Presentation on theme: "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."— Presentation transcript:
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 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) Standardized ad shapes with images Loosely related to content Context linked ads (Google AdSense) Related to content on page Search linked ads (Google Adwords) Related to search terms Banner ad Content ads
Search engine Advertisement Search engines are highly profitable 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) Standardized ad shapes with images Loosely related to content Context linked ads (Google AdSense) Related to content on page Search linked ads (Google Adwords) Related to search terms Organic search results Top-of-organic ads RHS ads
Search engine ads Ads are highly effective due to high relevance But even so, advertising still requires scale 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) Matches up those seeking info to those having info Matches up buyers with sellers Relevant disciplines 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 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 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 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 But people did not like their ranks be ad-driven They split the results: some organic, some ad-driven (2000) GoTo Ad Auction 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 (1 st price auction) (about $0.50) How-to bid example: 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 Ordering (Ranking) of ads based on expected revenue Auction-based Negotiation-based (used to)
Google’s auction version Rank ads by expected revenue 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 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) Adopted 2 nd price model in 2007 Increased their revenues
Google and Game theory When everyone bids optimally, we have Nash equilibrium Basic principle of equilibrium: each bidder prefers the position he/she is in to any other position Gives set of inequalities that can be analyzed to describe equilibrium Inequalities can also be inverted to give values as a function of bids 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 Price = marginal cost Marginal cost has to be increasing
Simple example Suppose all advertisers have same value v for click Two cases: Undersold auctions: There are more slots on page than bidders. r = The minimum price per click (the “reserved price”) (say, 5 cents). Oversold auctions: There are more bidders than slots on page. Last bidder pays price determined by 1 st excluded bidder.
Undersold pages Bidder in each slot must be indifferent to being in last slot v = value you get p s = price you pay at slot s x s = number of clicks in slot s r= reserve value x m = number of clicks in last position Payment for slot s = payment for last position + value of incremental clicks
Example of undersold case Two slots x 1 = 100 clicks x 2 = 80 clicks v= 0.50 r= 0.05 Solve equation P 1 * 100 = 0.05 * 80 + 0.50 * (100-80) p 1 = 14 cents, p 2 =5 cents 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 For previous 2-slot example, when 3 bidders: p s =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 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 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
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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