Keyword Search Patterns in Sponsored Link Advertisements

Over time, an online user searching for information about an idea or product may enter multiple search engine queries, thus creating a keyword search pattern from which the user’s intent may be inferable. Our research seeks to establish the relationship between these patterns and user actions, specifically their purchase behavior. To test our hypotheses, we examine a unique dataset from a large Asian travel agency; the dataset includes search engine and on-site behavior from over a million users during a one year span. We have developed a typology for the coding of search queries used and determining the level of specificity and breadth as well as content type for each of well over two million unique searches. Once coded, our analysis will allow us to identify types of patterns and test our hypotheses, thus providing important findings regarding the relationship between search patterns and behavior

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

"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.

A Novel Method to Calculate Click Through Rate for Sponsored Search

Sponsored search adopts generalized second price (GSP) auction mechanism which works on the concept of pay per click which is most commonly used for the allocation of slots in the searched page. Two main aspects associated with GSP are the bidding amount and the click through rate (CTR). The CTR learning algorithms currently being used works on the basic principle of (#clicks_i/ #impressions_i) under a fixed window of clicks or impressions or time. CTR are prone to fraudulent clicks, resulting in sudden increase of CTR. The current algorithms are unable to find the solutions to stop this, although with the use of machine learning algorithms it can be detected that fraudulent clicks are being generated. In our paper, we have used the concept of relative ranking which works on the basic principle of (#clicks_i /#clicks_t). In this algorithm, both the numerator and the denominator are linked. As #clicks_t is higher than previous algorithms and is linked to the #clicks_i, the small change in the clicks which occurs in the normal scenario have a very small change in the result but in case of fraudulent clicks the number of clicks increases or decreases rapidly which will add up with the normal clicks to increase the denominator, thereby decreasing the CTR.Comment: 10 pages, 1 figur

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

An Empirical Analysis of Search Engine Advertising: Sponsored Search and Cross-Selling in Electronic Markets

The phenomenon of sponsored search advertising – where advertisers pay a fee to Internet search engines to be displayed alongside organic (non-sponsored) web search results – is gaining ground as the largest source of revenues for search engines. Using a unique panel dataset of several hundred keywords collected from a large nationwide retailer that advertises on Google, we empirically model the relationship between different metrics such as click-through rates, conversion rates, bid prices and keyword ranks. Our paper proposes a novel framework and data to better understand what drives these differences. We use a Hierarchical Bayesian modeling framework and estimate the model using Markov Chain Monte Carlo (MCMC) methods. We empirically estimate the impact of keyword attributes on consumer search and purchase behavior as well as on firms’ decision-making behavior on bid prices and ranks. We find that the presence of retailer-specific information in the keyword increases click-through rates, and the presence of brand-specific information in the keyword increases conversion rates. Our analysis provides some evidence that advertisers are not bidding optimally with respect to maximizing the profits. We also demonstrate that as suggested by anecdotal evidence, search engines like Google factor in both the auction bid price as well as prior click-through rates before allotting a final rank to an advertisement. Finally, we conduct a detailed analysis with product level variables to explore the extent of cross-selling opportunities across different categories from a given keyword advertisement. We find that there exists significant potential for cross-selling through search keyword advertisements. Latency (the time it takes for consumer to place a purchase order after clicking on the advertisement) and the presence of a brand name in the keyword are associated with consumer spending on product categories that are different from the one they were originally searching for on the Internet

Sponsored Search: Do Organic Results help or hurt the Performance and under what conditions?

We study the relative impact of competing links in organic and sponsored search results on the performance of sponsored search ads. We use data generated through a field experiment for several keywords from the ad campaign of an online retailer. Using a hierarchical Bayesian model, we measure the impact of competition on both click-through rate and conversion rate of sponsored search ads for these keywords. We find that the competitor links in organic results have a higher impact on the performance as compared to the competitor links in sponsored results. We also find that competition has a greater influence on the conversion performance as compared to the click through performance. Our results inform advertisers on the impact of organic results on their performance. Our results reveal inefficiency in the current auction mechanism as the click performance may not reveal the true relative quality of advertisers