Keyword Targeting Optimization in Sponsored Search Advertising: Combining Selection and Matching

In sponsored search advertising (SSA), advertisers need to select keywords and determine matching types for selected keywords simultaneously, i.e., keyword targeting. An optimal keyword targeting strategy guarantees reaching the right population effectively. This paper aims to address the keyword targeting problem, which is a challenging task because of the incomplete information of historical advertising performance indices and the high uncertainty in SSA environments. First, we construct a data distribution estimation model and apply a Markov Chain Monte Carlo method to make inference about unobserved indices (i.e., impression and click-through rate) over three keyword matching types (i.e., broad, phrase and exact). Second, we formulate a stochastic keyword targeting model (BB-KSM) combining operations of keyword selection and keyword matching to maximize the expected profit under the chance constraint of the budget, and develop a branch-and-bound algorithm incorporating a stochastic simulation process for our keyword targeting model. Finally, based on a realworld dataset collected from field reports and logs of past SSA campaigns, computational experiments are conducted to evaluate the performance of our keyword targeting strategy. Experimental results show that, (a) BB-KSM outperforms seven baselines in terms of profit; (b) BB-KSM shows its superiority as the budget increases, especially in situations with more keywords and keyword combinations; (c) the proposed data distribution estimation approach can effectively address the problem of incomplete performance indices over the three matching types and in turn significantly promotes the performance of keyword targeting decisions. This research makes important contributions to the SSA literature and the results offer critical insights into keyword management for SSA advertisers.Comment: 38 pages, 4 figures, 5 table

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

Liquidity constraints of the middle class

There is evidence that a household's consumption response to transitory income does not decline, and perhaps increases, with the level of financial assets it holds. That is, middle class households with assets act as if they face liquidity constraints. This paper addresses this puzzling observation with a model of impatient households that face a large recurring expenditure. In spite of impatience, they save as this expenditure draws near. The authors call such saving made in preparation for a foreseeable event at a given future date "term saving." Term saving reverses the role of assets in the presence of liquidity constraints.

Evaluation and Design of Supply Chain Operations using DEA

Performance evaluation has been one of the most critical components in management. As production systems nowadays consist of a growing number of integrated and interacting processes, the interrelationship and dynamic among processes have create a major challenge in measuring system and process performance. Meanwhile, rapid information obsolescence has become a commonplace in today’s high-velocity environment. Managers therefore need to make process design decisions based on incomplete information regarding the future market. This thesis studies the above problems in the evaluation and design of complex production systems. Based on the widely used Data Envelopment Analysis models, we develop a generalized methodology to evaluate the dynamic efficiency of production networks. Our method evaluates both the supply network and its constituent firms in a systematic way. The evaluation result can help identify inefficiency in the network, which is important information for improving the network performance. Part II of the thesis covers multi-criteria process design methods developed for situations of different information availability. Our design approaches combine interdisciplinary techniques to facilitate efficient decision-making in situations with limited information and high uncertainty. As an illustration, we apply these approaches to project selection and resource allocation problems in a supply chain

A marketing mix model developed from single source data : a semiparametric approach

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 1991.Includes bibliographical references (leaves 215-222).by Makoto Abe.Ph.D