T his paper reports on a large-scale implementation of marketing science models to solve the bidding problem in search engine advertising. In cooperation with the online marketing agency SoQuero, we developed a fully automated bidding decision support system, PROSAD (PRofit Optimizing Search engine ADvertising; see http://www.prosad.de), and implemented it through the agency's bid management software. The PROSAD system maximizes an advertiser's profit per keyword without the need for human intervention. A closed-form solution for the optimized bid and a newly developed "costs-per-profit" heuristic enable advertisers to submit good bids even when there is significant noise in the data. A field experiment demonstrates that PROSAD can increase the return on investment by 21 percentage points and improve the yearly profit potential for SoQuero and its clients by E2.7 million.

In search engine marketing, such as on Google, advertisements’ ranking and prices paid per click result from generalized, second-price, sealed bid auctions that weight the submitted bids for each keyword by the quality of an advertisement. Conventional wisdom suggests that advertisers can only benefit from improving their advertisement's quality. With an empirical study, this article shows that quality improvements have complex effects whose returns are actually unclear: 5% of all quality improvements to an advertisement lead to higher prices (measured by price per click) per keyword, 100% to a higher number of clicks, 53% to higher costs for search engine marketing, and 37% to lower profits. Quality improvements lead to higher weighted bids, which only lower prices if they do not improve the ranking of the advertisement. Otherwise, better ranks likely lead to higher prices. A decomposition method can disentangle these effects and explain their effects on search engine marketing costs and profits. Finally, the results indicate that advertisers benefit if they lower their bids after improvements to advertising quality.

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Purpose: Advertisers setting up search engine advertising campaigns for the first time need to place bids on keywords, but typically lack experience and data to determine ranks that maximize a keyword's profit (generally referred to as a cold-start problem). This article aims at solving the problem of bidding on keywords in newly set-up search engine advertising campaigns. Approach:We suggest that advertisers collect data from the Google Keyword Planner to obtain precise estimates of the percentage increases in prices per click and clickthrough rates, which are needed to calculate optimal bids (exact approach). Together with the profit contribution per conversion and the conversion rate, the advertiser might then set bids that maximize profit. In case advertisers cannot afford to collect the required data, we suggest two proxy approaches and evaluate their performance using the exact approach as a benchmark.Findings: The empirical study shows that both proxy approaches perform reasonably well-the easier approach to implement (proxy 2) sometimes performs even better than the more sophisticated one (proxy 1). As a consequence, advertisers might just use this very simple proxy when bidding on keywords in newly set-up SEA campaigns. Originality/value:This research extends the stream of literature on how to determine optimal bids, which so far focuses on campaigns that are already running and where the required data to calculate bids is already available. This research offers a novel approach of determining bids when advertisers lack the aforementioned information.

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