Pricing Paid Placements on Search Engines

Abstract

ABSTRACT The objective of this research is to identify the optimal pricing strategy for paid placements on engines' search-results listings. To accomplish this we develop a mathematical model incorporating a constellation of parameters that describe buyers' online intensity, competition among online sellers, and co-opetition between the online sellers and engine. This model allows us to analyze three pricing strategies, namely pay-per-purchase (PPP), pay-per-click (PPC), and flat-fee (FF), for paid placement services. The paper then compares these pricing strategies in terms of their revenue potential for a engine and identifies conditions when one pricing strategy is preferred over the other. Our analysis shows that PPC, the most popular pricing strategy, is not the optimal strategy to use when the proportion of buyers, who online and end up buying online, is high. Instead the engines would be better off by using PPP strategy. Another finding is that it is not always optimal to price paid-placements in proportion to their rank in the results' listings. For instance, our analysis shows that when the proportion of buyers with low intensity is high and a engine is following a PPC pricing strategy, then it is better off charging a higher price for a lower-ranked listing. Keywords: SEM, SEO, paid placement, pricing strategy, engine, e-commerce (ProQuest: ... denotes formulae omitted.) 1. Introduction Extensive research has been done on factors that influence online users' choice of one web site over others [e.g. Subramony 2002; Lightner & Eastman 2002]. However, there are hardly any studies on the role played by engines in directing the online users, specifically potential online buyers, to particular websites despite that fact that engines play a crucial role in web-based e-commerce transactions by bridging the gap between online buyers and sellers. Existing research on buyers' online-search behavior finds that the use of engines to look for product and price information dominates other forms of online-search strategies [Sen et al. 2006]. This dependence of the browsing population on engines makes it important that the sellers develop strategies that improve their visibility in the search provided to the buyers. One strategy commonly used to improve visibility is to buy keyword-related banner advertisement on the search page. However, a study from NPD Group [Bruemmer 2002] found that standard banner or button advertisements are not as effective as listings when it comes to brand recall, favorable opinion rating and inspiring purchases. In unaided recall, listings outperformed banners and buttons by three to one. However, just being listed in the results is not enough. Sellers should aim to maximize the traffic that comes via engines to their web site. To maximize this traffic, sellers need to ensure a preferential placement of their website address, i.e. it should appear in the top 20 matches. It's highly unlikely that a seller's site will be visited if it is listed in the engine, but in the back pages of results. Research has shown that users hardly ever go beyond the top 30 engine listings for a single search. It is estimated that the top 30 results receive over 90% of traffic [Bruemmer 2002]. Sellers can improve their listing on the search pages-(a) by engine optimization (SEO)1 e.g., making changes to their site code to make it more relevant and therefore more engine compatible, and/or (b) by paying the engines for preferred placements [Hansell 2001; Bhargava & Feng 2002; Sullivan 2002a]. SEO is something that all sellers can do, which makes it difficult to get any sustainable competitive advantage by just using this method. Furthermore, the initial results of an optimization campaign can take up to one hundred twenty (120) days after submission before the results become visible. …

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