Effect of Paid Placement

Internet search engines execute a user's search query on a database index, and typically return a set of results ranked according to their relevance score. Given these results, the user selects specific content providers in the list for further transactions. It is well known that the ranking of a result term is strongly correlated with the probability that the user will follow up on the result term ((McLuhan ( [#!McLuhan-2000!#]). Commercial content providers are interested in clickthroughs and conversion rates - i.e., the likelihood that a search engine user will enter into a commercial transaction with the content provider. For this reason, content providers have an incentive towards paid placement - to pay the search engine in order to be included, ranked highly, or prominently featured in the search result. In practice, this may mean a higher relevance score, a featured listing, or perhaps even a guaranteed retrieval for certain search terms. Figure  displays a screen shot from a comparison shopping engine that includes paid placement (featured listing), normal placement, and advertising.

  

Figure: Paid placement, regular listings, and advertising in a comparison shopping engine. The top listing and graphical icon increase the likelihood that the paid placement listing will be followed up.

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What is the impact of paid placement on a search engine's perceived quality? Here we assume that search engine can not hide the fact that they perform paid placement, because this can not exist in equilibrium in the long run (bidders can perceive finally), or may cause some serious legal issues . Articles in the business press and data from commercial research firms suggest that paid placement strategies have a negative impact on a search engine's perceived quality and credibility. Goodman [#!Goodman-2000!#] argues that search engines must act as ``referees - fair arbiters of relevance'' or they will lose market share. Since loss of market share causes a fall in advertising revenue, search engines must trade-off potential revenues from paid placement with those from advertising. To model the effect of paid placement, suppose that the search engine offers priority placement to content providers who pay a placement fee 12#12. Let x represent the percentage of free listings, hence 1 - x is the fraction of providers who choose paid placement. The search engine can bias its relevance scoring algorithm and displays in many ways. Let 13#13 be the extent of bias chosen by the search engine. We represent the positive effect of the bias 13#13 on content providers with the function 14#14, and the negative effect on users (after normalization, without loss of generality) as 13#13 itself. Assume 15#15, and 16#16. Hence 13#13 represent the users' perceived disutility of paid placement. In the current model, 13#13 is treated as exogenous. However, our future work aims to endogenize 13#13 and determine the optimal bias level. Due to the negative impact on users, we rewrite the utility function as 17#17. Providers who choose paid placement get additional benefit 14#14, so that provider 5#5's valuation increases to 18#18. The search engine now gets additional revenues

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