We study the impact of changes in the competitors' listings in organic search results on the performance of sponsored search advertisements. Using data from an online retailer's keyword advertising campaign, we measure the impact of organic competition on both click-through rate and conversion rate of sponsored search advertisements. We find that an increase in organic competition leads to a decrease in the click performance of sponsored advertisements. However, organic competition helps the conversion performance of sponsored ads and leads to higher revenue. We also find that organic competition has a higher negative effect on click performance than does sponsored competition. Our results inform advertisers on how the presence of organic results influences the performance of their sponsored advertisements. Specifically, we show that organic competition acts as a substitute for clicks, but has a complementary effect on the conversion performance.
Cooperative caching is a popular mechanism to allow an array of distributed caches to cooperate and serve each others' Web requests. Controlling duplication of documents across cooperating caches is a challenging problem faced by cache managers. In this paper, we study the economics of document duplication in strategic and nonstrategic settings. We have three primary findings. First, we find that the optimum level of duplication at a cache is nondecreasing in intercache latency, cache size, and extent of request locality. Second, in situations in which cache peering spans organizations, we find that the interaction between caches is a game of strategic substitutes wherein a cache employs lesser resources towards eliminating duplicate documents when the other caches employs more resources towards eliminating duplicate documents at that cache. Thus, a significant challenge will be to simultaneously induce multiple caches to contribute more resources towards reducing duplicate documents in the system. Finally, centralized decision making, which as expected provides improvements in average latency over a decentralized setup, can entail highly asymmetric duplication levels at the caches. This in turn can benefit one set of users at the expense of the other, and thus will be challenging to implement.
Information specialists in enterprises regularly use distributed information retrieval (DIR) systems that query a large number of information retrieval (IR) systems, merge the retrieved results, and display them to users. There can be considerable heterogeneity in the quality of results returned by different IR servers. Further, because different servers handle collections of different sizes and have different processing and bandwidth capacities, there can be considerable heterogeneity in their response times. The broker in the DIR system has to decide which servers to query, how long to wait for responses, and which retrieved results to display based on the benefits and costs imposed on users. The benefit of querying more servers and waiting longer is the ability to retrieve more documents. The costs may be in the form of access fees charged by IR servers or user's cost associated with waiting for the servers to respond. We formulate the broker's decision problem as a stochastic mixed-integer program and present analytical solutions for the problem. Using data gathered from FedStats-a system that queries IR engines of several U.S. federal agencies-we demonstrate that the technique can significantly increase the utility from DIR systems. Finally, simulations suggest that the technique can be applied to solve the broker's decision problem under more complex decision environments.
In peer-to-peer (P2P) media distribution, users obtain content from other users who already have it. This form of decentralized product distribution demonstrates several unique features. Only a small fraction of users in the network are queried when a potential adopter seeks a file, and many of these users might even free-ride, i.e., not distribute the content to others. As a result, generated demand might not always be fulfilled immediately. We present mixing models for product diffusion in P2P networks that capture decentralized product distribution by current adopters, incomplete demand fulfillment and other unique aspects of P2P product diffusion. The models serve to demonstrate the important role that P2P search process and distribution referrals-payments made to users that distribute files-play in efficient P2P media distribution. We demonstrate the ability of our diffusion models to derive normative insights for P2P media distributors by studying the effectiveness of distribution referrals in speeding product diffusion and determining optimal referral policies for fully decentralized and hierarchical P2P networks.