Smart Pacing for Effective Online Ad Campaign Optimization · 2016-04-23 · Smart Pacing for E...

Smart Pacing for Effective Online Ad CampaignOptimization

Jian Xu, Kuang-chih Lee, Wentong Li, Hang Qi, and Quan Lu

Yahoo Inc.

August 26, 2015

Jian Xu et al. (Yahoo Inc.) Smart Pacing August 26, 2015 1 / 17

Overview

1 BackgroundPacing as a campaign optimization problem

2 Smart PacingMotivationOur approach - models and algorithms

3 EvaluationsReal ad campaignsSimulations

4 System Implementation


Background

Budget pacing helps advertisers to define and execute how their budget isspent over the time.

Why budget pacing?

Reach a wider range of audience,Build synergy with other marketing campaigns,Avoid premature campaign stop, overspending, and/or spendingfluctuations.

Budget pacing plan examples:

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(a) Even pacing

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(b) Traffic-based pacing


Background (cont.)

Two streams of strategies for budget pacing:

Bid modification: bid price is altered to influence bid win-rate so thatbudget spending can be controlled,

Probabilistic throttling: ad request is bid with some probability(pacing rate).

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(d) Probabilistic throttling


Background: Pacing as a campaign optimization problem

Campaign optimization objectives:

Reach the delivery and performance goals

Branding campaigns: spend out budget to reach an extensive audience.In the meanwhile, make campaign performance (e.g., in terms of eCPCor eCPA) as good as possible;Performance campaigns: meet performance goal (e.g., eCPC no morethan $5). In the meanwhile, spend as much budget as possible.

Execute the budget pacing plan

Reduce creative serving cost

Question

Can we achieve campaign optimization through smart pacing control?


Background: Pacing as a campaign optimization problem(cont.)

Problem def. 1

Smart pacing for ad campaigns without specificperformance goals: determining the values of riso that

minri

P

s.t. C = B, Ω(C,B) ≤ ε(1)

Problem def. 2

Smart pacing for ad campaigns with specificperformance goals: determining the values of riso that

minri

Ω(C,B)

s.t. P ≤ G , B − C ≤ ε(2)

Notation MeaningB total budget

B = (B(1), ...,B(K)) pacing planReqi i-th ad requestri point pacing rate on Reqisi ∼ Bern(ri ) indicator of participating auctionwi indicator of winning Reqici cost to serve ad to Reqipi response probabilityqi ∼ Bern(pi ) indicator of user response (click

or conversion)C =

∑i siwici total cost of the ad campaign

P = C/∑

i siwiqi performance of the ad campaign(eCPC or eCPA)

C = (C (1), ...,C (K)) actual spending patternΩ(C,B) penalty of deviating from pacing

planε tolerance level of the penaltyε tolerance level of remaining budget

Table: Notations in problem formulation.


Smart Pacing: Motivation

Directly solving for ri is difficult and computationally expensive.

Observations on prevalent campaign pricing setups:

CPM campaigns

Branding: optimize performance as long as budget spending aligns theplan → prefer high responding ad requests;Performance: optimize spending pattern as long as performance goal ismet → prefer high responding ad requests.

CPC/CPA campaigns

Have implicit performance goals. If prediction is perfect, creativeserving cost is a concern → prefer high responding ad requests.

dCPM campaigns

Have explicit performance goals. If prediction is perfect, creativeserving cost is a concern → prefer high responding ad requests.


Smart Pacing: Our approach - models and algorithms

Learn from offline serving logs a response prediction model toestimate pi = Pr(respond |Reqi ,Ad);

Reduce the solution space by grouping similarly responding adrequests. Requests in the same group share the same group pacingrate;

Develop a control-based method to learn from online feedback dataand dynamically adjust the group pacing rates to approximate theoptimal solution.


Smart Pacing: Our approach - models and algorithms(cont.)

A picture is worth a thousand words ...

1.0 0.6

1.0

0.1

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0.001

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1.0 1.0

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Layer 3

Layer 2

Layer 1

Layer 0

Ad request volume

Time slot Budget pacing plan

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Time slot

High responding Low responding

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Evaluations: Real ad campaigns

Evaluation setup:

Online blind A/B test with50%-50% traffic and budgetsplit;

Baseline: probabilistic throttlingwith global pacing rate(equivalent to one-layerSmartPacing);

SmartPacing: 8 layers of adrequest groups.

Pacing plan: even pacing.

0

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10 20 30 40 50 60 70 80 90 0

0.1

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Sp

en

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g (

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PC

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time slot

Spending BaselineSpending SmartPacing

eCPC BaselineeCPC SmartPacing

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10 20 30 40 50 60 70 80 90 0

0.2

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$)

eC

PC

($

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time slot



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10 20 30 40 50 60 70 80 90 0

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din

g (

$)

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PC

($

)

time slot




Evaluations: Simulations (w/o eCPC goal)

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0 10 20 30 40 50 60 70 80 90 20000

40000

60000

80000

100000

120000

140000

160000

Spendin

g (

$)

Tra

ffic

time slot

trafficbaseline

# layers:2# layers:4


Spendings over time with different # of layers (budget: $2000)

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70 80 90 20000

40000

60000

80000

100000

120000

140000

160000

Spendin

g (

$)

Tra

ffic

time slot

trafficbudget:$1000

budget:$2000budget:$3000


Spendings over time with different budget (# layers: 8)

0

0.5

1

1.5

2

2.5

3

0 10 20 30 40 50 60 70 80 90 20000

40000

60000

80000

100000

120000

140000

160000

eC

PC

($)

Tra

ffic

time slot

trafficbaseline



eCPC over time with different # of layers (budget: $2000)

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 10 20 30 40 50 60 70 80 90 20000

40000

60000

80000

100000

120000

140000

160000

eC

PC

($)

Tra

ffic

time slot

trafficbudget:$1000



eCPC over time with different budget (# of layers: 8)


Evaluations: Simulations (w eCPC goal)

0

5

10

15

20

25

30

0 10 20 30 40 50 60 70 80 90 20000

40000

60000

80000

100000

120000

140000

160000

Spendin

g (

$)

Tra

ffic

time slot

trafficbaseline



Spendings over time with different # of layers (budget: $2000)

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80 90 20000

40000

60000

80000

100000

120000

140000

160000

Spendin

g (

$)

Tra

ffic

time slot

trafficbudget:$1000



Spendings over time with different budget (# layers: 8)

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 10 20 30 40 50 60 70 80 90 20000

40000

60000

80000

100000

120000

140000

160000

eC

PC

($)

Tra

ffic

time slot

trafficbaseline



eCPC over time with different # of layers (budget: $2000)

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 10 20 30 40 50 60 70 80 90 20000

40000

60000

80000

100000

120000

140000

160000

eC

PC

($)

Tra

ffic

time slot

trafficbudget:$1000



eCPC over time with different budget (# of layers: 8)


Evaluations: Simulations (compare to state-of-the-art)

Comparison with the pacing algorithm proposed by Agarwal et al.[Agarwal 2014].

Their approach: conventional controller, not considering performance;

Our approach: adaptive controller, considering performance.

0

500

1000

1500

2000

0 200 400 600 800 1000 1200 1400 0

1

2

3

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7

Cum

ula

tive s

pendin

g (

$)

eC

PC

($)

time slot

AllocationSpend SmartPacing

Spend Agarwal et aleCPC SmartPacing

eCPC Agarwal et al


Evaluations: Simulations (compare to state-of-the-art)

Comparison with the pacing algorithm proposed by Agarwal et al.[Agarwal 2014].

Their approach: conventional controller, not considering performance;

Our approach: adaptive controller, considering performance.

0

500

1000

1500

2000

0 200 400 600 800 1000 1200 1400 0

1

2

3

4

5

6

7

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eC

PC

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time slot



eCPC Agarwal et al

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System Implementation

Besides the common implementation considerations of a large scale adserving system, we focus on two components to support smart pacing:

Real-time feedback of layered statisticsUse message queue to rapidly send delivery message to control server;Use remote procedure call to send pacing rate and quick stop messageto serving boxes.

In-memory data sourcesUse snapshot + message queue to avoid data loss;Use aggregation to reduce memory usage.

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References

D. Agarwal, S. Ghosh, K. Wei, and S. You

Budget pacing for targeted online advertisements at Linkedin

Proceedings of the 20th ACM SIGKDD, 2014

K.-C. Lee, A. Jalali, and A. Dasdan

Real time bid optimization with smooth budget delivery in online advertising

The Seventh International Workshop on Data Mining for Online Advertising, 2013.

A. Bhalgat, J. Feldman, and V. Mirrokni

Online allocation of display ads with smooth delivery

Proceedings of the 18th ACM SIGKDD, 2012.

Z. Abrams, O. Mendelevitch, and J. Tomlin

Optimal delivery of sponsored search advertisements subject to budget constraints

Proceedings of the 8th ACM conference on Electronic Commerce, 2007.

C. Borgs, J. Chayes, N. Immorlica, K. Jain, O. Etesami, and M. Mahdian

Dynamics of bid optimization in online advertisement auctions

Proceedings of the 16th international conference on World Wide Web, 2007.


Thank you!


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