2015 05-15-yandex

Real-Time Bidding (RTB) based Display Advertising

- System Mechanisms and Algorithms

Dr. Jun Wang, Senior Lecturer Computer Science, University College London

Co-founder, CTO, MediaGamma Email: [email protected] Twitter: @seawan

Joint work with Weinan Zhang, Shuai Yuan, and Bowei Chen

Computa(onal Adver(sing and Behavior Targe(ng Research Group at UCL Computer Science

•  Work on related topics, including informa=on retrieval, real-‐=me bidding, collabora=ve filtering and personalisa=on

21/05/15 Yandex Talk 2

Summary

•  Introduc=on to Real-‐Time Bidding (RTB) •  RTB exchange mechanisms: measurements and analysis

•  Buy side: – Bidding op=misa=on – Sta=s=cal Arbitrage Mining (SAM)

•  Sell side: – Forward market (Waterfall op=misa=on) – Ad op=ons pricing


Summary





Contextual ads: relevant to the webpage content

Content: iPad

Ads: Tablet PCs, mobile phone etc.


Real-‐(me Adver(sing: Selling ad slot per impression targeted to the user


Real-‐(me Adver(sing: Selling ad slot per impression targeted to the user

s


How big RTB is?

•  The AppNexus (exchange) sees more than 30 billion impressions in a single day

•  The Rubicon Project (SSP) has hit 1 trillion impressions

•  The Global Real-‐Time Bidding (RTB) market is forecast to GROW at 41.18% CAGR (Compound annual growth rate) during 2014-‐2019


Life of a display ad in the RTB environment: 0.2 seconds

Ad Exchange

Demand-Side Platform

Advertiser

Data Management

Platform

0. Ad Request 1. Bid Request (user, context)

2. Bid Response (ad, bid)

3. Ad Auc=on 4. Win No=ce (paying price)

5. Ad (with tracking)

6. User Feedback (click, conversion, etc.)

User Informa=on

User Demography: Male, 20+, etc. User Segmenta=ons: Travel, etc.

Webpage

User


The new RTB eco-‐system

21/05/15 Yandex Talk

Demand Side PlaKorm (DSP)

Adver(ser PublisherSupply Side PlaKorm (SSP)

Ad Exchange

-‐15% -‐ 20% -‐15% -‐ 20%

3rd party data provider, ad serving, ad agency, campaign analy(cs,

-‐10% -‐ 30%

-‐10 % – 15%£1.00 £0.15 -‐ £0.50

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Summary





The mechanism: a mixture of first and second price auc(ons

•  A high soft floor price can make it first price auction (In RTB, floor prices are not always disclosed before auctions)

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

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

21/05/15 Yandex Talk Shuai Yuan, Jun Wang, Real-‐=me Bidding for Online Adver=sing: Measurement and Analysis, AdKDD’13

12

Auc(ons: Winning bids against (me for a given placement

•  Winning bids peak at 8-10am

•  The hourly average

series peaks around 6-8am every day when there are less impressions but more bidders.


Auc(ons: The distribu(on of number of bidders and impressions against (me for a given placement

•  More bidders in the morning, which may be due to the mixture of hour-of-day targeting and no daily pacing setup

•  A clear lag suggests the

unbalance of supply and demand of the market in certain hours.

The plots used 3 months worth of data sampled from a single placement. 21/05/15 Yandex Talk 14

Conversions: post-‐view & post-‐click

Daily periodic patterns for conv (left) and cvr (right) show that Some pattern, e.g., in this case, people are less likely to convert during late night

The post-view conversions are NOT negligible


Conversions: User target frequency distribu(on

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


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

The wide conversion window (right) challenges attribution models

Conversions: Recency Distribu(on


Summary





Op(mal Bidder in DSP

Weinan Zhang, Shuai Yuan, Jun Wang, Op=mal Real-‐Time Bidding for Display Adver=sing, KDD’14 19

Op(mal Bidder: Problem Defini(on

Bid Engine Bid Request Bid Price

Input: bid request include Cookie informa=on (anonymous profile), website category & page, user terminal, loca=on etc Output: bid price Considera(ons: Historic data, CRM (first party data), DMP (3rd party data from Data Management Plaiorm)

What is the op(mal bidder given a budget constraint? e.g., Maximise Subject to the budget constraint

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Op(mal Bidder Formula(on •  Func=onal op=misa=on problem

•  Solu=on: Calculus of varia=ons

CTR winning func=on

bidding func=on budget

Est. volume

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Op(mal Bidding Strategy Solu(on

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Op(mal bidding strategy compared with a linear solu(on

Slight increase at low bids is more effec=ve

Thus reduce the bids at high CTR or CVR

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Overall Performance – Op(mising Clicks

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25 hlp://contest.ipinyou.com/session-‐three-‐online-‐leaderboard.html

Summary





DSP as an Intermediary in RTB

•  Different pricing schemes: CPM/CPC/CPA

RTB Intermediary

Ad agent

Trading Desk DSP

Publishers

Ad inventories

Advertisers

Sell ad inventory

Sell ad inventory

Buy ad inventory

Buy ad inventory

CPA CPM

Weinan Zhang and Jun Wang, Sta=s=cal Arbitrage Mining for Display Adver=sing, accepted KDD’15 27

DSP as an intermediary in RTB

•  Sta=s=cal arbitrage opportunity occurs when (CPM) cost per conversion < (CPA) payoff per conversion

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Sta(s(cal Arbitrage Mining

•  Op=mising net profit by tuning bidding func=on and campaign volume alloca=on

Total cost constraint

Risk control

E-‐Step M-‐Step

Total arbitrage net profit

•  Solve it in an EM fashion 29

EM Solu(ons •  M-‐Step: Fix v and tune b()

•  E-‐Step: Fix b() and tune v

Poriolio margin variance

Poriolio margin mean

Total arbitrage net profit

Total cost constraint

Net profit margin on each campaign

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Mul(ple Campaign Arbitrage Results

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Dynamic PorKolio Op(misa(on

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Online A/B Test on BigTree™ Mobile DSP

•  23 hours, 13-‐14 Feb. 2015, with $60 budget each 33

Summary





(RTB) Ads prices are vola(le

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Figure 1: The instability of CPM (cost per mille), AWR (auctionwinning ratio), eCPC (effective cost per click), and CTR (click-through rate) for two sample campaigns without a controller.Dataset: iPinYou.

trollers are capable to control eCPC and AWR, where PID furtherprovides a better control accuracy and robustness than WL. We findthat the farer reference value is away from the initial performance,the more difficult to control and settle the KPI to the reference.

Moreover, we also study whether feedback control techniquescan be leveraged for bid optimisation. It is quite common that acampaign has different performance (e.g. eCPC) on the ad invento-ries from different channels (e.g. ad exchanges and user geographicregions). If we can reallocate part of the budget from the less cost-effective channels to the more cost-effective ones, the campaign-level performance would get improved. Formally, we propose amodel to calculate the optimal reference eCPC for each channeland then deploy the feedback control to settle the campaign’s eCPCperformance of each channel to the optimal reference eCPC. As aresult, we discover that the campaign-level click number and eCPCachieve significant improvement with the same budget.

Furthermore, the feedback control system has been integrated ina commercial DSP. The live test has demonstrated its effectivenessto produce the controllable advertising performance during the highcompetitive RTB market around the new year.

The rest of this paper is organised as follows. Section 2 providespreliminaries for RTB and feedback control. Our solution is for-mally presented in Section 3, followed by the empirical study inSection 4 and the online deployment and test in Section 5. Section6 discusses the related work and we conclude this paper in Section7.

2. PRELIMINARIESTo make the paper self-contained, we now take a brief review on

the RTB eco-system, a common bidding strategy, and some funda-mental of feedback control theory.

2.1 RTB Flow StepsFigure 2 illustrates the interaction process among the main com-

ponents of RTB ecosystem: (0) When a user visits an ad-supportedsite (e.g., web pages, streaming videos and mobile apps), each adplacement will trigger a call for ad (ad request) to the ad exchange.(1) The ad exchange sends the bid requests for this particular adimpression to each advertiser’s DSP bidding agent, along with theavailable information such as the user and context information. (2)

DSPBiddingAgent

0. Ad Request

5. Ad

6. User feedback

PageUser

RTBAd

Exchange3. Auction

1. Bid Request(user, page, context data)

(with tracking)4. Win Notice(charged price)

(click, conversion)

(ad, bid price)2. Bid Response

Figure 2: A brief illustration of the interactions between user,ad exchange and advertiser’s DSP bidding agent.

DynamicSystem

Monitor

Actuator

SystemInput

ControlSignal

SystemOutput

MeasuredOutput

ControlFunction

Reference KPI

ErrorFactor

Controller

Figure 3: Control-feedback loop.

With the information of the bid request and each of its qualifiedad, the bidding agent calculates a bid price. Then the bid response(ad, bid price) is sent back to the exchange. (3) Having receivedthe bid responses from the advertisers, the ad exchange hosts anauction and picks the ad with the highest bid as the auction winner.(4) Then the winner is notified for the auction winning from the adexchange. (5) Finally, the winner’s ad will be shown to the visitoralong with the regular content of the publisher’s site. It is com-monly known that a long time page-loading would greatly reduceusers’ satisfactory [23]. Thus, advertiser bidding agents are usuallyrequired to return a bid in a very short time frame (e.g. 100 ms). (6)The user’s feedback (e.g., click and conversion) on the displayed adis tracked and sent back to the winner advertiser.

2.2 Bidding StrategiesFrom Figure 2, it is clear that the core problem for each bidding

agent is to figure out how much to bid for each bid request. From[37], the bid decision depends on two factors for each ad impres-sion: the utility (e.g., CTR, expected revenue) and cost (i.e., ex-pected charged price). As a widely adopted bid strategy introducedin [27], the utility is evaluated by CTR estimation while the basebid price is tuned based on the bid landscape [9] for cost evaluation.The generalised bidding strategy in [27] is

b(t) = b0✓t

✓0, (1)

where ✓t is the estimated CTR for the bid request at moment t;✓0 is the average CTR under a target condition (e.g., a user interestsegment); and b0 is the tuned base bid price for one target condition.In this work, we adopt this widely used bidding strategy based onthe logistic CTR estimator [30].

2.3 Feedback Control TheoryFeedback control theory deals with the reaction of dynamic sys-

tems with control signals as inputs, and the system feedback asoutputs [2]. Figure 3 briefly shows the interactions between thecontroller and the dynamic system. The usual objective of feed-back control theory is to control a dynamic system so that the sys-tem output follows a desired control signal, called the reference,which may be a fixed or changing value. To do this, a controller isdesigned which monitors the output and compares it with the refer-ence. The difference between actual and desired output, called theerror factor, is applied as feedback from the dynamic system to the


Hedge the price risk

•  Need Ad’s Futures Contract and Risk-‐reduc=on Capabili=es – Technologies are constrained mainly to “spots” markets, i.e., any transac=on where delivery takes place right away (in Real-‐=me Adver=sing and Sponsored Search)

– No principled technologies to support efficient forward pricing &risk management mechanisms

36 21/05/15 Yandex Talk

Adv

ertis

er Demand Side

Platform (DSP)

RTB / Spot Exchange

Supply Side Platform (SSP)

Pub

lishe

r

3rd party data providers, ad serving, ad agency, ad networks, campaign

analytics

-10% to -30%

An analogy with financial markets

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Forward/Futures/Options Exchange

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Solution 1: Combine RTB with Forward Market, which pre-sell inventories in advance with a fixed price

Solution 2: If we got Futures Exchange or provide Option Contracts, advertisers could lock in the campaign cost and Publishers could lock in a profit in the future


Publisher ad serving “waterfall” (programma(c direct)

21/05/15 Yandex Talk Illustra=on from Sitescout 38

Solu(on 1: RTB combined with Forward Programma(c Guaranteed/Direct Market

39 21/05/15 Yandex Talk B Chen et al., A Dynamic Pricing Model for Unifying Programmatic Guarantee and Real-Time Bidding in Display Advertising. Best Paper in ADKDD 2014

Op(misa(on objec(ve


Op(mised pricing scheme


An Ad Option is a contract in which the option publisher grants the advertiser the right but not the obligation to enter into a transaction either buy or sell an underlying ad slot at a specified price on or before a specified date. The specified pre-agreed price is called strike price and the specified date is called expiration date. The option seller grants this right in exchange for a certain amount of money at the current time is called option price.

Solu(on 2: Ad Op(on Contract

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


Ad op(ons: Benefits

Advertisers

§  secure impressions delivery

§  reduce uncertainty in auctions

§  cap cost

Publishers

§  sell the inventory in advance

§  have a more stable and predictable revenue over a long-

term period

§  increase advertisers’ loyalty


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 op(ons contd. Sells a list of ad keywords via a multi-keyword multi-click option

t = T

Timeline

search engine online advertiser

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

B Chen et al., Multi-Keyword Multi-Click Advertisement Option Contract for Sponsored Search, ACM TOIST, 2015

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

44 21/05/15 Yandex Talk

Exercising the op(on

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

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search engine online advertiser 21/05/15 Yandex Talk

Not exercising the op(on

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.

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search engine online advertiser 21/05/15 Yandex Talk

Risk Hedge when Ad Op(ons and RTB spot are combined

Table 10: Overview of the improvement in delivery performance by using ad options for all ad slots in the

SSP dataset.

Bull market Bear market

Change on used budget (%) -8.7878% –

Change on delivery of impressions (%) 6.1781% –

Table 11: Overview of the improvement in delivery performance by using ad options for keywords in the

Google AdWords dataset.

Market GroupChange in used budget (%) Change in delivery of impressions (%)

Bull market Bear market Bull market Bear market

US

1 0.3447% 2.3438% 9.3050% -0.1122%

2 1.7748% 3.9687% 2.3153% -2.6285%

3 0.5372% 4.8567% 44.3735% -0.0940%

4 5.6288% 29.3626% 1.6433% -1.0993%

UK

1 21.4285% 6.8940% 3.0717% -0.2523%

2 5.4426% 0.0000% 0.4419% 0.0000%

3 10.9285% 3.8474% 28.7706% -2.1066%

4 6.7155% 0.1552% 16.6955% -2.1550%

6.4

6.6

6.8

1.35 1.40 1.45Revenue std

Rev

enue

mea

n

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1

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Rev

enue

mea

n(b)

Sell ratio 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

Figure 10: Empirical examples of the publisher’s revenue: (a) from an ad slot in the bull market; and (b)

from an ad slot in the bear market. The sell ratio represents the percentage of future daily impressions that

are sold in advance via display ad options. Note that here the ad slot in the bear market does not receive

enough bids in the test set, so we randomly simulate some underlying prices for the bear market.

30

47 21/05/15 Yandex Talk B Chen and J Wang, A Lattice Framework for Pricing Display Ad Options with the Stochastic Volatility Underlying Model, Technical Report, 2014

An empirical example of search engine's revenue for the keyword equity loans

:20 B. Chen et al.

4 4.5 50

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Revenueofsearchengine

(a) GBM

Reve differenceOption priceExp spot CPC

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(b) CEV


4 4.5 50

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(c) MRD


4 4.5 50

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(d) CIR


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(e) HWV


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(a) GBM


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(c) MRD


4 4.5 50

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(d) CIR


4 4.5 50

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(e) HWV


Fig. 8. Empirical example of search engine’s revenue for the keyword ‘equity loans’.

Next, Figure 9 illustrates the case where we have 2 candidate keywords: ‘iphone4’and ‘dar martens’, where r = 5% and ⇢ = 0.0259. In Figure 9(a), we see that thehigher the fixed CPCs the lower is the option price. This property is the same as forthe single-keyword options. Also, the calculated option price achieves maximum whenall the fixed CPCs are zeros. Figure 9(b) then shows the revenue difference curve ofthe search engine, where the red star represents the value when F1 = EQ

t

[C1(T )] andF2 = EQ

t

[C2(T )]. The expected revenue differences are all above zero, showing thatthis 2-keyword ad option is beneficial to the search engine’s revenue. However, aninteresting point to discuss is that the red star point is not the maximum differencerevenue, which is different from single-keyword options. This may be due to the factthat the underlying CPCs move in a correlated manner and the advertiser switcheshis/her exercising from one to another. The revenues’ difference curve in Figure 9(b)is very smooth while Figure 10(b) shows a bit volatile pattern because the underlyingcorrelation increases. Above all, the properties of the revenue difference are similar tothose of single-keyword options and they are all positive.

It would be impossible to graphically examine the revenue difference for higher di-mensional ad options (i.e., n � 3). However, based on the earlier discussions, we cansummarize two properties. First, there are boundary values of the revenue differences.If every F

i

! 0, D(F) ! 0; if every F

i

! 1, D(F) ! 0. Second, there exists a maxi-mum revenue difference value even though this may not at the point F

i

= EQt

[C

i

(T )].Overall, we are able to say that a proper setting of fixed CPCs by a search engine canincrease the ad revenue compared to keyword auctions.

5. CONCLUDING REMARKSIn this paper, we proposed a new ad selling mechanism for sponsored search that bene-fits both advertisers and search engine. On the one hand, advertisers are able to secure

† This manuscript is under submission to a journal.

B Chen et al. Multi-Keyword Multi-Click Option Contracts for Sponsored Search Advertising, ACM TOIST, 2015 21/05/15 Yandex Talk 48

Acknowledgements •  Thanks to my PhD students Weinan Zhang, Shuai Yuan, Bowei Chen, Xiaoxue Zhao…


For more informa(on, please refer to 1.  Shuai Yuan, Jun Wang, Real-‐=me Bidding for Online Adver=sing:

Measurement and Analysis, AdKDD’13 2.  Weinan Zhang, Shuai Yuan, Jun Wang, Op=mal Real-‐Time Bidding for

Display Adver=sing, KDD’14 3.  Bowei Chen, Shuai Yuan, and Jun Wang. A dynamic pricing model for

unifying programma=c guarantee and real-‐=me bidding in display adver=sing, Best Paper Award, ADKDD’14,

4.  Weinan Zhang and Jun Wang, Sta=s=cal Arbitrage Mining for Display Adver=sing, accepted KDD’15

5.  Shuai Yuan, Jun Wang, Bowei Chen, An Empirical Study of Reserve Price Op=misa=on in Real-‐Time Bidding, CIKM’14

6.  Bowei Chen, Jun Wang, A latce framework for pricing display ad op=ons with stochas=c vola=lity underlying models, under submission, 2015

7.  Bowei Chen, Jun Wang, Ingemar Cox, and Mohan Kankanhalli, Mul=-‐Keyword Mul=-‐Click Op=on Contracts for Sponsored Search Adver=sing, ACM TOIST, 2015


Thanks for your acen(on

51

2015 05-15-yandex

Science

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