Google Case : Capital Structure
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
mirrokni@google.com
muthu@google.com
narchak@stern.nyu.edu
ABSTRACT
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,
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
mirrokni@google.com
muthu@google.com
narchak@stern.nyu.edu
ABSTRACT
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,