Google Case : Capital Structure

Topics: Data mining, Advertising, Pay per click Pages: 27 (10157 words) Published: July 7, 2010
Mining Advertiser-specific User Behavior Using Adfactors
Nikolay Archak
New York University, Leonard N. Stern School of Business 44 West 4th Street, Suite 8-185 New York, NY, 10012

Vahab S. Mirrokni
Google Research 76 9th Ave New York, NY 10011

S. Muthukrishnan
Google Research 76 9th Ave New York, NY 10011

Consider an online ad campaign run by an advertiser. The ad serving companies that handle such campaigns record users’ behavior that leads to impressions of campaign ads, as well as users’ responses to such impressions. This is summarized and reported to the advertisers to help them evaluate the performance of their campaigns and make better budget allocation decisions. The most popular reporting statistics are the click-through rate and the conversion rate. While these are indicative of the effectiveness of an ad campaign, the advertisers often seek to understand more sophisticated long-term effects of their ads on the brand awareness and the user behavior that leads to the conversion, thus creating a need for the reporting measures that can capture both the duration and the frequency of the pathways to user conversions. In this paper, we propose an alternative data mining framework for analyzing user-level advertising data. In the aggregation step, we compress individual user histories into a graph structure, called the adgraph, representing local correlations between ad events. For the reporting step, we introduce several scoring rules, called the adfactors (AF), that can capture global role of ads and ad paths in the adgraph, in particular, the structural correlation between an ad impression and the user conversion. We present scalable local algorithms for computing the adfactors; all algorithms were implemented using the MapReduce programming model and the Pregel framework. Using an anonymous user-level dataset of sponsored search campaigns for eight different advertisers, we evaluate our framework with different adgraphs and adfactors in terms of their statistical fit to the data, and show its value for mining the long-term behavioral patterns in the advertising data.

sponsored search, ad auctions, online advertising, PageRank, user behavior models, clickthrough rate, conversion rate

The Internet has become a major advertising medium. Although a number of different factors contributed to this, what distinguishes the Internet advertising from the offline advertising competitors is its inherently interactive nature. Measuring effectiveness of a particular advertising campaign and allocating the advertising budget optimally was and still remains a very challenging task, yet the Internet made the task easier by connecting ad impressions 1 to tangible user actions and artifacts such as posing a search query, clicking on an ad or converting 2 . The simplicity of measuring and attributing user clicks has established the clickthrough rate (CTR) 3 as the current de-facto standard of ad quality for sponsored search. It is now customary to define the advertiser’s optimization problem as maximization of the expected number of ad clicks given a certain budget constraint [13, 23, 10]. The conversion rate (CR), defined similarly as the probability of the user conversion, is another popular ad effectiveness measure; together with the CTR it is frequently used by the advertisers to measure the return on investment of specific keywords in the advertising campaign. Recent empirical studies show that the effects of online ads cannot be fully captured by the CTR or the CR. In particular, the sponsored search advertising, as well as the display advertising, can have a significant number of indirect effects such as building the brand awareness [18]. For instance, Lewis and Reiley [20], in cooperation between Yahoo! and a major retailer, performed a randomized controlled experiment to measure the effect of the online advertising on...
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