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

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

"To Sponsor or not to Sponsor: Sponsored Search Auctions with Organic Links"

In 2010 sponsored search advertisements generated over $12 billion in revenue for search engines in the US market and accounted for 46% of online advertising revenue. A substantial portion of this revenue was generated by the sale of search keywords using auction mechanism. We analyze a game-theoretic model to understand the interplay between organic and sponsored links in keyword auctions. Our model allows both the relevance of the advertising firm as well as the position of its sponsored link to impact click-through-rates. Our results demonstrate how the presence of organic links (links generated by the search engine algorithm) may lead to either more or less aggressive bidding for sponsored link positions depending on consumers attitudes toward sponsored links and the extent to which sponsored and organic links are complements or substitutes. In contrast to equilibrium results in existing literature, the firm with the highest value per click does not necessarily win the first spot in the sponsored search listing. It also may be optimal for a firm to bid an amount greater than the expected value (or sale) from a click.sponsored search, organic search, online advertising, keyword auction

To Sponsor or Not to Sponsor: Sponsored Search Auctions with Organic Links

In 2010 sponsored search advertisements generated over $12 billion in revenue for search engines in the US market and accounted for 46% of online advertising revenue. A substantial portion of this revenue was generated by the sale of search keywords using an auction mechanism. We analyze a game-theoretic model to understand the interplay between organic and sponsored links in keyword auctions. Our model allows both the relevance of the advertising firm as well as the position of its sponsored link to impact click-through-rates. Our results demonstrate how the presence of organic links (links generated by the search engine algorithm) may lead to either more or less aggressive bidding for sponsored link positions depending on consumer attitudes toward sponsored links and the extent to which sponsored and organic links are complements or substitutes. In contrast to equilibrium results in existing literature, the …rm with the highest value per click does not necessarily win the first spot in the sponsored search listings. It also may be optimal for a firm to bid an amount greater than the expected value (or sale) from a click.

Machine Learning to Predict Advertisement Targeting Solutions

Generally, the present disclosure is directed to using machine learning to predict advertisement targeting solutions. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict optimal advertisement target solutions such as, for example, keyword word sets, negative word sets, location restrictions, bid adjustments, and/or schedules based on product data such as, for example, advertisement content (e.g., ad creatives text), seed keywords, images of the product, and/or advertiser metadata

Differential Effects of Keyword Selection in Search Engine Advertising on Direct and Indirect Sales

Product sales via sponsored keyword advertising on search engines rely on an effective selection of keywords that describe the offerings. In this study, we consider both the direct sales of the advertised products and indirect sales (i.e., cross-selling) of other products, and examine how specific keywords and general keywords influence these two types of sales differently. We also examine how the cross-selling effects may vary across different types of products (main products and accessories). Our results suggest that the use of specific keywords leans toward improving the direct sales of advertised products, while the use of general keywords leans toward improving the indirect sales of other products. The contribution of keywords to indirect sales is influenced by product type. For main products, the use of specific keywords generates a higher marginal contribution to indirect sales than that of general keywords. For accessory products, the use of general keywords generates a higher marginal contribution to indirect sales than that of specific keywords. The key implication of this study is that sellers focusing on different types of sales (direct or indirect sales) or products (main or accessory products) should consider using different types of keywords in search engine advertising to drive sales

The Endogenous Market Structures Approach. A Non-technical Survey with Applications to the Crisis and Future Scenarios for the New Economy

The EMSs approach to macroeconomics introduces strategic interactions and endogenous entry decisions in the analysis of aggregate phenomena as business cycle, international trade and growth. This survey provides a non-technical discussion of the applications of the EMSs approach to positive and normative issues, and relates these with recent debates on the current recession, future scenarios for glabalization, policymaking and the New Economy.

Auctions Versus Negotiations in Procurement: An Empirical Analysis

Should the buyer of a customized good use competitive bidding or negotiation to select a contractor? To shed light on this question, we offer a framework that compares auctions with negotiations. We then examine a comprehensive data set of private sector building contracts awarded in Northern California during the years 1995-2000. The analysis suggests a number of potential limitations to the use of auctions. Auctions perform poorly when projects are complex, contractual design is incomplete and there are few available bidders. Furthermore, auctions stifle communication between buyers and the sellers, preventing the buyer from utilizing the contractor's expertise when designing the project. Some implications of these results for procurement in the public sector are discussed.

Operating an Advertising Programmatic Buying Platform: A Case Study

This paper analyses how new technological developments and the possibilities generated by the internet are shaping the online advertising market. More specifically it focuses on a programmatic advertising case study. The origin of the problem is how publishers resort to automated buying and selling when trying to shift unsold inventory. To carry out our case study, we will use a programmatic online advertising sales platform, which identifies the optimal way of promoting a given product. The platform executes, evaluates, manages and optimizes display advertising campaigns, all in real-time. The empirical analysis carried out in the case study reveals that the platform and its exclusion algorithms are suitable mechanisms for analysing the performance and efficiency of the various segments that might be used to promote products. Thanks to Big Data tools and artificial intelligence the platform performs automatically, providing information in a user-friendly and simple manner