Born to trade: a genetically evolved keyword bidder for sponsored search

In sponsored search auctions, advertisers choose a set of keywords based on products they wish to market. They bid for advertising slots that will be displayed on the search results page when a user submits a query containing the keywords that the advertiser selected. Deciding how much to bid is a real challenge: if the bid is too low with respect to the bids of other advertisers, the ad might not get displayed in a favorable position; a bid that is too high on the other hand might not be profitable either, since the attracted number of conversions might not be enough to compensate for the high cost per click. In this paper we propose a genetically evolved keyword bidding strategy that decides how much to bid for each query based on historical data such as the position obtained on the previous day. In light of the fact that our approach does not implement any particular expert knowledge on keyword auctions, it did remarkably well in the Trading Agent Competition at IJCAI2009

Multi-keyword multi-click advertisement option contracts for sponsored search

In sponsored search, advertisement (abbreviated ad) slots are usually sold by a search engine to an advertiser through an auction mechanism in which advertisers bid on keywords. In theory, auction mechanisms have many desirable economic properties. However, keyword auctions have a number of limitations including: the uncertainty in payment prices for advertisers; the volatility in the search engine's revenue; and the weak loyalty between advertiser and search engine. In this paper we propose a special ad option that alleviates these problems. In our proposal, an advertiser can purchase an option from a search engine in advance by paying an upfront fee, known as the option price. He then has the right, but no obligation, to purchase among the pre-specified set of keywords at the fixed cost-per-clicks (CPCs) for a specified number of clicks in a specified period of time. The proposed option is closely related to a special exotic option in finance that contains multiple underlying assets (multi-keyword) and is also multi-exercisable (multi-click). This novel structure has many benefits: advertisers can have reduced uncertainty in advertising; the search engine can improve the advertisers' loyalty as well as obtain a stable and increased expected revenue over time. Since the proposed ad option can be implemented in conjunction with the existing keyword auctions, the option price and corresponding fixed CPCs must be set such that there is no arbitrage between the two markets. Option pricing methods are discussed and our experimental results validate the development. Compared to keyword auctions, a search engine can have an increased expected revenue by selling an ad option.Comment: Chen, Bowei and Wang, Jun and Cox, Ingemar J. and Kankanhalli, Mohan S. (2015) Multi-keyword multi-click advertisement option contracts for sponsored search. ACM Transactions on Intelligent Systems and Technology, 7 (1). pp. 1-29. ISSN: 2157-690

Price Cycles in Online Advertising Auctions

Paid placement in search engines has become one of the most successful and rapidly growing sectors of the online advertising industry. The innovative use of auctions to sell keyword-related advertisement positions is perhaps the most important factor driving the success of this market. There has been no systematic analysis, however, of the advertisers’ strategies to bid for ranks in a dynamic environment, where each bidder’s bid can be updated and observed by the competitors in real time. We capture this dynamic setting using a Markov process and identify the Markov perfect equilibrium. We find that in such a dynamic environment, bidders’ bidding strategies follow a cyclical pattern (Edgeworth cycle) similar to that conjectured by Edgeworth (1925) in a totally different context. A new data set that contains a detailed bidding history of all advertisers for sample keywords in a leading search engine makes it possible for us to study the real-world behavior of bidders. We propose an empirical framework based on maximum likelihood estimation of latent Markov state switching to confirm the theory. We also discuss the theoretical and practical significance of finding such cycles in an online market place

Do Organic Results Help or Hurt Sponsored Search Performance

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

Pricing average price advertising options when underlying spot market prices are discontinuous

Advertising options have been recently studied as a special type of guaranteed contracts in online advertising, which are an alternative sales mechanism to real-time auctions. An advertising option is a contract which gives its buyer a right but not obligation to enter into transactions to purchase page views or link clicks at one or multiple pre-specified prices in a specific future period. Different from typical guaranteed contracts, the option buyer pays a lower upfront fee but can have greater flexibility and more control of advertising. Many studies on advertising options so far have been restricted to the situations where the option payoff is determined by the underlying spot market price at a specific time point and the price evolution over time is assumed to be continuous. The former leads to a biased calculation of option payoff and the latter is invalid empirically for many online advertising slots. This paper addresses these two limitations by proposing a new advertising option pricing framework. First, the option payoff is calculated based on an average price over a specific future period. Therefore, the option becomes path-dependent. The average price is measured by the power mean, which contains several existing option payoff functions as its special cases. Second, jump-diffusion stochastic models are used to describe the movement of the underlying spot market price, which incorporate several important statistical properties including jumps and spikes, non-normality, and absence of autocorrelations. A general option pricing algorithm is obtained based on Monte Carlo simulation. In addition, an explicit pricing formula is derived for the case when the option payoff is based on the geometric mean. This pricing formula is also a generalized version of several other option pricing models discussed in related studies.Comment: IEEE Transactions on Knowledge and Data Engineering, 201

Dynamic bidding strategies in search-based advertising

Cataloged from PDF version of article.Search-based advertising allows the advertisers to run special campaigns targeted to different groups of potential consumers at low costs. Google, Yahoo and Microsoft advertising programs allow the advertisers to bid for an ad position on the result page of a user's query when the user searches for a keyword that the advertiser relates to its products or services. The expected revenue generated by the ad depends on the ad position, and the ad positions of the advertisers are concurrently determined after an instantaneous auction based on the bids of the advertisers. The advertisers are charged only when their ads are clicked by the users. To avoid excessive ad expenditures due to sudden surges in the keyword-search activities, each advertiser reserves a fixed finite daily budget, and the ads are not shown in the remainder of the day when the budget is depleted. Arrival times of keyword-search instances, ad positions, ad selections, and sales generated by the ads are random. Therefore, an advertiser faces a dynamic stochastic total net revenue optimization problem subject to a strict budget constraint. Here we formulate and solve this problem using dynamic programming. We show that there is always an optimal dynamic bidding policy. We describe an iterative numerical approximation algorithm that uniformly converges to the optimal solution at an exponential rate of the number of iterations. We illustrate the algorithm on numerical examples. Because dynamic programing calculations of the optimal bidding policies are computationally demanding, we also propose both static and dynamic alternative bidding policies. We numerically compare the performances of optimal and alternative bidding policies by systematically changing each input parameter. The relative percentage total net revenue losses of the alternative bidding policies increases with the budget loading, but were never more than 3.5 % of maximum expected total net revenue. The best alternative to the optimal bidding policy turned out to be a static greedy bidding policy. Finally, statistical estimation of the model parameters is visited

Models for Budget Constrained Auctions: An Application to Sponsored Search & Other Auctions

The last decade has seen the emergence of auction mechanisms for pricing and allocating goods on the Internet. A successful application area for auctions has been sponsored search. Search firms like Google, Bing and Yahoo have shown stellar revenue growths due to their ability to run large number of auctions in a computationally efficient manner. The online advertisement market in the U.S. is estimated to be around $41 billion in 2010 and expected to grow to$50 billion by 2011 (http://www.marketingcharts.com/interactive/us-online-advertising-market-to-reach-50b-in-2011-3128/). The paid search component is estimated to account for nearly 50% of online advertising spend. This dissertation considers two problems in the sponsored search auction domain. In sponsored search, the search operator solves a multi-unit allocation and pricing problem with the specified bidder values and budgets. The advertisers, on the other hand, regularly solve a bid determination problem for the different keywords, given their budget and other business constraints. We develop a model for the auctioneer that allows the bidders to place differing bids for different advertisement slots for any keyword combination. Despite the increased complexity, our model is solved in polynomial time. Next, we develop a column-generation procedure for large advertisers to bid optimally in the sponsored search auctions. Our focus is on solving large-scale versions of the problem. Multi-unit auctions have also found a number of applications in other areas that include supply chain coordination, wireless spectrum allocation and transportation. Current research in the multi-unit auction domain ignores the budget constraint faced by participants. We address the computational issues faced by the auctioneer when dealing with budget constraints in a multi-unit auction. We propose an optimization model and solution approach to ensure that the allocation and prices are in the core. We develop an algorithm to determine an allocation and Walrasian equilibrium prices (when they exist) under additive bidder valuations where the auctioneer's goal is social welfare maximization and extend the approach to address general package auctions. We, also, demonstrate the applicability of the Benders decomposition technique to model and solve the revenue maximization problem from an auctioneer's standpoint