Keyword Segmentation, Campaign Organization, and Budget Allocation in Sponsored Search Advertising

Sponsored search advertising, where search providers allow advertisers to participate in a real-time auction and compete for ad slots on search engine results pages (SERPs), is currently one of the most popular advertising channels by marketers. Some domains such as Amazon.com allocate in millions of dollars a month to their sponsored search campaigns. Considering the amount of money allocated to sponsored search as well as the dynamic nature of keyword advertising process, the campaign budget planning decision is a non-trivial task for advertisers. Budget constrained advertisers must consider a number of factors when deciding how to organize campaigns, how much budget to allocate to them, and which keywords to bid on. Specifically, they must decide how to spend budget across planning horizons, markets, campaigns, and ad groups. In this thesis, I develop a simulation model that integrates the issues of keyword segmentation, campaign organization, and budget allocation in order to characterize different budget allocation strategies and understand their implications on search advertising performance. Using the buying funnel model as the basis of keyword segmentation and campaign organization, I examine several budget allocation strategies (i.e., search Volume-based, Cost-based, and Clicks-based) and evaluate their performance implications for firms that may pursue different marketing objectives based on industry and or product/service offerings. I evaluate the simulation model using four fortune 500 companies as cases and their keyword advertising data obtained from Spyfu.com. The results and statistical analysis shows significant improvements in budget utilization using the above-mentioned allocation strategies over a Baseline strategy commonly used in practice. The study offers a unique insight into the budget allocation problem in sponsored search advertising by leveraging a theoretical framework for keyword segmentation, campaign management, and performance evaluation. It also provides insights for advertiser on operational issues such as keyword categorization and campaign organization and prioritization for improved performance. The proposed simulation model also contributes a valid experimental environment to test further decision scenarios, theoretical frameworks, and campaign allocation strategies in sponsored search advertising

A Free Exchange e-Marketplace for Digital Services

The digital era is witnessing a remarkable evolution of digital services. While the prospects are countless, the e-marketplaces of digital services are encountering inherent game-theoretic and computational challenges that restrict the rational choices of bidders. Our work examines the limited bidding scope and the inefficiencies of present exchange e-marketplaces. To meet challenges, a free exchange e-marketplace is proposed that follows the free market economy. The free exchange model includes a new bidding language and a double auction mechanism. The rule-based bidding language enables the flexible expression of preferences and strategic conduct. The bidding message holds the attribute-valuations and bidding rules of the selected services. The free exchange deliberates on attributes and logical bidding rules for automatic deduction and formation of elicited services and bids that result in a more rapid self-managed multiple exchange trades. The double auction uses forward and reverse generalized second price auctions for the symmetric matching of multiple digital services of identical attributes and different quality levels. The proposed double auction uses tractable heuristics that secure exchange profitability, improve truthful bidding and deliver stable social efficiency. While the strongest properties of symmetric exchanges are unfeasible game-theoretically, the free exchange converges rapidly to the social efficiency, Nash truthful stability, and weak budget balance by multiple quality-levels cross-matching, constant learning and informs at repetitive thick trades. The empirical findings validate the soundness and viability of the free exchange

Algorithms for Online Advertising Portfolio Optimization and Capacitated Mobile Facility Location

In this dissertation, we apply large-scale optimization techniques including column generation and heuristic approaches to problems in the domains of online advertising and mobile facility location. First, we study the online advertising portfolio optimization problem (OAPOP) of an advertiser. In the OAPOP, the advertiser has a set of targeting items of interest (in the order of tens of millions for large enterprises) and a daily budget. The objective is to determine how much to bid on each targeting item to maximize the return on investment. We show the OAPOP can be represented by the Multiple Choice Knapsack Problem (MCKP). We propose an efficient column generation (CG) algorithm for the linear programming relaxation of the problem. The computations demonstrate that our CG algorithm significantly outperforms the state-of-the-art linear time algorithm used to solve the MCKP relaxation for the OAPOP. Second, we study the problem faced by the advertiser in online advertising in the presence of bid adjustments. In addition to bids, the advertisers are able to submit bid adjustments for ad query features such as geographical location, time of day, device, and audience. We introduce the Bid Adjustments Problem in Online Advertising (BAPOA) where an advertiser determines base bids and bid adjustments to maximize the return on investment. We develop an efficient algorithm to solve the BAPOA. We perform computational experiments and demonstrate, in the presence of high revenue-per-click variation across features, the revenue benefit of using bid adjustments can exceed 20%. Third, we study the capacitated mobile facility location problem (CMFLP), which is a generalization of the well-known capacitated facility location problem that has applications in supply chain and humanitarian logistics. We provide two integer programming formulations for the CMFLP. The first is on a layered graph, while the second is a set partitioning formulation. We develop a branch-and-price algorithm on the set partitioning formulation. We find that the branch-and-price procedure is particularly effective, when the ratio of the number of clients to the number of facilities is small and the facility capacities are tight. We also develop a local search heuristic and a rounding heuristic for the CMFLP

Convex Optimization, Stochastic Approximation, and Optimal Contract Management in Real-time Bidding

This thesis studies problems at the intersection of monotone and convex optimization, auction theory, and electronic commerce. Convex optimization and the theory of stochastic approximation serve as the basic practical and theoretical tools we have drawn upon. We solve important problems facing Demand Side Platforms (DSPs) and other demand aggregators (to be defined in the main body) in the e-commerce space, particularly in the field of real-time bidding (RTB). RTB is a real-time auction market, the primary application of which is the selling advertising space. Our main contribution to this field, at its most basic, is to recognize that certain optimal bidding problems can be re-cast as convex optimization problems. Particular focus will be placed upon the second price auction mechanism due to the strikingly simple structural results that hold in this case; but many results generalize to the first price auction mechanism under additional assumptions. We will also touch upon formal connections between these auction problems and two important problems in finance, namely the dark pool problem, and optimal portfolio construction

Mitigating airport congestion : market mechanisms and airline response models

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (leaves 157-165).Efficient allocation of scarce resources in networks is an important problem worldwide. In this thesis, we focus on resource allocation problems in a network of congested airports. The increasing demand for access to the world's major commercial airports combined with the limited operational capacity at many of these airports have led to growing air traffic congestion resulting in several billion dollars of delay cost every year. In this thesis, we study two demand-management techniques -- strategic and operational approaches -- to mitigate airport congestion. As a strategic initiative, auctions have been proposed to allocate runway slot capacity. We focus on two elements in the design of such slot auctions -- airline valuations and activity rules. An aspect of airport slot market environments, which we argue must be considered in auction design, is the fact that the participating airlines are budget-constrained. -- The problem of finding the best bundle of slots on which to bid in an iterative combinatorial auction, also called the preference elicitation problem, is a particularly hard problem, even more in the case of airlines in a slot auction. We propose a valuation model, called the Aggregated Integrated Airline Scheduling and Fleet Assignment Model, to help airlines understand the true value of the different bundles of slots in the auction. This model is efficient and was found to be robust to data uncertainty in our experimental simulations.(cont.) -- Activity rules are checks made by the auctioneer at the end of every round to suppress strategic behavior by bidders and to promote consistent, continual preference elicitation. These rules find applications in several real world scenarios including slot auctions. We show that the commonly used activity rules are not applicable for slot auctions as they prevent straightforward behavior by budget-constrained bidders. We propose the notion of a strong activity rule which characterizes straightforward bidding strategies. We then show how a strong activity rule in the context of budget-constrained bidders (and quasilinear bidders) can be expressed as a linear feasibility problem. This work on activity rules also applies to more general iterative combinatorial auctions.We also study operational (real-time) demand-management initiatives that are used when there are sudden drops in capacity at airports due to various uncertainties, such as bad-weather. We propose a system design that integrates the capacity allocation, airline recovery and inter-airline slot exchange procedures, and suggest metrics to evaluate the different approaches to fair allocations.by Pavithra Harsha.Ph.D

A review of the housing market-clearing process in integrated land-use and transport models

The land-use/transport interaction (LUTI) modeling framework has become the current state of best practice for analyzing the interdependency between the land-use and transportation systems. This paper presents a comprehensive review of the housing market-clearing mechanisms used in operational LUTI models. Market clearing is a critical component of modeling housing markets, but a systematic review and critique of the current state of the art have not previously been undertaken. In the review paper, the theoretical foundations for modeling household location choice are reviewed, including bid-rent and random utility theories. Five LUTI models are discussed in detail: two equilibrium models, MUSSA and RELU-TRAN, and three dynamic disequilibrium models, UrbanSim, ILUTE, and SimMobility. The discussion focuses on the following key points: the assumptions embedded in the models, the aggregation level of households and locations, computational cost and operationalization of the models. One of the challenges is that there are rarely any empirical studies that compare the performance of equilibrium and dynamic models in the same study context. Future research is recommended to empirically investigate the pros and cons of the two modeling approaches and compare the model performances for their representativeness of real-world behavior, computational efficiencies, and abilities for policy analysis. More sophisticated studies about the impacts of agents’ behavior on the housing market-clearing process are also recommended

The Economics of Risk

This collection offers an economics-based overview of the various facets of risk. It contains six papers that examine a broad array of research relating to risk. Two papers examine risk management and its application to decision making as well as what researchers have learned over the past few decades in their theoretical investigations of risk. The remaining chapters examine how risk plays out in the particular markets in which it has a significant presence, including casino gambling enterprises, agricultural markets, auctions, and health insurance.https://research.upjohn.org/up_press/1176/thumbnail.jp

Essays on Labor Economics and Advertising Auctions.

This dissertation contains three essays. The first is about an experiment with an advertising auction to determine its effects on a company and its main competitor. The second is about the role of preferences and skill in determining whether lawyers choose to work in the nonprofit or private sectors and their pay. The third is about the impact of immigrant computer scientists on the labor market. The first essay describes the design and results of an experiment in which one company temporarily suspended its search advertising campaign in randomized locations in the U.S. The experiment demonstrated that the company gained less new business from its ads than naive non-experimental methods predicted. Using data from the company's closest competitor, the experiment revealed that spillover effects on the competitor's business and marketing campaigns were small overall, and unexpectedly, on searches for the company's name. The second essay uses data from two different surveys of lawyers to document facts about their pay, in particular, pay differences between the nonprofit and private sectors. Private sector lawyers make higher wages, especially those who graduated from top tier law schools, whereas pay in the nonprofit sector is lower and flat across law school tiers. A wage equation model estimated using this survey data suggests that nonprofit lawyers would earn more in the private sector and thus pay an opportunity cost to do nonprofit work. The third essay develops and calibrates a dynamic structural model of the impact of high-skilled immigration on the labor market for computer scientists (CS) in the U.S. during the dot-com boom and bust. Workers choose whether to study and work in the CS field based on wages, preferences and expectations about the future. Employers choose how many domestic and foreign workers to hire considering their productivity and hiring costs. Counterfactual simulations suggest that American CS employment and wages would have been modestly highest in 2004 if firms could not hire more foreigners than they could in 1994. However, total CS employment would have been 3.8% - 9.0% lower.PhDEconomicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110421/1/goldenjm_1.pd