economicsOfSearchEngines

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The Economics of
Internet Search
Slides adopted from a talk by:
Hal R. Varian
Google’s Chief Economist
UC Berkeley Economics Prof.
Sept 31, 2007
Search engine Advertisement
Search engines are highly profitable
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Revenue comes from
selling ads related to queries
99% of Google’s revenue come from ads
Yahoo, MSN also uses similar model
Banner ads (Doubleclick)
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Standardized ad shapes with images
Loosely related to content
Context linked ads (Google AdSense)
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Related to content on page
Search linked ads (Google Adwords)

Related to search terms
Content
ads
Banner
ad
Search engine Advertisement
Search engines are highly profitable


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Revenue comes from
selling ads related to queries
99% of Google’s revenue come from ads
Yahoo, MSN also uses similar model
Top-of-organic ads
Banner ads (Doubleclick)
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Standardized ad shapes with images
Loosely related to content
Context linked ads (Google AdSense)
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Related to content on page
Search linked ads (Google Adwords)

Related to search terms
Organic
search
results
RHS ads
Search engine ads
Ads are highly effective due to high relevance

But even so, advertising still requires scale
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2% of ads (“impressions”) might get clicks
2% of clicks might convert into sales
So only 4 / 1000 who see an ad actually buy
Cost Per Million impressions (CPM) will not be large
But this performance is good
compared to conventional advertising!
Search technology exhibits increasing returns to scale

High fixed costs for infrastructure,
low marginal costs for serving
Advertising industry economies
Entry costs (at a profitable scale) are large
due to fixed costs
Very low incremental cost for 2nd ad
User switching costs are low
 56% of search engine users use more than one
 (but 44% use only one…)
Advertisers follow the eyeballs
 Place ads wherever there are sufficient users, no exclusivity
Hence market’s structure is likely to be
 A few large search engines in each language/country group
 Highly contestable market for users
 No demand-side network effects that drive
towards a single supplier so multiple players can co-exist
What services do S.E.’s provide?
Google is yenta (matchmaker)

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Matches up those seeking info
to those having info
Matches up buyers with sellers
Relevant disciplines

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Computer Science:
Theory of information retrieval
Economics:
The assignment problem: Match efficiently providers to consumers
The advent of the web
By mid-1990s IR algorithms were very mature

Very little difference in IR competitions (mostly CIA sponsored)
The Web comes along
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IR researchers were slow to react
CS researchers were quick to react
Crucial NSF grants:
1993 to UIC - NCSA (Mosaic);
1994 to Stanford (Digital Libraries Initiative)
Link structure of Web became new explanatory variable

PageRank improved relevance of search results dramatically
Google’s birth
Brin and Page tried to sell algorithm to Yahoo
for just $1 million

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Yahoo did not buy it;
they believed that search was in a commodity business,
nothing new to find
while Yahoo is in the Directory business,
it adds value to information
Most people thought this way, banner ads were the only (boring) game
They formed Google in late 1998
with no real idea of how they would make money
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Put a lot of effort into improving algorithms
Started selling intranet services (they still do)
Then tried selling keywords
(like everyone else) via negotiation
Why online business are different
Online businesses (Amazon, eBay, Google…)
can continually experiment

Hard to really do continuously for offline companies
(they sell a product)
 Manufacturing
 Services

Very easy to do continually for online companies
(they sell online service)
 Leads to very rapid (and subtle) improvement
 Learning-by-doing leads to significant competitive advantage
Business model
(aka: how to make money out of it?)
Initially: sell keywords

All SE’s and Dir’s did it; it does not scale
Then: Sell search ranking
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But people did not like their ranks be ad-driven
They split the results: some organic, some ad-driven (2000)
GoTo Ad Auction
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GoTo’s model was to auction search results
Changed name to Overture, auctioned ads
Google liked ad auction, tried to improve on Overture’s model (2001)
Lawsuit by Overture (2002) settled in 2003
Google licenses technology from Yahoo (bought Overture for $1.63B)
Original Overture model
Rank ads by bids
Ads assigned to slots
depending on bids

Highest bidders
get better (higher up) slots
High bidders pay
what they bid
(1st price auction)
(about $0.50)
How-to bid example:
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Conversion Rate = 1% sales per click
Net profit per sale = $20
If you bid PPC=$0.20/click, you are expected to break even
Google ads
Ads are selected
based on
query+keywords
Negotiation-based (used to)
Auction-based
Ordering (Ranking)
of ads based on
expected revenue
Google’s auction version
Rank ads by expected revenue
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This makes sense: revenue = price * quantity
Expected revenue = bid * expected clicks
But you sell impressions, so
price /impression = Price /click * clicks /impression
Each bidder pays price determined
by bidder below him
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Price = minimum bid necessary to retain position
Motivated by engineering, not economics
People would do it by trial & error anyway…
Overture (now owned by Yahoo)
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Adopted 2nd price model in 2007
Increased their revenues
Google and Game theory
When everyone bids optimally, we have Nash equilibrium
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Basic principle of equilibrium:
each bidder prefers the position he/she is in
to any other position
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Gives set of inequalities that can be analyzed to describe equilibrium
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Inequalities can also be inverted to give values as a function of bids
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The highest value people end up in higher positions
Implications of analysis
Basic theoretical result:
incremental cost per click has to be increasing
in the click through rate.
Why?
If incremental cost per click ever decreased,
then someone bought expensive clicks
and passed up cheap ones.
Similar to classic competitive pricing in economics
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Price = marginal cost
Marginal cost has to be increasing
Simple example
Suppose all advertisers have same value v for click
Two cases:
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Undersold auctions:
There are more slots on page than bidders.
r = The minimum price per click (the “reserved price”)
(say, 5 cents).
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Oversold auctions:
There are more bidders than slots on page.
Last bidder pays price determined by 1st excluded bidder.
Undersold pages
Bidder in each slot must be indifferent to being in last slot
(v  ps ) xs  (v  r ) xm
v
ps
xs
r
xm
= value you get
= price you pay at slot s
= number of clicks in slot s
= reserve value
= number of clicks in last position
ps x s  rx m  v(x s  x m )
Payment for slot s =
payment for last position + value of incremental clicks

Example of undersold case
Two slots
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x1
x2
v
r
= 100 clicks
= 80 clicks
= 0.50
= 0.05
(v  ps ) xs  (v  r ) xm
ps x s  rx m  v(x s  x m )
Solve equation
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P1 * 100 = 0.05 * 80 + 0.50 * (100-80)
2=5 cents
p1 = 14 cents, p
Google’s Revenue = 0.14 * 100 + 0.05 * 80 = $18
Oversold pages
Each bidder has to be indifferent
between having his slot and not being shown:
So
(v  p )x  0  p  v
s

s
s
For previous 2-slot example, when 3 bidders:
ps=50 cents for each slot, and
Google’s revenue = 0.50 x (100+80) = $90
Revenue takes big jump
when advertisers have to compete for slots!
How Google determines number of ads shown
Showing more ads
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Pushes revenue up,
particularly moving from undersold to oversold
But relevancy goes down (irrelevant ads will sneak in)
=> Users click less in future (“ad blindness”)
Optimal choice

Depends on balancing
short run profit against long run goals
Top-of-organic vs RHS ads
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Initially top ads sold negotiated, but RHS revenue much higher
Now all are auctioned,
but for top ads are only selected if they are very relevant
If you only see RHS ads: they were good enough to be shown,
but not so relevant to be on top of organic
Other forms of online ads
Contextual ads
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AdSense puts relevant text ads next to content
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Treats the content of the page as a query!
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Publisher (content owner) puts some Javascript on page
and shares in revenue from ad clicks
Display ads (aka “Rich Media Ad”)
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Advertiser negotiates with publisher for CPM (price) and impressions
(at specific time, for specific audience, for up to X impressions/viewer)
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Ad server (e.g. Doubleclick) serves up ads to pub server
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Are more sophisticated than simple ads (e.g. max num of impr/IP addr.)
Ad effectiveness
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Increase reach
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Target frequency
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Privacy issues
Monitoring and fine-tuning
Opportunities for employment
“Marketing as the new finance”
Availability of real time data allows for fine tuning,
constant improvement
Market prices reflect value
Quantitative methods are very valuable
We are just at the beginning…
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