Heavlin, 2012

• Optimal allocation for display advertising, H Rui et al., 2012

• A unified optimization framework for auction and guaranteed delivery in online advertising, K Salomatin,

2012

• Maximally representative allocations for guaranteed delivery advertising campaigns, RP McAfee, 2013

129

Bid landscape forecasting

courtesy of Google AdWords

The tool usually exists as a service provided to advertisers

130


Bid landscape forecasting in online ad exchange marketplace, Y Cui et al,. 2011

• The task:

Given a campaign (a set of

targeting rules), what is the

bid-impression distribution

for a given venue (domain,

placement, etc.)?

• The challenge:

Forecasting for new &

changed campaigns

Forecasting the win rate for

unseen bids

131


Ad impression forecasting for sponsored search, A Nath et al., 2013 132

A Generative Model based ad Impression Forecasting method

Bid landscape forecasting: references

• Bid landscape forecasting in online ad exchange marketplace, Y Cui et al., 2011

• Handling forecast errors while bidding for display advertising, KJ Lang et al., 2012

• Ad impression forecasting for sponsored search, A Nath et al., 2013

• Forecasting user visits for online display advertising, S Cetintas et al., 2013

• Predicting advertiser bidding behaviors in sponsored search by rationality modeling, H Xu et al., 2013

• Optimizing volume and frequency forecasts for an online video advertiser, J Talbot et al., 2013

133

Data leakage protection

• Every player in the ad eco-system realises the value of audience data

– who owns it?

– what’s its value?

• The data leakage problem

– buyers collect user data from premium website

– then retarget these users on cheap inventories

• The task

– To learn who is collecting user data on the webpage (piggybacks)

– To stop the unauthorized collection and ask for payment

• The challenge

– The optimal price: limiting the user data access will hurt the CPM

134

Part 5: Financial methods in Computational Advertising

• Game theory basics

• Ad options

135

Examples

A futures market in computer time, IE Sutherland, 1968

Financial methods and game theory have a long history in CS research

Auction for sharing compute resources

136

Examples contd.

• Amazon EC2 is a web service that provides resizable compute capacity in the cloud• In late 2009, Amazon announce its spot instances pricing system

Deconstructing Amazon EC2 Spot Instance Pricing, OA Ben-Yehuda, 2011

Auction and futures in cloud computing

137

Examples contd.

courtesy of Wall Street Journal

In the age of the Internet, fixed prices are a thing of the pastProfessor Oren Etzioni

138

Game theory basics

• A game is “a competitive activity … in which players contend with each other

according to a set of rules”

• A strategic game (with ordinal preferences) consists of

– a set of players

– for each player, a set of actions

– for each player, preferences over the set of action profiles

• The best response

• The Nash equilibrium of static games

An introduction to game theory, MJ Osborne, 2003 139

Example: the prisoner's dilemma

The mysterious benedict society and the prisoner’s dilemma, TL Stewart, 2009courtesy of Encyclopaedia Britannica, 2006

• A typical example of non-

cooperative game

• The strictly dominant strategy

for both players is to confess,

which also forms the Nash

equilibrium of the game

• When played iteratively, the

winning deterministic strategy is

tit-for-tat

(first cooperate, then subsequently

replicate an opponent's previous

action)

140

Example: The game of chicken

• While each player prefers not to

yield to the other, the worst possible

outcome occurs when both players

do not yield.

– The nuclear crisis

– The promise on advertising effects

– The bidding on similar audiences

simultaneously

Dare Chicken

Dare 0, 0 7, 2

Chicken 2, 7 6, 6

courtesy of Rebel Without a Cause 141

Example: The Cournot competition• Assumptions

– There is more than one firm and all firms produce a

homogeneous product, i.e. there is no product

differentiation;

– Firms do not cooperate, i.e. there is no collusion;

– Firms have market power, i.e. each firm's output decision

affects the good's price;

– The number of firms is fixed;

– Firms compete in quantities, and choose quantities

simultaneously;

– The firms are economically rational and act strategically,

usually seeking to maximize profit given their competitors'

decisions.

courtesy of palimpsestes.fr 142

Example: The Cournot competition

• 𝑝1, 𝑝2: prices

• 𝑞1, 𝑞2: quantities

• Firm 1’s profit:

Π1 = 𝑞1 𝑃 𝑞1 + 𝑞2 − 𝑐

• Firm 1’s best response function:

𝑅(𝑞2) =𝜕Π1𝜕𝑞2

• To obtain the equilibrium:

𝑅 𝑞2 = 𝑅 𝑞1

(the intersection)


Example: The Bertrand competition

• Assumptions

– Firms compete by setting prices simultaneously and

consumers want to buy everything from a firm with a

lower price

– If two firms charge the same price, consumers

demand is split evenly between them


Example: The Bertrand competition

both firms are pricing at marginal cost

courtesy of Wikipedia

• If both firms set equal prices

above marginal cost, firms

would get half the market at a

higher than MC price

• By lowering prices just slightly,

a firm could gain the whole

market

• Both firms are tempted to

lower prices as much as they

can

• It would be irrational to price

below marginal cost, because

the firm would make a loss 145

Application of the duopoly competition

• If capacity and output can be easily changed -> Bertrand model

• if output and capacity are difficult to adjust -> Cournot model

• Analogy

– Two publishers serving similar content

– Premium and long-tail publishers seeing similar users

– etc.

146

References: financial methods and game theory basics

• A policy framework for trading configurable goods and services in open electronic markets, S Lamparter, 2006

• Planning and pricing of service mashups, B Blau et al., 2008

• Web service derivatives, T Meinl and B Blau, 2009

• How to coordinate value generation in service networks, B Blau et al., 2009

• Enabling cloud service reservation with derivatives and yield management, T Meinl et al., 2010

• Web services advanced reservation contracts, C Weinhardt et al., 2011

• Finite automata play the repeated prisioners dilemma, A Rubinstein, 1986

• A course in game theory, MJ Osborne and A Rubinstein, 1994

• An introduction to game theory, MJ Osborne, 2004

• Sponsored search auctions: an overview of research with emphasis on game theoretic aspects, P Mailléet al.,

2012

• Repeated keyword auctions played by finite automata, W Ding et al, 2013

147

Ad options

• The task:

– To sell impressions in advance

(a natural extension to the programmatic

guarantee)

– Both party can choose to exercise or not

• The (pricing) challenge:

– Impression prices are volatile

– Non-storability: impressions cannot be

bought and kept

– Not just about the price movements: the

uncertainty of traffic volume, CTR and etc.

courtesy of Webscope from Yahoo! Labs 148

Ad options contd.

Advertisers Publishers

secure impressions delivery

reduce uncertainty in auctions

cap cost

sell the inventory in advance

have a more stable and predictable

revenue over a long-term period

increase advertisers’ loyalty

Benefits

149

Submits a request of guaranteed ad delivery for the keywords ‘MSc Web Science’, ‘MSc Big Data Analytics’ and ‘Data Mining’ for the future 3 month term [0, T], where T = 0.25.

Ad options contd.

Sells a list of ad keywords via a multi-keyword multi-click option

t = T

Timeline

search engineonline advertiser

t = 0Pays £5 upfront option price to obtain the option.

Multi-Keyword Multi-Click Advertisement Option Contract for Sponsored Search, B Chen et al., 2013

multi-keyword multi-click option (3 month term)

upfront fee

(m = 100)keywords list fixed CPCs

£5

‘MSc Web Science’ £1.80

‘MSc Big Data Analytics’

£6.25

‘Data Mining’ £8.67

150

Exercising the option

t = T

Timeline

Exercises 100 clicks of ‘MSc Web Science’ via option.

t = 0

Pays £1.80 to the search engine for each click until the requested 100 clicks are fully clicked by Internet users.

t = t1

Reserves an ad slot of the keyword ‘MSc Web Science’ for the advertiser for 100 clicks until all the 100 clicks are fully clicked by Internet users..

t = t1c

Multi-Keyword Multi-Click Advertisement Option Contract for Sponsored Search, B Chen et al., 2013 151


Not exercising the option

t = T

Timeline

If the advertiser thinks the fixed CPC £8.67 of the keyword ‘Data Mining’ is expensive, he/she can attend keyword auctions to bid for the keyword as other bidders, say £8.

t = 0

Pays the GSP-based CPC for each click if winning the bid.

t = …

Selects the winning bidder for the keyword ‘Data Mining’ according to the GSP-based auction model.



Ad options pricing

• Building blocks

– No-arbitrage [F Black and M Scholes1973; H Varian1994]

– Stochastic underlying keyword CPC [P Samuelson1965]

– Terminal value formulation

• Formula

– n=1, Black-Scholes-Merton European call

– n=2, Peter Zhang dual strike European call

– n>=3, Monte Carlo method


Ad options: references

• Option pricing: a simplified approach, J Cox et al., 1979

• Online ad slotting with cancellations, F Constantin, 2008

• A truthful mechanism for offline ad slot scheduling, J Feldman et al., 2008

• Selling ad campaigns: online algorithms with cancellations, M Babaioff et al., 2009

• Options, futures and other derivative securities (7th edition), J Hull, 2009

• Online advertisement service pricing and an option contract, Y Moon and CY Kwon, 2010

• Selling futures online advertising slots via option contracts, J Wang and B Chen, 2012

• Multi-keyword multi-click advertisement option contract for sponsored search, B Chen et al., 2013

154

Additional references

• Internet advertising and the generalized second price auction: selling billions of dollars worth of keywords, B

Edelman, 2005

• Price cycles in online advertising auctions, X Zhang and J Feng, 2005

• Budget optimization in search-based advertising auctions, J Feldman et al., 2007

• The economics of the online advertising industry, DS Evans, 2008

• Expressive banner ad auctions and model-based online optimization for clearing, C Boutilier et al., 2008

• Computational advertising, AZ Broder, 2008

• Algorithmic methods for sponsored search advertising, J Feldman and S Muthukrishnan, 2008

• Internet ad auctions: Insights and directions, S Muthukrishnan, 2008

• The online advertising industry: economics, evolution, and privacy, , DS Evans, 2009

• Ad exchanges: Research issues, S Muthukrishnan, 2009

• Adaptive bidding for display advertising, A Ghosh et al., 2009

• The arrival of real-time bidding, Google, 2011

• Algorithms and strategies for web advertising, P Papadimitriou, 2011

• OpenRTB API specification, IAB, 2012

155

Additional references

• Targeted, not tracked: client-side profiles and privacy-friendly behavioral advertising, M Bilenko and M

Richardson, 2012

• Computational advertising in social networks, A Bhasin, 2012

• Size, labels, and privacy in targeted display advertising, C Perlich, 2012

• Estimating conversion rate in display advertising from past erformance data, K Lee et al,. 2012

• Handling forecast errors while bidding for display advertising, KJ Lang et al., 2012

• Marketing campaign evaluation in targeted display advertising, J Barajas et al., 2012

• Ad exchange-proposal for a new trading agent competition game, M Schain and Y Mansour, 2012

• Auctions for online display advertising exchanges: approximations and design, S Balseiro et al., 2012

• Real-time bidding for online advertising: measurement and analysis, S Yuan et al., 2013

• Impression fraud in on-line advertising via pay-per-view networks, K Springborn and P Barford, 2013

• An overview of computational challenges in online advertising, RE Chatwin, 2013

• Competition and yield optimization in ad exchanges, SR Balseiro, 2013

• Internet advertising revenue report, IAB and PwC

156