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LECTURE 10: ECONOMICS OF ONLINE
ADVERTISING
AEM 4550:
Economics of Advertising
Prof. Jura Liaukonyte
Lecture Plan
 HW 3
 Required additional readings:
 HBS case: Google Inc.
 HBS industry note: Paid Search Advertising
 Online Advertising Models
 Long Tail
 Real time bidding
Online Advertising
US Consumer Time Spent versus
Media Ad Spending, 2013
PAY-PER-CLICK Advertising Model
 Targeted advertisement based on two effectiveness
measures:
1.
Click-Through Rate (CTR): specifies on how many ads X, out of
the total number of ads Y shown to the visitors, the visitors
actually clicked; in other words, CTR = X/Y. CTR measures how
often visitors click on the ad
2.
Conversion Rate: it specifies the percentage of visitors who
took the conversion action. Conversion rate gives a sense of
how often visitors actually act on a given ad, which is a better
measure of ad’s effectiveness than the CTR measure
Recent History of CPC Method
 Cost Per Click is the predominant advertising payment
method, made popular by search engines such a Google and
Overture (now part of Yahoo!).
 Google introduced CPC AdWords program in 2002.
 Combining a particular ad payment method with a particular
targeting method. For Google and Yahoo! the two main
models are the keyword-based PPC and the content-based
PPC models.
Cost Per Click
Two problems with the Cost per click model:
 Although correlated, good click-through rates are still not
indicative of good conversion rates
 It does not offer any “built in” fundamental protection
mechanisms against the click fraud
Problems with PAY-PER-CLICK
 CLICK FRAUD: People clicking on products/advertisements
excessively without the real intent of actually making any
purchases.
Click fraud in AdWords
 Make the competitor pay more
 If you’re second, click on the competitor’s advertisement
enough so that he will hit his budget for the day
Google’s Pay-per-Click Advertising
Model
 AdWords
 A program allowing advertisers to purchase CPC-based
advertising that targets the ads based on the keywords specified
in the users’ search queries.
 Ad Rank = CPC x QualityScore
 QualityScore- a measure identifying the “quality” of the
keyword and the ad combined
 The more the advertiser is willing to pay (CPC) and the higher the
click through rate on the ad (CTR), the higher the position of the
ad in the listing is.
Example of Search Ads in a Search Engine
Paid
Organic
Paid
The “Golden Triangle” for Search
Engine Results
Creating an AdWords Ad
15
Temporal Pattern
of Influence
in Consumer
Auto Searches
Google AdSense
 Google AdSense is a
program for website owners
to display Google’s ads on
their websites and earn
money from Google as a
result.
Uses of AdSense
 AdSense for Search (AFS): publishers allow Google to place its
ads on their websites when the user does keyword-based
searches on their sites.
 AdSense for Content (AFC): the system that automatically
delivers targeted ads to the publisher’s web pages that the
user is visiting. These ads are based on the content of the
visited pages, geographical location and some other factors.
AdSense for
Content
 Contextually-targeted
ads
 Example: cheese.com
Opportunity Cost
 Calculate the missed opportunity cost (forgone revenue)
# of people
searching for a
specific
keyword
x
engine share
expected
(Google ~=
click-through
x
80%)
rate
x
average
conversion
rate
x
average
transaction
amount
 E.g.10,000/day x 80% x 10% x 5% x $100 = $4,000/day
Challenge to advertisers
 Keyword choice
 This is the most critical
 Market efficiencies: high CTR words have high prices
 What matters is the cost effectiveness: the ROI or ROA
 E.g., plurals get more clicks and more conversions than
singulars: “Diamonds” more valuable than “diamond”
 How much to bid
 Measure cost-per-acquisition and/or ROA
Bidding Strategy
 Determine value per click
 Probability of purchase x profit margin
 Determine relationship between cost and clicks
 How much do you have to pay to get x clicks?
 Equivalently use incremental cost per click
BIG DATA AND ADVERTISING
What is BIG DATA?
 Collection of data sets so large and complex that it becomes
difficult to process using on-hand database management
tools or traditional data processing applications.
 Data sets grow in size in part because they are increasingly
being gathered by ubiquitous information-sensing mobile
devices, aerial sensory technologies, software logs, cameras,
microphones, etc.
 The volume of business data worldwide, across all companies,
doubles every 1.2 years, according to some estimates
What is BIG DATA?
 Data companies are collecting enormous amounts of
information about consumers.
 They sell information about certain demographic factors:
 whether you're pregnant or
 divorced or
 trying to lose weight,
 about how rich you are and
 what kinds of cars you have
 Are you an allergy sufferer?
How much do these companies
know about individual people?
 names, addresses and contact information, demographics,
like age, race, occupation and education level
 Data on life event triggers:
 getting married,
 buying a home,
 sending a kid to college
 getting divorced.
How much do these companies
know about individual people?
 Marketing divisions of Credit report agencies (Experian,
Equifax) can and do sell:
 names of expectant parents and families with newborns
 http://www.experian.com/marketing-services/life-event-
marketing.html
 Salary and pay-stub information
Where are they getting this info
from?
 The stores where you shop sell it to these data brokers.
 Store loyalty cards is a primary source of data
 Government records and other publicly available information
 Department of Motor Vehicles may sell personal information
such as name, address, and the type of vehicles you own — to
data brokers
 Public voting records, which include information about your
party registration and how often you vote can be sold to data
companies
What about Online?
 Some data companies companies record and then resell
screen names, web site addresses, interests, hometown and
professional history, and how many friends or followers you
have.
 Some companies also collect and analyze information about
users’ tweets, posts, comments, likes, shares, and
recommendations.
 Collects information about which social media sites individual
people use, and whether they are a heavy or a light user.
Regulation
 9 Data brokers are currently under review by the FTC. (1)
Acxiom, 2) Corelogic, 3) Datalogix, 4) eBureau, 5) ID Analytics,
6) Intelius, 7) Peekyou, 8) Rapleaf, and 9) Recorded Future.)
 The FTC is seeking details about:
 the nature and sources of the consumer information the data
brokers collect;
 how they use, maintain, and disseminate the information; and
 the extent to which the data brokers allow consumers to access
and correct their information or to opt out of having their personal
information sold.
Required “reading” for the exam!
 http://www.youtube.com/watch?v=Sh4ePpDn960
 Note:
 What are the most current trends in advertising buying?
 What are the implications of such changing conditions for
consumers, advertisers and content providers
The Long Tail
 The internet vs. brick-and-mortar
 Nearly unlimited capacity
 Distribution and shelving costs approaching zero
 Global distribution channels
 A changing economy
 Popularity no longer has a monopoly on profitability
 Can generate significant revenues by selling small number of
millions of niche products vs. selling millions of a small number of
“hits”
The Long Tail
Wal-Mart vs. Rhapsody
 Wal-Mart
 39,000 songs on CDs in average store
 Must sell at least 100,000 copies of a CD to cover its retail
overhead and make a sufficient profit
 Less than 1 percent of CDs sell that much
 Therefore, can carry only “hits”
 Itunes/Rhapsody/Spotify
 Millions of songs in archives
 Cost of storing one more song is essentially zero
 More streams each month beyond its top 10,000 than in the
top 10,000
 Therefore, no economic reason not to carry almost everything
Long Tail Examples: Travel
Netflix Long Tail