Sponsored search accounts for 40% of the total online advertising market. These ads appear as ordered lists along with the regular search results in search engine results pages. The conventional wisdom in the industry is that the top position is the most desirable position for advertisers. This has led to intense competition among advertisers to secure the top positions in the results pages.We evaluate the impact of ad placement on revenues and profits generated from sponsored search using data for several hundred keywords from the ad campaign of an online retailer. Using a hierarchical Bayesian model, we measure the impact of ad placement on both click-through rate and conversion rate for these keywords. We find that while click through rate decreases with position, conversion rate first increases and then decreases with position for longer keywords. The net effect is that, contrary to conventional wisdom, the topmost position in sponsored search advertisements is not necessarily the revenue-or profit-maximizing position. Our results inform the advertising strategies of firms participating in sponsored search auctions and provide insight into consumer behavior in these environments. Specifically, they help correct a significant misunderstanding among advertisers regarding the value of the top position. Further, they reveal potential inefficiencies in present auction mechanisms used by the search engines. the likelihood that a consumer will buy a product) and advertising costs.2 Thus, the net impact of ad position on overall revenues and profits is not well understood.In this paper we address this question by empirically analyzing how ad position in sponsored search impacts an advertiser's revenues and overall profits. We use a unique panel dataset from a Search Engine Marketing (SEM) firm that catalogs daily clicks, conversions, and cost data for multiple keywords sponsored by one of its clients. One of the challenges with sponsored search data is that clicks and conversions are sparse. In order to address this, we use a hierarchical Bayesian model to analyze the click and conversion probabilities in this environment while accounting for heterogeneity across keywords. Our findings suggest that, contrary to conventional wisdom, the topmost positions for keywords in our dataset are associated with lower revenues relative to lower (and less expensive) positions. Our results confirm that ad clickthrough rate decreases with position. However, we find that the conversion rate and revenue initially increase and then decrease with ad position for longer keyphrases. For shorter keyphrases, the revenue decreases with position. However, the costs are much higher in the top positions resulting in higher profits at lower position.Our paper makes two main contributions. First, our paper provides key managerial insights for advertisers. A common assumption in the industry is that the value of a click from a sponsored search campaign is independent of the position of the advertisement. Our results indicate this is not...