Sponsored Search

Sponsored Search

Cory Pender

Sherwin Doroudi

Optimal Delivery of Sponsored Search Advertisements Subject to Budget Constraints

Zoe Abrams

Ofer Mendelevitch

John A. Tomlin

Introduction

Search engines (Google, Yahoo!, MSN) auction off advertisement slots on search page related to user’s keywords

Pay per click Earn millions a day through these

auctions– Auction type is important

Sponsored search parameters

Bids Query frequencies

– Not controlled by advertisers or search engine– Few queries w/ large volume, many with low

volume Advertiser budgets Pricing and ranking algorithm

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Solution

Focus on small subset of queries– Predictable volumes in near future

– Constitute large amount of total volume

Sponsored search parameters Bids Query frequencies Advertiser budgets

– Controlled by advertisers

Pricing and ranking algorithm– Generalized second price (GSP) auction– Rankings according to (bid) x (quality score)– Charged minimum price needed to maintain rank

Goal: take these parameters into account, maximize revenue

Motivating example

Bidder Bid for q1 Bid for q2 Budget

b1 C1 + C1 C1

b2 C1 0 C1

b3 C1 - C1 - 2 C1

Allocation Shown for q1

Shown for q2

Total Revenue

Greedy b1 b3 C1 +

Optimal b2 b1 2C1 -

Reserve price is

Problem Definition Queries Q = {q1, q2, q3, ..., qN}

Bidders B = {b1, b2, b3, ..., bM}

Bidding state A(t); Aij(t) is j’s bid

for i-th query dj is j’s daily budget

vi is estimate of query frequency

Li = {jp : jp B, p = 1, ..., Pi}

Lik = {jik : jik Li, l ≤ Li

k ≤ P}

Ranking and revenue

Bid-ranking - Revenue-ranking - So, for slate k, Price per click: Independent click through rates Revenue-per-search:


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Total revenue:QuickTime™ and a


Bidder’s cost

Total spend for j: QuickTime™ and a




Linear program Queries i = 1, ..., N Bidders j = 1, ..., M Slates k = 1, ..., Ki

Data: dj, vi, cijk, rik

Variables: xik

Constraints:– Budget:

– Inventory:





Objective function

Maximize revenue:

Value objective:

Clicks objective:







Column Generation Each column represents a slate Could make all possible columns

– But for each query, exponential in number of bidders

Start with some initial set of columns j: Marginal value for j’s budget

i: Marginal value for ith keyword

Profit if Maximize


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How to maximize? If small number of bidders for a query,

enumerate all legal subsets Lik, find

maxima, see if adding increases profit Otherwise, use algorithm described in

another paper

tigerdirect.com





?

nextag.com

priceline.com

ebay.com

Summary (so far)

Various bidders vying for spots on the slate for each query

Constrained by budget, query frequencies, ranking method

Solve LP for some initial set of slates Check if profit can be made by adding new

slates Re-solve LP, if necessary Can be applied to maximize revenue or

efficiency

Simulation Methodology

Compare this method to greedy algorithm– For greedy, assign what gets most revenue at the time;

when bidder’s budget is reached, take them out of the pool

Used 5000 queries For 11 days, retrieved hourly data on bidders,

bids, budgets To determine which ads appear, assign based on

frequencies fik = xik/vi

After each hour, see if anyone has exceeded budget

Simulation Results

Current method better than greedy method, when optimizing over revenue or efficiency

Larger gain for revenue when revenue optimized

Revenue and efficiency are closely tied

Gains when efficiency is maximized



Gains when revenue is maximized



Impact on bidders



Limitations

Illegitimate price hikes for other bidders if one person exceeds budget in middle of hour

Assumption that expected number of clicks are correct

For the purposes of the simulation, expect these to affect greedy and LP optimization similarly

Future work

Focus on less frequent queries– Frequencies harder to predict– Some work has been done (doesn’t incorporate

pricing and ranking) Keywords with completely unknown

frequencies Parallel processing for submarkets Investigate how advertisers might respond to

this method– Potential changes in reported bids/budgets

Sponsored Search

Documents

Transcript of Sponsored Search